R version 2.15.1 Patched (2012-10-08 r60901) -- "Roasted Marshmallows" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-unknown-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "stats" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('stats') > > assign(".oldSearch", search(), pos = 'CheckExEnv') > cleanEx() > nameEx("AIC") > ### * AIC > > flush(stderr()); flush(stdout()) > > ### Name: AIC > ### Title: Akaike's An Information Criterion > ### Aliases: AIC BIC > ### Keywords: models > > ### ** Examples > > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 > stopifnot(all.equal(AIC(lm1), + AIC(logLik(lm1)))) > BIC(lm1) [1] 339.0226 > > lm2 <- update(lm1, . ~ . -Examination) > AIC(lm1, lm2) df AIC lm1 7 326.0716 lm2 6 325.2408 > BIC(lm1, lm2) df BIC lm1 7 339.0226 lm2 6 336.3417 > > > > cleanEx() > nameEx("ARMAacf") > ### * ARMAacf > > flush(stderr()); flush(stdout()) > > ### Name: ARMAacf > ### Title: Compute Theoretical ACF for an ARMA Process > ### Aliases: ARMAacf > ### Keywords: ts > > ### ** Examples > > ARMAacf(c(1.0, -0.25), 1.0, lag.max = 10) 0 1 2 3 4 5 1.000000000 0.875000000 0.625000000 0.406250000 0.250000000 0.148437500 6 7 8 9 10 0.085937500 0.048828125 0.027343750 0.015136719 0.008300781 > > ## Example from Brockwell & Davis (1991, pp.92-4) > ## answer 2^(-n) * (32/3 + 8 * n) /(32/3) > n <- 1:10; 2^(-n) * (32/3 + 8 * n) /(32/3) [1] 0.875000000 0.625000000 0.406250000 0.250000000 0.148437500 0.085937500 [7] 0.048828125 0.027343750 0.015136719 0.008300781 > ARMAacf(c(1.0, -0.25), 1.0, lag.max = 10, pacf = TRUE) [1] 0.8750000 -0.6000000 0.3750000 -0.2727273 0.2142857 -0.1764706 [7] 0.1500000 -0.1304348 0.1153846 -0.1034483 > zapsmall(ARMAacf(c(1.0, -0.25), lag.max = 10, pacf = TRUE)) [1] 0.80 -0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 > > ## Cov-Matrix of length-7 sub-sample of AR(1) example: > toeplitz(ARMAacf(0.8, lag.max = 7)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1.0000000 0.800000 0.64000 0.5120 0.4096 0.32768 0.262144 0.2097152 [2,] 0.8000000 1.000000 0.80000 0.6400 0.5120 0.40960 0.327680 0.2621440 [3,] 0.6400000 0.800000 1.00000 0.8000 0.6400 0.51200 0.409600 0.3276800 [4,] 0.5120000 0.640000 0.80000 1.0000 0.8000 0.64000 0.512000 0.4096000 [5,] 0.4096000 0.512000 0.64000 0.8000 1.0000 0.80000 0.640000 0.5120000 [6,] 0.3276800 0.409600 0.51200 0.6400 0.8000 1.00000 0.800000 0.6400000 [7,] 0.2621440 0.327680 0.40960 0.5120 0.6400 0.80000 1.000000 0.8000000 [8,] 0.2097152 0.262144 0.32768 0.4096 0.5120 0.64000 0.800000 1.0000000 > > > > cleanEx() > nameEx("ARMAtoMA") > ### * ARMAtoMA > > flush(stderr()); flush(stdout()) > > ### Name: ARMAtoMA > ### Title: Convert ARMA Process to Infinite MA Process > ### Aliases: ARMAtoMA > ### Keywords: ts > > ### ** Examples > > ARMAtoMA(c(1.0, -0.25), 1.0, 10) [1] 2.00000000 1.75000000 1.25000000 0.81250000 0.50000000 0.29687500 [7] 0.17187500 0.09765625 0.05468750 0.03027344 > ## Example from Brockwell & Davis (1991, p.92) > ## answer (1 + 3*n)*2^(-n) > n <- 1:10; (1 + 3*n)*2^(-n) [1] 2.00000000 1.75000000 1.25000000 0.81250000 0.50000000 0.29687500 [7] 0.17187500 0.09765625 0.05468750 0.03027344 > > > > cleanEx() > nameEx("Beta") > ### * Beta > > flush(stderr()); flush(stdout()) > > ### Name: Beta > ### Title: The Beta Distribution > ### Aliases: Beta dbeta pbeta qbeta rbeta > ### Keywords: distribution > > ### ** Examples > > x <- seq(0, 1, length=21) > dbeta(x, 1, 1) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 > pbeta(x, 1, 1) [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00 > > > > cleanEx() > nameEx("Binomial") > ### * Binomial > > flush(stderr()); flush(stdout()) > > ### Name: Binomial > ### Title: The Binomial Distribution > ### Aliases: Binomial dbinom pbinom qbinom rbinom > ### Keywords: distribution > > ### ** Examples > > require(graphics) > # Compute P(45 < X < 55) for X Binomial(100,0.5) > sum(dbinom(46:54, 100, 0.5)) [1] 0.6317984 > > ## Using "log = TRUE" for an extended range : > n <- 2000 > k <- seq(0, n, by = 20) > plot (k, dbinom(k, n, pi/10, log=TRUE), type='l', ylab="log density", + main = "dbinom(*, log=TRUE) is better than log(dbinom(*))") > lines(k, log(dbinom(k, n, pi/10)), col='red', lwd=2) > ## extreme points are omitted since dbinom gives 0. > mtext("dbinom(k, log=TRUE)", adj=0) > mtext("extended range", adj=0, line = -1, font=4) > mtext("log(dbinom(k))", col="red", adj=1) > > > > cleanEx() > nameEx("Cauchy") > ### * Cauchy > > flush(stderr()); flush(stdout()) > > ### Name: Cauchy > ### Title: The Cauchy Distribution > ### Aliases: Cauchy dcauchy pcauchy qcauchy rcauchy > ### Keywords: distribution > > ### ** Examples > > dcauchy(-1:4) [1] 0.15915494 0.31830989 0.15915494 0.06366198 0.03183099 0.01872411 > > > > cleanEx() > nameEx("Chisquare") > ### * Chisquare > > flush(stderr()); flush(stdout()) > > ### Name: Chisquare > ### Title: The (non-central) Chi-Squared Distribution > ### Aliases: Chisquare dchisq pchisq qchisq rchisq > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > dchisq(1, df=1:3) [1] 0.2419707 0.3032653 0.2419707 > pchisq(1, df= 3) [1] 0.198748 > pchisq(1, df= 3, ncp = 0:4)# includes the above [1] 0.19874804 0.13229855 0.08787311 0.05824691 0.03853592 > > x <- 1:10 > ## Chi-squared(df = 2) is a special exponential distribution > all.equal(dchisq(x, df=2), dexp(x, 1/2)) [1] TRUE > all.equal(pchisq(x, df=2), pexp(x, 1/2)) [1] TRUE > > ## non-central RNG -- df=0 with ncp > 0: Z0 has point mass at 0! > Z0 <- rchisq(100, df = 0, ncp = 2.) > graphics::stem(Z0) The decimal point is at the | 0 | 0000000000000000000000000000000000000013356778899 1 | 0001333456678888899 2 | 0011444467 3 | 00233345888 4 | 111246 5 | 6 | 7 | 178 8 | 23 > > ## Not run: > ##D ## visual testing > ##D ## do P-P plots for 1000 points at various degrees of freedom > ##D L <- 1.2; n <- 1000; pp <- ppoints(n) > ##D op <- par(mfrow = c(3,3), mar= c(3,3,1,1)+.1, mgp= c(1.5,.6,0), > ##D oma = c(0,0,3,0)) > ##D for(df in 2^(4*rnorm(9))) { > ##D plot(pp, sort(pchisq(rr <- rchisq(n,df=df, ncp=L), df=df, ncp=L)), > ##D ylab="pchisq(rchisq(.),.)", pch=".") > ##D mtext(paste("df = ",formatC(df, digits = 4)), line= -2, adj=0.05) > ##D abline(0,1,col=2) > ##D } > ##D mtext(expression("P-P plots : Noncentral "* > ##D chi^2 *"(n=1000, df=X, ncp= 1.2)"), > ##D cex = 1.5, font = 2, outer=TRUE) > ##D par(op) > ## End(Not run) > > ## "analytical" test > lam <- seq(0,100, by=.25) > p00 <- pchisq(0, df=0, ncp=lam) > p.0 <- pchisq(1e-300, df=0, ncp=lam) > stopifnot(all.equal(p00, exp(-lam/2)), + all.equal(p.0, exp(-lam/2))) > > > > cleanEx() > nameEx("Exponential") > ### * Exponential > > flush(stderr()); flush(stdout()) > > ### Name: Exponential > ### Title: The Exponential Distribution > ### Aliases: Exponential dexp pexp qexp rexp > ### Keywords: distribution > > ### ** Examples > > dexp(1) - exp(-1) #-> 0 [1] 0 > > > > cleanEx() > nameEx("Fdist") > ### * Fdist > > flush(stderr()); flush(stdout()) > > ### Name: FDist > ### Title: The F Distribution > ### Aliases: FDist df pf qf rf > ### Keywords: distribution > > ### ** Examples > > ## the density of the square of a t_m is 2*dt(x, m)/(2*x) > # check this is the same as the density of F_{1,m} > x <- seq(0.001, 5, len=100) > all.equal(df(x^2, 1, 5), dt(x, 5)/x) [1] TRUE > > ## Identity: qf(2*p - 1, 1, df)) == qt(p, df)^2) for p >= 1/2 > p <- seq(1/2, .99, length=50); df <- 10 > rel.err <- function(x,y) ifelse(x==y,0, abs(x-y)/mean(abs(c(x,y)))) > > > > cleanEx() > nameEx("GammaDist") > ### * GammaDist > > flush(stderr()); flush(stdout()) > > ### Name: GammaDist > ### Title: The Gamma Distribution > ### Aliases: GammaDist dgamma pgamma qgamma rgamma > ### Keywords: distribution > > ### ** Examples > > -log(dgamma(1:4, shape=1)) [1] 1 2 3 4 > p <- (1:9)/10 > pgamma(qgamma(p,shape=2), shape=2) [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 > 1 - 1/exp(qgamma(p, shape=1)) [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 > > > > cleanEx() > nameEx("Geometric") > ### * Geometric > > flush(stderr()); flush(stdout()) > > ### Name: Geometric > ### Title: The Geometric Distribution > ### Aliases: Geometric dgeom pgeom qgeom rgeom > ### Keywords: distribution > > ### ** Examples > > qgeom((1:9)/10, prob = .2) [1] 0 0 1 2 3 4 5 7 10 > Ni <- rgeom(20, prob = 1/4); table(factor(Ni, 0:max(Ni))) 0 1 2 3 4 5 6 7 8 9 10 11 5 3 3 1 2 2 0 1 0 1 1 1 > > > > cleanEx() > nameEx("HoltWinters") > ### * HoltWinters > > flush(stderr()); flush(stdout()) > > ### Name: HoltWinters > ### Title: Holt-Winters Filtering > ### Aliases: HoltWinters print.HoltWinters residuals.HoltWinters > ### Keywords: ts > > ### ** Examples > > ## Don't show: > od <- options(digits=5) > ## End Don't show > require(graphics) > > ## Seasonal Holt-Winters > (m <- HoltWinters(co2)) Holt-Winters exponential smoothing with trend and additive seasonal component. Call: HoltWinters(x = co2) Smoothing parameters: alpha: 0.51265 beta : 0.0094977 gamma: 0.47289 Coefficients: [,1] a 364.76162 b 0.12474 s1 0.22153 s2 0.95528 s3 1.59847 s4 2.87580 s5 3.28201 s6 2.44070 s7 0.89694 s8 -1.37964 s9 -3.41124 s10 -3.25702 s11 -1.91349 s12 -0.58442 > plot(m) > plot(fitted(m)) > > (m <- HoltWinters(AirPassengers, seasonal = "mult")) Holt-Winters exponential smoothing with trend and multiplicative seasonal component. Call: HoltWinters(x = AirPassengers, seasonal = "mult") Smoothing parameters: alpha: 0.27559 beta : 0.032693 gamma: 0.87073 Coefficients: [,1] a 469.32322 b 3.02154 s1 0.94646 s2 0.88292 s3 0.97174 s4 1.03048 s5 1.04769 s6 1.18053 s7 1.35908 s8 1.33317 s9 1.10834 s10 0.98688 s11 0.83613 s12 0.92099 > plot(m) > > ## Non-Seasonal Holt-Winters > x <- uspop + rnorm(uspop, sd = 5) > m <- HoltWinters(x, gamma = FALSE) > plot(m) > > ## Exponential Smoothing > m2 <- HoltWinters(x, gamma = FALSE, beta = FALSE) > lines(fitted(m2)[,1], col = 3) > ## Don't show: > options(od) > ## End Don't show > > > > cleanEx() > nameEx("Hypergeometric") > ### * Hypergeometric > > flush(stderr()); flush(stdout()) > > ### Name: Hypergeometric > ### Title: The Hypergeometric Distribution > ### Aliases: Hypergeometric dhyper phyper qhyper rhyper > ### Keywords: distribution > > ### ** Examples > > m <- 10; n <- 7; k <- 8 > x <- 0:(k+1) > rbind(phyper(x, m, n, k), dhyper(x, m, n, k)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0 0.0004113534 0.01336898 0.117030 0.4193747 0.7821884 0.9635952 [2,] 0 0.0004113534 0.01295763 0.103661 0.3023447 0.3628137 0.1814068 [,8] [,9] [,10] [1,] 0.99814891 1.00000000 1 [2,] 0.03455368 0.00185109 0 > all(phyper(x, m, n, k) == cumsum(dhyper(x, m, n, k)))# FALSE [1] FALSE > > > cleanEx() > nameEx("IQR") > ### * IQR > > flush(stderr()); flush(stdout()) > > ### Name: IQR > ### Title: The Interquartile Range > ### Aliases: IQR > ### Keywords: univar robust distribution > > ### ** Examples > > IQR(rivers) [1] 370 > > > > cleanEx() > nameEx("Logistic") > ### * Logistic > > flush(stderr()); flush(stdout()) > > ### Name: Logistic > ### Title: The Logistic Distribution > ### Aliases: Logistic dlogis plogis qlogis rlogis > ### Keywords: distribution > > ### ** Examples > > var(rlogis(4000, 0, scale = 5))# approximately (+/- 3) [1] 86.93007 > pi^2/3 * 5^2 [1] 82.2467 > > > > cleanEx() > nameEx("Lognormal") > ### * Lognormal > > flush(stderr()); flush(stdout()) > > ### Name: Lognormal > ### Title: The Log Normal Distribution > ### Aliases: Lognormal dlnorm plnorm qlnorm rlnorm > ### Keywords: distribution > > ### ** Examples > > dlnorm(1) == dnorm(0) [1] TRUE > > > > cleanEx() > nameEx("Multinom") > ### * Multinom > > flush(stderr()); flush(stdout()) > > ### Name: Multinom > ### Title: The Multinomial Distribution > ### Aliases: Multinomial rmultinom dmultinom > ### Keywords: distribution > > ### ** Examples > > rmultinom(10, size = 12, prob=c(0.1,0.2,0.8)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 0 1 0 3 1 0 1 2 2 1 [2,] 2 4 4 2 0 1 2 2 5 3 [3,] 10 7 8 7 11 11 9 8 5 8 > > pr <- c(1,3,6,10) # normalization not necessary for generation > rmultinom(10, 20, prob = pr) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 3 0 0 0 1 1 1 1 1 1 [2,] 1 2 3 3 2 4 3 4 4 4 [3,] 7 6 9 7 8 3 8 6 2 7 [4,] 9 12 8 10 9 12 8 9 13 8 > > ## all possible outcomes of Multinom(N = 3, K = 3) > X <- t(as.matrix(expand.grid(0:3, 0:3))); X <- X[, colSums(X) <= 3] > X <- rbind(X, 3:3 - colSums(X)); dimnames(X) <- list(letters[1:3], NULL) > X [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] a 0 1 2 3 0 1 2 0 1 0 b 0 0 0 0 1 1 1 2 2 3 c 3 2 1 0 2 1 0 1 0 0 > round(apply(X, 2, function(x) dmultinom(x, prob = c(1,2,5))), 3) [1] 0.244 0.146 0.029 0.002 0.293 0.117 0.012 0.117 0.023 0.016 > > > > cleanEx() > nameEx("NLSstAsymptotic") > ### * NLSstAsymptotic > > flush(stderr()); flush(stdout()) > > ### Name: NLSstAsymptotic > ### Title: Fit the Asymptotic Regression Model > ### Aliases: NLSstAsymptotic NLSstAsymptotic.sortedXyData > ### Keywords: manip > > ### ** Examples > > Lob.329 <- Loblolly[ Loblolly$Seed == "329", ] > print(NLSstAsymptotic(sortedXyData(expression(age), + expression(height), + Lob.329)), digits=3) b0 b1 lrc -8.25 102.38 -3.22 > > > > cleanEx() > nameEx("NLSstClosestX") > ### * NLSstClosestX > > flush(stderr()); flush(stdout()) > > ### Name: NLSstClosestX > ### Title: Inverse Interpolation > ### Aliases: NLSstClosestX NLSstClosestX.sortedXyData > ### Keywords: manip > > ### ** Examples > > DNase.2 <- DNase[ DNase$Run == "2", ] > DN.srt <- sortedXyData(expression(log(conc)), expression(density), DNase.2) > NLSstClosestX(DN.srt, 1.0) [1] 0.9795406 > > > > cleanEx() > nameEx("NLSstLfAsymptote") > ### * NLSstLfAsymptote > > flush(stderr()); flush(stdout()) > > ### Name: NLSstLfAsymptote > ### Title: Horizontal Asymptote on the Left Side > ### Aliases: NLSstLfAsymptote NLSstLfAsymptote.sortedXyData > ### Keywords: manip > > ### ** Examples > > DNase.2 <- DNase[ DNase$Run == "2", ] > DN.srt <- sortedXyData( expression(log(conc)), expression(density), DNase.2 ) > NLSstLfAsymptote( DN.srt ) [1] -0.1869375 > > > > cleanEx() > nameEx("NLSstRtAsymptote") > ### * NLSstRtAsymptote > > flush(stderr()); flush(stdout()) > > ### Name: NLSstRtAsymptote > ### Title: Horizontal Asymptote on the Right Side > ### Aliases: NLSstRtAsymptote NLSstRtAsymptote.sortedXyData > ### Keywords: manip > > ### ** Examples > > DNase.2 <- DNase[ DNase$Run == "2", ] > DN.srt <- sortedXyData( expression(log(conc)), expression(density), DNase.2 ) > NLSstRtAsymptote( DN.srt ) [1] 2.157437 > > > > cleanEx() > nameEx("NegBinomial") > ### * NegBinomial > > flush(stderr()); flush(stdout()) > > ### Name: NegBinomial > ### Title: The Negative Binomial Distribution > ### Aliases: NegBinomial dnbinom pnbinom qnbinom rnbinom > ### Keywords: distribution > > ### ** Examples > > require(graphics) > x <- 0:11 > dnbinom(x, size = 1, prob = 1/2) * 2^(1 + x) # == 1 [1] 1 1 1 1 1 1 1 1 1 1 1 1 > 126 / dnbinom(0:8, size = 2, prob = 1/2) #- theoretically integer [1] 504.0 504.0 672.0 1008.0 1612.8 2688.0 4608.0 8064.0 14336.0 > > > x <- 0:15 > size <- (1:20)/4 > persp(x,size, dnb <- outer(x, size, function(x,s) dnbinom(x,s, prob= 0.4)), + xlab = "x", ylab = "s", zlab="density", theta = 150) > title(tit <- "negative binomial density(x,s, pr = 0.4) vs. x & s") > > image (x,size, log10(dnb), main= paste("log [",tit,"]")) > contour(x,size, log10(dnb),add=TRUE) > > ## Alternative parametrization > x1 <- rnbinom(500, mu = 4, size = 1) > x2 <- rnbinom(500, mu = 4, size = 10) > x3 <- rnbinom(500, mu = 4, size = 100) > h1 <- hist(x1, breaks = 20, plot = FALSE) > h2 <- hist(x2, breaks = h1$breaks, plot = FALSE) > h3 <- hist(x3, breaks = h1$breaks, plot = FALSE) > barplot(rbind(h1$counts, h2$counts, h3$counts), + beside = TRUE, col = c("red","blue","cyan"), + names.arg = round(h1$breaks[-length(h1$breaks)])) > > > > cleanEx() > nameEx("Normal") > ### * Normal > > flush(stderr()); flush(stdout()) > > ### Name: Normal > ### Title: The Normal Distribution > ### Aliases: Normal dnorm pnorm qnorm rnorm > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > dnorm(0) == 1/ sqrt(2*pi) [1] TRUE > dnorm(1) == exp(-1/2)/ sqrt(2*pi) [1] TRUE > dnorm(1) == 1/ sqrt(2*pi*exp(1)) [1] TRUE > > ## Using "log = TRUE" for an extended range : > par(mfrow=c(2,1)) > plot(function(x) dnorm(x, log=TRUE), -60, 50, + main = "log { Normal density }") > curve(log(dnorm(x)), add=TRUE, col="red",lwd=2) > mtext("dnorm(x, log=TRUE)", adj=0) > mtext("log(dnorm(x))", col="red", adj=1) > > plot(function(x) pnorm(x, log.p=TRUE), -50, 10, + main = "log { Normal Cumulative }") > curve(log(pnorm(x)), add=TRUE, col="red",lwd=2) > mtext("pnorm(x, log=TRUE)", adj=0) > mtext("log(pnorm(x))", col="red", adj=1) > > ## if you want the so-called 'error function' > erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1 > ## (see Abramowitz and Stegun 29.2.29) > ## and the so-called 'complementary error function' > erfc <- function(x) 2 * pnorm(x * sqrt(2), lower = FALSE) > ## and the inverses > erfinv <- function (x) qnorm((1 + x)/2)/sqrt(2) > erfcinv <- function (x) qnorm(x/2, lower = FALSE)/sqrt(2) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("Poisson") > ### * Poisson > > flush(stderr()); flush(stdout()) > > ### Name: Poisson > ### Title: The Poisson Distribution > ### Aliases: Poisson dpois ppois qpois rpois > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > -log(dpois(0:7, lambda=1) * gamma(1+ 0:7)) # == 1 [1] 1 1 1 1 1 1 1 1 > Ni <- rpois(50, lambda = 4); table(factor(Ni, 0:max(Ni))) 0 1 2 3 4 5 6 7 8 9 10 1 2 7 9 8 13 5 4 0 0 1 > > 1 - ppois(10*(15:25), lambda=100) # becomes 0 (cancellation) [1] 1.233094e-06 1.261664e-08 7.085799e-11 2.252643e-13 4.440892e-16 [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 [11] 0.000000e+00 > ppois(10*(15:25), lambda=100, lower.tail=FALSE) # no cancellation [1] 1.233094e-06 1.261664e-08 7.085800e-11 2.253110e-13 4.174239e-16 [6] 4.626179e-19 3.142097e-22 1.337219e-25 3.639328e-29 6.453883e-33 [11] 7.587807e-37 > > par(mfrow = c(2, 1)) > x <- seq(-0.01, 5, 0.01) > plot(x, ppois(x, 1), type="s", ylab="F(x)", main="Poisson(1) CDF") > plot(x, pbinom(x, 100, 0.01),type="s", ylab="F(x)", + main="Binomial(100, 0.01) CDF") > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSD") > ### * SSD > > flush(stderr()); flush(stdout()) > > ### Name: SSD > ### Title: SSD Matrix and Estimated Variance Matrix in Multivariate Models > ### Aliases: SSD estVar > ### Keywords: models multivariate > > ### ** Examples > > # Lifted from Baron+Li: > # "Notes on the use of R for psychology experiments and questionnaires" > # Maxwell and Delaney, p. 497 > reacttime <- matrix(c( + 420, 420, 480, 480, 600, 780, + 420, 480, 480, 360, 480, 600, + 480, 480, 540, 660, 780, 780, + 420, 540, 540, 480, 780, 900, + 540, 660, 540, 480, 660, 720, + 360, 420, 360, 360, 480, 540, + 480, 480, 600, 540, 720, 840, + 480, 600, 660, 540, 720, 900, + 540, 600, 540, 480, 720, 780, + 480, 420, 540, 540, 660, 780), + ncol = 6, byrow = TRUE, + dimnames=list(subj=1:10, + cond=c("deg0NA", "deg4NA", "deg8NA", + "deg0NP", "deg4NP", "deg8NP"))) > > mlmfit <- lm(reacttime~1) > SSD(mlmfit) $SSD cond cond deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP deg0NA 29160 30600 26640 23760 32400 25560 deg4NA 30600 66600 32400 7200 36000 30600 deg8NA 26640 32400 56160 41040 57600 69840 deg0NP 23760 7200 41040 70560 72000 63360 deg4NP 32400 36000 57600 72000 108000 100800 deg8NP 25560 30600 69840 63360 100800 122760 $call lm(formula = reacttime ~ 1) $df [1] 9 attr(,"class") [1] "SSD" > estVar(mlmfit) cond cond deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP deg0NA 3240 3400 2960 2640 3600 2840 deg4NA 3400 7400 3600 800 4000 3400 deg8NA 2960 3600 6240 4560 6400 7760 deg0NP 2640 800 4560 7840 8000 7040 deg4NP 3600 4000 6400 8000 12000 11200 deg8NP 2840 3400 7760 7040 11200 13640 > > > > cleanEx() > nameEx("SSasymp") > ### * SSasymp > > flush(stderr()); flush(stdout()) > > ### Name: SSasymp > ### Title: Self-Starting Nls Asymptotic Regression Model > ### Aliases: SSasymp > ### Keywords: models > > ### ** Examples > > ## Don't show: > options(show.nls.convergence=FALSE) > ## End Don't show > Lob.329 <- Loblolly[ Loblolly$Seed == "329", ] > SSasymp( Lob.329$age, 100, -8.5, -3.2 ) # response only [1] 3.988924 11.505611 27.822517 41.130854 51.985354 60.838463 > Asym <- 100 > resp0 <- -8.5 > lrc <- -3.2 > SSasymp( Lob.329$age, Asym, resp0, lrc ) # response and gradient [1] 3.988924 11.505611 27.822517 41.130854 51.985354 60.838463 attr(,"gradient") Asym resp0 lrc [1,] 0.1151053 0.8848947 11.74087 [2,] 0.1843835 0.8156165 18.03613 [3,] 0.3347697 0.6652303 29.42113 [4,] 0.4574272 0.5425728 35.99454 [5,] 0.5574687 0.4425313 39.14366 [6,] 0.6390642 0.3609358 39.90776 > getInitial(height ~ SSasymp( age, Asym, resp0, lrc), data = Lob.329) $Asym [1] 94.1282 $resp0 [1] -8.250753 $lrc [1] -3.217578 > ## Initial values are in fact the converged values > fm1 <- nls(height ~ SSasymp( age, Asym, resp0, lrc), data = Lob.329) > summary(fm1) Formula: height ~ SSasymp(age, Asym, resp0, lrc) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 94.1282 8.4030 11.202 0.001525 ** resp0 -8.2508 1.2261 -6.729 0.006700 ** lrc -3.2176 0.1386 -23.218 0.000175 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7493 on 3 degrees of freedom > ## Don't show: > require(graphics) > > xx <- seq(0, 5, len = 101) > yy <- 5 - 4 * exp(-xx/(2*log(2))) > par(mar = c(0, 0, 4.1, 0)) > plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,6), xlim = c(-1, 5), + xlab = "", ylab = "", lwd = 2, + main = "Parameters in the SSasymp model") > usr <- par("usr") > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > abline(h = 5, lty = 2, lwd = 0) > arrows(-0.8, 2.1, -0.8, 0, length = 0.1, angle = 25) > arrows(-0.8, 2.9, -0.8, 5, length = 0.1, angle = 25) > text(-0.8, 2.5, expression(phi[1]), adj = c(0.5, 0.5)) > segments(-0.4, 1, 0, 1, lty = 2, lwd = 0.75) > arrows(-0.3, 0.25, -0.3, 0, length = 0.07, angle = 25) > arrows(-0.3, 0.75, -0.3, 1, length = 0.07, angle = 25) > text(-0.3, 0.5, expression(phi[2]), adj = c(0.5, 0.5)) > segments(1, 3.025, 1, 4, lty = 2, lwd = 0.75) > arrows(0.2, 3.5, 0, 3.5, length = 0.08, angle = 25) > arrows(0.8, 3.5, 1, 3.5, length = 0.08, angle = 25) > text(0.5, 3.5, expression(t[0.5]), adj = c(0.5, 0.5)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSasympOff") > ### * SSasympOff > > flush(stderr()); flush(stdout()) > > ### Name: SSasympOff > ### Title: Self-Starting Nls Asymptotic Regression Model with an Offset > ### Aliases: SSasympOff > ### Keywords: models > > ### ** Examples > > CO2.Qn1 <- CO2[CO2$Plant == "Qn1", ] > SSasympOff(CO2.Qn1$conc, 32, -4, 43) # response only [1] 19.65412 29.14785 31.27791 31.88435 31.99259 31.99970 32.00000 > Asym <- 32; lrc <- -4; c0 <- 43 > SSasympOff(CO2.Qn1$conc, Asym, lrc, c0) # response and gradient [1] 19.65412 29.14785 31.27791 31.88435 31.99259 31.99970 32.00000 attr(,"gradient") Asym lrc c0 [1,] 0.6141911 1.175838e+01 -2.261227e-01 [2,] 0.9108704 6.895531e+00 -5.223887e-02 [3,] 0.9774346 2.737698e+00 -1.322559e-02 [4,] 0.9963859 6.503026e-01 -2.118250e-03 [5,] 0.9997683 6.204920e-02 -1.357751e-04 [6,] 0.9999906 3.479529e-03 -5.505583e-06 [7,] 1.0000000 1.369435e-05 -1.430967e-08 > getInitial(uptake ~ SSasympOff(conc, Asym, lrc, c0), data = CO2.Qn1) $Asym [1] 38.13978 $lrc [1] -4.380647 $c0 [1] 51.22324 > ## Initial values are in fact the converged values > fm1 <- nls(uptake ~ SSasympOff(conc, Asym, lrc, c0), data = CO2.Qn1) > summary(fm1) Formula: uptake ~ SSasympOff(conc, Asym, lrc, c0) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 38.1398 0.9164 41.620 1.99e-06 *** lrc -4.3806 0.2042 -21.457 2.79e-05 *** c0 51.2232 11.6698 4.389 0.0118 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.663 on 4 degrees of freedom > ## Don't show: > require(graphics) > > xx <- seq(0.5, 5, len = 101) > yy <- 5 * (1 - exp(-(xx - 0.5)/(2*log(2)))) > par(mar = c(0, 0, 4.0, 0)) > plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,6), xlim = c(-1, 5), + xlab = "", ylab = "", lwd = 2, + main = "Parameters in the SSasympOff model") > usr <- par("usr") > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > abline(h = 5, lty = 2, lwd = 0) > arrows(-0.8, 2.1, -0.8, 0, length = 0.1, angle = 25) > arrows(-0.8, 2.9, -0.8, 5, length = 0.1, angle = 25) > text(-0.8, 2.5, expression(phi[1]), adj = c(0.5, 0.5)) > segments(0.5, 0, 0.5, 3, lty = 2, lwd = 0.75) > text(0.5, 3.1, expression(phi[3]), adj = c(0.5, 0)) > segments(1.5, 2.525, 1.5, 3, lty = 2, lwd = 0.75) > arrows(0.7, 2.65, 0.5, 2.65, length = 0.08, angle = 25) > arrows(1.3, 2.65, 1.5, 2.65, length = 0.08, angle = 25) > text(1.0, 2.65, expression(t[0.5]), adj = c(0.5, 0.5)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSasympOrig") > ### * SSasympOrig > > flush(stderr()); flush(stdout()) > > ### Name: SSasympOrig > ### Title: Self-Starting Nls Asymptotic Regression Model through the Origin > ### Aliases: SSasympOrig > ### Keywords: models > > ### ** Examples > ## Don't show: > require(graphics) > > xx <- seq(0, 5, len = 101) > yy <- 5 * (1- exp(-xx/(2*log(2)))) > par(mar = c(0, 0, 3.5, 0)) > plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,6), xlim = c(-1, 5), + xlab = "", ylab = "", lwd = 2, + main = "Parameters in the SSasympOrig model") > usr <- par("usr") > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > abline(h = 5, lty = 2, lwd = 0) > arrows(-0.8, 2.1, -0.8, 0, length = 0.1, angle = 25) > arrows(-0.8, 2.9, -0.8, 5, length = 0.1, angle = 25) > text(-0.8, 2.5, expression(phi[1]), adj = c(0.5, 0.5)) > segments(1, 2.525, 1, 3.5, lty = 2, lwd = 0.75) > arrows(0.2, 3.0, 0, 3.0, length = 0.08, angle = 25) > arrows(0.8, 3.0, 1, 3.0, length = 0.08, angle = 25) > text(0.5, 3.0, expression(t[0.5]), adj = c(0.5, 0.5)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSbiexp") > ### * SSbiexp > > flush(stderr()); flush(stdout()) > > ### Name: SSbiexp > ### Title: Self-Starting Nls Biexponential model > ### Aliases: SSbiexp > ### Keywords: models > > ### ** Examples > > Indo.1 <- Indometh[Indometh$Subject == 1, ] > SSbiexp( Indo.1$time, 3, 1, 0.6, -1.3 ) # response only [1] 2.08098572 1.29421044 0.87967145 0.65483364 0.52711347 0.36094621 [7] 0.26575722 0.20176113 0.15359129 0.11694936 0.06780767 > A1 <- 3; lrc1 <- 1; A2 <- 0.6; lrc2 <- -1.3 > SSbiexp( Indo.1$time, A1, lrc1, A2, lrc2 ) # response and gradient [1] 2.08098572 1.29421044 0.87967145 0.65483364 0.52711347 0.36094621 [7] 0.26575722 0.20176113 0.15359129 0.11694936 0.06780767 attr(,"gradient") A1 lrc1 A2 lrc2 [1,] 5.068347e-01 -1.033290e+00 0.9341363 -0.03818728 [2,] 2.568814e-01 -1.047414e+00 0.8726106 -0.07134424 [3,] 1.301964e-01 -7.962985e-01 0.8151372 -0.09996786 [4,] 6.598804e-02 -5.381222e-01 0.7614492 -0.12451147 [5,] 3.344502e-02 -3.409237e-01 0.7112973 -0.14538835 [6,] 4.354421e-03 -7.101926e-02 0.5798049 -0.18961833 [7,] 2.873397e-04 -7.029632e-03 0.4414920 -0.21657709 [8,] 1.896098e-05 -6.184955e-04 0.3361737 -0.21988328 [9,] 1.251198e-06 -5.101663e-05 0.2559792 -0.20928744 [10,] 8.256409e-08 -4.039784e-06 0.1949152 -0.19123411 [11,] 3.595188e-10 -2.345456e-08 0.1130128 -0.14783797 > print(getInitial(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1), + digits = 5) A1 lrc1 A2 lrc2 2.02928 0.57939 0.19155 -1.78778 > ## Initial values are in fact the converged values > fm1 <- nls(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1) > summary(fm1) Formula: conc ~ SSbiexp(time, A1, lrc1, A2, lrc2) Parameters: Estimate Std. Error t value Pr(>|t|) A1 2.0293 0.1099 18.464 3.39e-07 *** lrc1 0.5794 0.1247 4.648 0.00235 ** A2 0.1915 0.1106 1.731 0.12698 lrc2 -1.7878 0.7871 -2.271 0.05737 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.04103 on 7 degrees of freedom > ## Don't show: > require(graphics) > > xx <- seq(0, 5, len = 101) > y1 <- 3.5 * exp(-4*xx) > y2 <- 1.5 * exp(-xx) > yy <- y1 + y2 > par(mar = c(0, 0, 3.5, 0)) > plot(xx, yy, type = "n", axes = FALSE, ylim = c(-0.2,6), xlim = c(0, 5), + xlab = "", ylab = "", main = "Components of the SSbiexp model") > usr <- par("usr") > lines(xx, y1, lty = 2, lwd = 0.75) > lines(xx, y2, lty = 3, lwd = 0.75) > lines(xx, yy, lwd = 2) > segments(1:5, 0, 1:5, -0.15) > segments(0, 1:5, -0.06, 1:5) > text(-0.1, 1:5, 1:5, adj = c(1, 0.25), cex = 0.7) > text(1:5, -0.15, 1:5, adj = c(0.5, 1), cex = 0.7) > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSfol") > ### * SSfol > > flush(stderr()); flush(stdout()) > > ### Name: SSfol > ### Title: Self-Starting Nls First-order Compartment Model > ### Aliases: SSfol > ### Keywords: models > > ### ** Examples > > Theoph.1 <- Theoph[ Theoph$Subject == 1, ] > SSfol(Theoph.1$Dose, Theoph.1$Time, -2.5, 0.5, -3) # response only [1] 0.000000 2.214486 3.930988 5.261945 5.659813 5.084852 4.587699 3.916808 [9] 3.318395 2.579204 0.943593 > lKe <- -2.5; lKa <- 0.5; lCl <- -3 > SSfol(Theoph.1$Dose, Theoph.1$Time, lKe, lKa, lCl) # response and gradient [1] 0.000000 2.214486 3.930988 5.261945 5.659813 5.084852 4.587699 3.916808 [9] 3.318395 2.579204 0.943593 attr(,"gradient") lKe lKa lCl [1,] 0.000000 0.00000000 0.000000 [2,] 2.190284 1.78781716 -2.214486 [3,] 3.825518 2.35519507 -3.930988 [4,] 4.952713 1.75648252 -5.261945 [5,] 4.976520 0.53458070 -5.659813 [6,] 3.752822 -0.18560297 -5.084852 [7,] 2.906859 -0.22729852 -4.587699 [8,] 1.861771 -0.20447579 -3.916808 [9,] 1.027129 -0.17383515 -3.318395 [10,] 0.148370 -0.13513891 -2.579204 [11,] -0.894541 -0.04944021 -0.943593 > getInitial(conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data = Theoph.1) $lKe [1] -2.994845 $lKa [1] 0.609169 $lCl [1] -3.971003 > ## Initial values are in fact the converged values > fm1 <- nls(conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data = Theoph.1) > summary(fm1) Formula: conc ~ SSfol(Dose, Time, lKe, lKa, lCl) Parameters: Estimate Std. Error t value Pr(>|t|) lKe -2.9196 0.1709 -17.085 1.40e-07 *** lKa 0.5752 0.1728 3.328 0.0104 * lCl -3.9159 0.1273 -30.768 1.35e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.732 on 8 degrees of freedom > > > > cleanEx() > nameEx("SSfpl") > ### * SSfpl > > flush(stderr()); flush(stdout()) > > ### Name: SSfpl > ### Title: Self-Starting Nls Four-Parameter Logistic Model > ### Aliases: SSfpl > ### Keywords: models > > ### ** Examples > > Chick.1 <- ChickWeight[ChickWeight$Chick == 1, ] > SSfpl(Chick.1$Time, 13, 368, 14, 6) # response only [1] 44.38189 55.31704 69.39853 87.05603 108.47420 133.43149 161.18758 [8] 190.50000 219.81242 247.56851 272.52580 283.70240 > A <- 13; B <- 368; xmid <- 14; scal <- 6 > SSfpl(Chick.1$Time, A, B, xmid, scal) # response and gradient [1] 44.38189 55.31704 69.39853 87.05603 108.47420 133.43149 161.18758 [8] 190.50000 219.81242 247.56851 272.52580 283.70240 attr(,"gradient") A B xmid scal [1,] 0.9116003 0.08839968 -4.767956 11.125231 [2,] 0.8807971 0.11920292 -6.212120 12.424241 [3,] 0.8411309 0.15886910 -7.906425 13.177374 [4,] 0.7913915 0.20860853 -9.767885 13.023846 [5,] 0.7310586 0.26894142 -11.632873 11.632873 [6,] 0.6607564 0.33924363 -13.262646 8.841764 [7,] 0.5825702 0.41742979 -14.388278 4.796093 [8,] 0.5000000 0.50000000 -14.791667 0.000000 [9,] 0.4174298 0.58257021 -14.388278 -4.796093 [10,] 0.3392436 0.66075637 -13.262646 -8.841764 [11,] 0.2689414 0.73105858 -11.632873 -11.632873 [12,] 0.2374580 0.76254197 -10.713410 -12.498978 > print(getInitial(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1), + digits = 5) A B xmid scal 27.4532 348.9712 19.3905 6.6726 > ## Initial values are in fact the converged values > fm1 <- nls(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1) > summary(fm1) Formula: weight ~ SSfpl(Time, A, B, xmid, scal) Parameters: Estimate Std. Error t value Pr(>|t|) A 27.453 6.601 4.159 0.003169 ** B 348.971 57.899 6.027 0.000314 *** xmid 19.391 2.194 8.836 2.12e-05 *** scal 6.673 1.002 6.662 0.000159 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.351 on 8 degrees of freedom > ## Don't show: > require(graphics) > > xx <- seq(-0.5, 5, len = 101) > yy <- 1 + 4 / ( 1 + exp((2-xx))) > par(mar = c(0, 0, 3.5, 0)) > plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,6), xlim = c(-1, 5), + xlab = "", ylab = "", lwd = 2, + main = "Parameters in the SSfpl model") > usr <- par("usr") > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > abline(h = 5, lty = 2, lwd = 0) > arrows(-0.8, 2.1, -0.8, 0, length = 0.1, angle = 25) > arrows(-0.8, 2.9, -0.8, 5, length = 0.1, angle = 25) > text(-0.8, 2.5, expression(phi[1]), adj = c(0.5, 0.5)) > abline(h = 1, lty = 2, lwd = 0) > arrows(-0.3, 0.25, -0.3, 0, length = 0.07, angle = 25) > arrows(-0.3, 0.75, -0.3, 1, length = 0.07, angle = 25) > text(-0.3, 0.5, expression(phi[2]), adj = c(0.5, 0.5)) > segments(2, 0, 2, 3.3, lty = 2, lwd = 0.75) > text(2, 3.3, expression(phi[3]), adj = c(0.5, 0)) > segments(3, 1+4/(1+exp(-1)) - 0.025, 3, 2.5, lty = 2, lwd = 0.75) > arrows(2.3, 2.7, 2.0, 2.7, length = 0.08, angle = 25) > arrows(2.7, 2.7, 3.0, 2.7, length = 0.08, angle = 25) > text(2.5, 2.7, expression(phi[4]), adj = c(0.5, 0.5)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSgompertz") > ### * SSgompertz > > flush(stderr()); flush(stdout()) > > ### Name: SSgompertz > ### Title: Self-Starting Nls Gompertz Growth Model > ### Aliases: SSgompertz > ### Keywords: models > > ### ** Examples > > DNase.1 <- subset(DNase, Run == 1) > SSgompertz(log(DNase.1$conc), 4.5, 2.3, 0.7) # response only [1] 0.00525729 0.00525729 0.07323255 0.07323255 0.18049064 0.18049064 [7] 0.36508763 0.36508763 0.63288772 0.63288772 0.97257180 0.97257180 [13] 1.36033340 1.36033340 1.76786902 1.76786902 > Asym <- 4.5; b2 <- 2.3; b3 <- 0.7 > SSgompertz(log(DNase.1$conc), Asym, b2, b3) # response and gradient [1] 0.00525729 0.00525729 0.07323255 0.07323255 0.18049064 0.18049064 [7] 0.36508763 0.36508763 0.63288772 0.63288772 0.97257180 0.97257180 [13] 1.36033340 1.36033340 1.76786902 1.76786902 attr(,"gradient") Asym b2 b3 [1,] 0.001168287 -0.01543407 0.1531221 [2,] 0.001168287 -0.01543407 0.1531221 [3,] 0.016273900 -0.13112424 0.7036230 [4,] 0.016273900 -0.13112424 0.7036230 [5,] 0.040109031 -0.25238507 0.7795153 [6,] 0.040109031 -0.25238507 0.7795153 [7,] 0.081130585 -0.39869082 0.3233828 [8,] 0.081130585 -0.39869082 0.3233828 [9,] 0.140641716 -0.53975407 -0.7914802 [10,] 0.140641716 -0.53975407 -0.7914802 [11,] 0.216127067 -0.64777036 -2.4251586 [12,] 0.216127067 -0.64777036 -2.4251586 [13,] 0.302296311 -0.70757894 -4.2605728 [14,] 0.302296311 -0.70757894 -4.2605728 [15,] 0.392859783 -0.71814108 -5.9597255 [16,] 0.392859783 -0.71814108 -5.9597255 > print(getInitial(density ~ SSgompertz(log(conc), Asym, b2, b3), + data = DNase.1), digits = 5) Asym b2 b3 4.60333 2.27134 0.71647 > ## Initial values are in fact the converged values > fm1 <- nls(density ~ SSgompertz(log(conc), Asym, b2, b3), + data = DNase.1) > summary(fm1) Formula: density ~ SSgompertz(log(conc), Asym, b2, b3) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 4.60333 0.65321 7.047 8.71e-06 *** b2 2.27134 0.14373 15.803 7.24e-10 *** b3 0.71647 0.02206 32.475 7.85e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.02684 on 13 degrees of freedom > > > > cleanEx() > nameEx("SSlogis") > ### * SSlogis > > flush(stderr()); flush(stdout()) > > ### Name: SSlogis > ### Title: Self-Starting Nls Logistic Model > ### Aliases: SSlogis > ### Keywords: models > > ### ** Examples > ## Don't show: > require(graphics) > > xx <- seq(-0.5, 5, len = 101) > yy <- 5 / ( 1 + exp((2-xx))) > par(mar = c(0, 0, 3.5, 0)) > plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,6), xlim = c(-1, 5), + xlab = "", ylab = "", lwd = 2, + main = "Parameters in the SSlogis model") > usr <- par("usr") > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > abline(h = 5, lty = 2, lwd = 0) > arrows(-0.8, 2.1, -0.8, 0, length = 0.1, angle = 25) > arrows(-0.8, 2.9, -0.8, 5, length = 0.1, angle = 25) > text(-0.8, 2.5, expression(phi[1]), adj = c(0.5, 0.5)) > segments(2, 0, 2, 4.0, lty = 2, lwd = 0.75) > text(2, 4.0, expression(phi[2]), adj = c(0.5, 0)) > segments(3, 5/(1+exp(-1)) + 0.025, 3, 4.0, lty = 2, lwd = 0.75) > arrows(2.3, 3.8, 2.0, 3.8, length = 0.08, angle = 25) > arrows(2.7, 3.8, 3.0, 3.8, length = 0.08, angle = 25) > text(2.5, 3.8, expression(phi[3]), adj = c(0.5, 0.5)) > ## End Don't show > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSmicmen") > ### * SSmicmen > > flush(stderr()); flush(stdout()) > > ### Name: SSmicmen > ### Title: Self-Starting Nls Michaelis-Menten Model > ### Aliases: SSmicmen > ### Keywords: models > > ### ** Examples > > PurTrt <- Puromycin[ Puromycin$state == "treated", ] > SSmicmen(PurTrt$conc, 200, 0.05) # response only [1] 57.14286 57.14286 109.09091 109.09091 137.50000 137.50000 162.96296 [8] 162.96296 183.60656 183.60656 191.30435 191.30435 > Vm <- 200; K <- 0.05 > SSmicmen(PurTrt$conc, Vm, K) # response and gradient [1] 57.14286 57.14286 109.09091 109.09091 137.50000 137.50000 162.96296 [8] 162.96296 183.60656 183.60656 191.30435 191.30435 attr(,"gradient") Vm K [1,] 0.2857143 -816.3265 [2,] 0.2857143 -816.3265 [3,] 0.5454545 -991.7355 [4,] 0.5454545 -991.7355 [5,] 0.6875000 -859.3750 [6,] 0.6875000 -859.3750 [7,] 0.8148148 -603.5665 [8,] 0.8148148 -603.5665 [9,] 0.9180328 -300.9944 [10,] 0.9180328 -300.9944 [11,] 0.9565217 -166.3516 [12,] 0.9565217 -166.3516 > print(getInitial(rate ~ SSmicmen(conc, Vm, K), data = PurTrt), digits=3) Vm K 212.6837 0.0641 > ## Initial values are in fact the converged values > fm1 <- nls(rate ~ SSmicmen(conc, Vm, K), data = PurTrt) > summary(fm1) Formula: rate ~ SSmicmen(conc, Vm, K) Parameters: Estimate Std. Error t value Pr(>|t|) Vm 2.127e+02 6.947e+00 30.615 3.24e-11 *** K 6.412e-02 8.281e-03 7.743 1.57e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 10.93 on 10 degrees of freedom > ## Alternative call using the subset argument > fm2 <- nls(rate ~ SSmicmen(conc, Vm, K), data = Puromycin, + subset = state == "treated") > summary(fm2) Formula: rate ~ SSmicmen(conc, Vm, K) Parameters: Estimate Std. Error t value Pr(>|t|) Vm 2.127e+02 6.947e+00 30.615 3.24e-11 *** K 6.412e-02 8.281e-03 7.743 1.57e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 10.93 on 10 degrees of freedom > ## Don't show: > require(graphics) > > xx <- seq(0, 5, len = 101) > yy <- 5 * xx/(1+xx) > par(mar = c(0, 0, 3.5, 0)) > plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,6), xlim = c(-1, 5), + xlab = "", ylab = "", lwd = 2, + main = "Parameters in the SSmicmen model") > usr <- par("usr") > arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25) > arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25) > text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0)) > text(-0.1, usr[4], "y", adj = c(1, 1)) > abline(h = 5, lty = 2, lwd = 0) > arrows(-0.8, 2.1, -0.8, 0, length = 0.1, angle = 25) > arrows(-0.8, 2.9, -0.8, 5, length = 0.1, angle = 25) > text(-0.8, 2.5, expression(phi[1]), adj = c(0.5, 0.5)) > segments(1, 0, 1, 2.7, lty = 2, lwd = 0.75) > text(1, 2.7, expression(phi[2]), adj = c(0.5, 0)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("SSweibull") > ### * SSweibull > > flush(stderr()); flush(stdout()) > > ### Name: SSweibull > ### Title: Self-Starting Nls Weibull Growth Curve Model > ### Aliases: SSweibull > ### Keywords: models > > ### ** Examples > > Chick.6 <- subset(ChickWeight, (Chick == 6) & (Time > 0)) > SSweibull(Chick.6$Time, 160, 115, -5.5, 2.5) # response only [1] 47.62811 59.09743 79.79756 105.12008 128.41818 145.02585 154.25783 [8] 158.24919 159.58222 159.92314 159.97023 > Asym <- 160; Drop <- 115; lrc <- -5.5; pwr <- 2.5 > SSweibull(Chick.6$Time, Asym, Drop, lrc, pwr) # response and gradient [1] 47.62811 59.09743 79.79756 105.12008 128.41818 145.02585 154.25783 [8] 158.24919 159.58222 159.92314 159.97023 attr(,"gradient") Asym Drop lrc pwr [1,] 1 -0.9771469094 2.5978438 1.8006881 [2,] 1 -0.8774136912 13.1957043 18.2931305 [3,] 1 -0.6974125358 28.9032091 51.7875987 [4,] 1 -0.4772166721 40.5993205 84.4239136 [5,] 1 -0.2746244909 40.8147795 93.9795029 [6,] 1 -0.1302099955 30.5264027 75.8552610 [7,] 1 -0.0499319343 17.2098335 45.4177374 [8,] 1 -0.0152244293 7.3268815 20.3144290 [9,] 1 -0.0036328431 2.3469622 6.7835933 [10,] 1 -0.0006683898 0.5619310 1.6833949 [11,] 1 -0.0002589123 0.2459116 0.7486834 > getInitial(weight ~ SSweibull(Time, Asym, Drop, lrc, pwr), data = Chick.6) Asym Drop lrc pwr 158.501204 110.997081 -5.993421 2.646141 > ## Initial values are in fact the converged values > fm1 <- nls(weight ~ SSweibull(Time, Asym, Drop, lrc, pwr), data = Chick.6) > summary(fm1) Formula: weight ~ SSweibull(Time, Asym, Drop, lrc, pwr) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 158.5012 1.1769 134.67 3.28e-13 *** Drop 110.9971 2.6330 42.16 1.10e-09 *** lrc -5.9934 0.3733 -16.05 8.83e-07 *** pwr 2.6461 0.1613 16.41 7.62e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.061 on 7 degrees of freedom > > > > cleanEx() > nameEx("SignRank") > ### * SignRank > > flush(stderr()); flush(stdout()) > > ### Name: SignRank > ### Title: Distribution of the Wilcoxon Signed Rank Statistic > ### Aliases: SignRank dsignrank psignrank qsignrank rsignrank > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > par(mfrow=c(2,2)) > for(n in c(4:5,10,40)) { + x <- seq(0, n*(n+1)/2, length=501) + plot(x, dsignrank(x,n=n), type='l', main=paste("dsignrank(x,n=",n,")")) + } > ## Don't show: > p <- c(1, 1, 1, 2, 2:6, 8, 10, 11, 13, 15, 17, 20, 22, 24, + 27, 29, 31, 33, 35, 36, 38, 39, 39, 40) > stopifnot(round(dsignrank(0:56, n=10)* 2^10) == c(p, rev(p), 0), + qsignrank((1:16)/ 16, n=4) == c(0:2, rep(3:7, each=2), 8:10)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("StructTS") > ### * StructTS > > flush(stderr()); flush(stdout()) > > ### Name: StructTS > ### Title: Fit Structural Time Series > ### Aliases: StructTS print.StructTS predict.StructTS > ### Keywords: ts > > ### ** Examples > > ## see also JohnsonJohnson, Nile and AirPassengers > require(graphics) > > trees <- window(treering, start=0) > (fit <- StructTS(trees, type = "level")) Call: StructTS(x = trees, type = "level") Variances: level epsilon 0.00037 0.07199 > plot(trees) > lines(fitted(fit), col = "green") > tsdiag(fit) > > (fit <- StructTS(log10(UKgas), type = "BSM")) Call: StructTS(x = log10(UKgas), type = "BSM") Variances: level slope seas epsilon 0.000e+00 1.733e-05 7.137e-04 3.678e-04 > par(mfrow = c(4, 1)) # to give appropriate aspect ratio for next plot. > plot(log10(UKgas)) > plot(cbind(fitted(fit), resids=resid(fit)), main = "UK gas consumption") > > ## keep some parameters fixed; trace optimizer: > StructTS(log10(UKgas), type = "BSM", fixed = c(0.1,0.001,NA,NA), + optim.control = list(trace=TRUE)) iter 0 value -0.914421 iter 10 value -0.936176 final value -0.936176 converged Call: StructTS(x = log10(UKgas), type = "BSM", fixed = c(0.1, 0.001, NA, NA), optim.control = list(trace = TRUE)) Variances: level slope seas epsilon 0.1000000 0.0010000 0.0003012 0.0000000 > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("TDist") > ### * TDist > > flush(stderr()); flush(stdout()) > > ### Name: TDist > ### Title: The Student t Distribution > ### Aliases: TDist dt pt qt rt > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > 1 - pt(1:5, df = 1) [1] 0.25000000 0.14758362 0.10241638 0.07797913 0.06283296 > qt(.975, df = c(1:10,20,50,100,1000)) [1] 12.706205 4.302653 3.182446 2.776445 2.570582 2.446912 2.364624 [8] 2.306004 2.262157 2.228139 2.085963 2.008559 1.983972 1.962339 > > tt <- seq(0,10, len=21) > ncp <- seq(0,6, len=31) > ptn <- outer(tt,ncp, function(t,d) pt(t, df = 3, ncp=d)) > t.tit <- "Non-central t - Probabilities" > image(tt,ncp,ptn, zlim=c(0,1), main = t.tit) > persp(tt,ncp,ptn, zlim=0:1, r=2, phi=20, theta=200, main=t.tit, + xlab = "t", ylab = "non-centrality parameter", + zlab = "Pr(T <= t)") > > plot(function(x) dt(x, df = 3, ncp = 2), -3, 11, ylim = c(0, 0.32), + main="Non-central t - Density", yaxs="i") > > > > cleanEx() > nameEx("Tukey") > ### * Tukey > > flush(stderr()); flush(stdout()) > > ### Name: Tukey > ### Title: The Studentized Range Distribution > ### Aliases: Tukey ptukey qtukey > ### Keywords: distribution > > ### ** Examples > > if(interactive()) + curve(ptukey(x, nm=6, df=5), from=-1, to=8, n=101) > (ptt <- ptukey(0:10, 2, df= 5)) [1] 0.0000000 0.4889159 0.7835628 0.9126407 0.9632574 0.9833586 0.9918510 [8] 0.9957141 0.9976011 0.9985838 0.9991249 > (qtt <- qtukey(.95, 2, df= 2:11)) [1] 6.079637 4.500659 3.926503 3.635351 3.460456 3.344084 3.261182 3.199173 [9] 3.151064 3.112663 > ## The precision may be not much more than about 8 digits: > > > > cleanEx() > nameEx("TukeyHSD") > ### * TukeyHSD > > flush(stderr()); flush(stdout()) > > ### Name: TukeyHSD > ### Title: Compute Tukey Honest Significant Differences > ### Aliases: TukeyHSD TukeyHSD.aov print.TukeyHSD plot.TukeyHSD > ### Keywords: models design > > ### ** Examples > > require(graphics) > > summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)) Df Sum Sq Mean Sq F value Pr(>F) wool 1 451 450.7 3.339 0.07361 . tension 2 2034 1017.1 7.537 0.00138 ** Residuals 50 6748 135.0 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(fm1, "tension", ordered = TRUE) Tukey multiple comparisons of means 95% family-wise confidence level factor levels have been ordered Fit: aov(formula = breaks ~ wool + tension, data = warpbreaks) $tension diff lwr upr p adj M-H 4.722222 -4.6311985 14.07564 0.4474210 L-H 14.722222 5.3688015 24.07564 0.0011218 L-M 10.000000 0.6465793 19.35342 0.0336262 > plot(TukeyHSD(fm1, "tension")) > > > > cleanEx() > nameEx("Uniform") > ### * Uniform > > flush(stderr()); flush(stdout()) > > ### Name: Uniform > ### Title: The Uniform Distribution > ### Aliases: Uniform dunif punif qunif runif > ### Keywords: distribution > > ### ** Examples > > u <- runif(20) > > ## The following relations always hold : > punif(u) == u [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [16] TRUE TRUE TRUE TRUE TRUE > dunif(u) == 1 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE [16] TRUE TRUE TRUE TRUE TRUE > > var(runif(10000))#- ~ = 1/12 = .08333 [1] 0.08475621 > > > > cleanEx() > nameEx("Weibull") > ### * Weibull > > flush(stderr()); flush(stdout()) > > ### Name: Weibull > ### Title: The Weibull Distribution > ### Aliases: Weibull dweibull pweibull qweibull rweibull > ### Keywords: distribution > > ### ** Examples > > x <- c(0,rlnorm(50)) > all.equal(dweibull(x, shape = 1), dexp(x)) [1] TRUE > all.equal(pweibull(x, shape = 1, scale = pi), pexp(x, rate = 1/pi)) [1] TRUE > ## Cumulative hazard H(): > all.equal(pweibull(x, 2.5, pi, lower.tail=FALSE, log.p=TRUE), -(x/pi)^2.5, + tol = 1e-15) [1] TRUE > all.equal(qweibull(x/11, shape = 1, scale = pi), qexp(x/11, rate = 1/pi)) [1] TRUE > > > > cleanEx() > nameEx("Wilcoxon") > ### * Wilcoxon > > flush(stderr()); flush(stdout()) > > ### Name: Wilcoxon > ### Title: Distribution of the Wilcoxon Rank Sum Statistic > ### Aliases: Wilcoxon dwilcox pwilcox qwilcox rwilcox > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > x <- -1:(4*6 + 1) > fx <- dwilcox(x, 4, 6) > Fx <- pwilcox(x, 4, 6) > > layout(rbind(1,2), widths=1, heights=c(3,2)) > plot(x, fx,type='h', col="violet", + main= "Probabilities (density) of Wilcoxon-Statist.(n=6,m=4)") > plot(x, Fx,type="s", col="blue", + main= "Distribution of Wilcoxon-Statist.(n=6,m=4)") > abline(h=0:1, col="gray20",lty=2) > layout(1)# set back > > N <- 200 > hist(U <- rwilcox(N, m=4,n=6), breaks=0:25 - 1/2, + border="red", col="pink", sub = paste("N =",N)) > mtext("N * f(x), f() = true \"density\"", side=3, col="blue") > lines(x, N*fx, type='h', col='blue', lwd=2) > points(x, N*fx, cex=2) > > ## Better is a Quantile-Quantile Plot > qqplot(U, qw <- qwilcox((1:N - 1/2)/N, m=4,n=6), + main = paste("Q-Q-Plot of empirical and theoretical quantiles", + "Wilcoxon Statistic, (m=4, n=6)",sep="\n")) > n <- as.numeric(names(print(tU <- table(U)))) U 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 1 4 6 7 10 8 12 14 22 13 16 13 23 16 11 7 4 4 3 3 1 1 > text(n+.2, n+.5, labels=tU, col="red") > > > > cleanEx() > nameEx("acf") > ### * acf > > flush(stderr()); flush(stdout()) > > ### Name: acf > ### Title: Auto- and Cross- Covariance and -Correlation Function Estimation > ### Aliases: acf ccf pacf pacf.default [.acf > ### Keywords: ts > > ### ** Examples > > require(graphics) > > ## Examples from Venables & Ripley > acf(lh) > acf(lh, type = "covariance") > pacf(lh) > > acf(ldeaths) > acf(ldeaths, ci.type = "ma") > acf(ts.union(mdeaths, fdeaths)) > ccf(mdeaths, fdeaths, ylab = "cross-correlation") > # (just the cross-correlations) > > presidents # contains missing values Qtr1 Qtr2 Qtr3 Qtr4 1945 NA 87 82 75 1946 63 50 43 32 1947 35 60 54 55 1948 36 39 NA NA 1949 69 57 57 51 1950 45 37 46 39 1951 36 24 32 23 1952 25 32 NA 32 1953 59 74 75 60 1954 71 61 71 57 1955 71 68 79 73 1956 76 71 67 75 1957 79 62 63 57 1958 60 49 48 52 1959 57 62 61 66 1960 71 62 61 57 1961 72 83 71 78 1962 79 71 62 74 1963 76 64 62 57 1964 80 73 69 69 1965 71 64 69 62 1966 63 46 56 44 1967 44 52 38 46 1968 36 49 35 44 1969 59 65 65 56 1970 66 53 61 52 1971 51 48 54 49 1972 49 61 NA NA 1973 68 44 40 27 1974 28 25 24 24 > acf(presidents, na.action = na.pass) > pacf(presidents, na.action = na.pass) > > > > cleanEx() > nameEx("acf2AR") > ### * acf2AR > > flush(stderr()); flush(stdout()) > > ### Name: acf2AR > ### Title: Compute an AR Process Exactly Fitting an ACF > ### Aliases: acf2AR > ### Keywords: ts > > ### ** Examples > > (Acf <- ARMAacf(c(0.6, 0.3, -0.2))) 0 1 2 3 1.0000000 0.6923077 0.5769231 0.3538462 > acf2AR(Acf) 1 2 3 ar(1) 0.6923077 0.0000 0.0 ar(2) 0.5625000 0.1875 0.0 ar(3) 0.6000000 0.3000 -0.2 > > > > cleanEx() > nameEx("add1") > ### * add1 > > flush(stderr()); flush(stdout()) > > ### Name: add1 > ### Title: Add or Drop All Possible Single Terms to a Model > ### Aliases: add1 add1.default add1.lm add1.glm drop1 drop1.default > ### drop1.lm drop1.glm > ### Keywords: models > > ### ** Examples > > ## Don't show: > od <- options(digits=5) > ## End Don't show > require(graphics); require(utils) > ## following example(swiss) > lm1 <- lm(Fertility ~ ., data = swiss) > add1(lm1, ~ I(Education^2) + .^2) Single term additions Model: Fertility ~ Agriculture + Examination + Education + Catholic + Infant.Mortality Df Sum of Sq RSS AIC 2105 191 I(Education^2) 1 11.8 2093 192 Agriculture:Examination 1 10.7 2094 192 Agriculture:Education 1 1.8 2103 193 Agriculture:Catholic 1 75.0 2030 191 Agriculture:Infant.Mortality 1 4.4 2101 193 Examination:Education 1 48.7 2056 192 Examination:Catholic 1 40.8 2064 192 Examination:Infant.Mortality 1 65.9 2039 191 Education:Catholic 1 278.2 1827 186 Education:Infant.Mortality 1 93.0 2012 191 Catholic:Infant.Mortality 1 2.4 2103 193 > drop1(lm1, test="F") # So called 'type II' anova Single term deletions Model: Fertility ~ Agriculture + Examination + Education + Catholic + Infant.Mortality Df Sum of Sq RSS AIC F value Pr(>F) 2105 191 Agriculture 1 308 2413 195 5.99 0.0187 * Examination 1 53 2158 190 1.03 0.3155 Education 1 1163 3268 209 22.64 2.4e-05 *** Catholic 1 448 2553 198 8.72 0.0052 ** Infant.Mortality 1 409 2514 197 7.96 0.0073 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ## following example(glm) > ## Don't show: > example(glm, echo = FALSE) treatment outcome counts 1 1 1 18 2 1 2 17 3 1 3 15 4 2 1 20 5 2 2 10 6 2 3 20 7 3 1 25 8 3 2 13 9 3 3 12 > ## End Don't show > drop1(glm.D93, test="Chisq") Single term deletions Model: counts ~ outcome + treatment Df Deviance AIC LRT Pr(>Chi) 5.13 56.8 outcome 2 10.58 58.2 5.45 0.065 . treatment 2 5.13 52.8 0.00 1.000 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > drop1(glm.D93, test="F") Warning in drop1.glm(glm.D93, test = "F") : F test assumes 'quasipoisson' family Single term deletions Model: counts ~ outcome + treatment Df Deviance AIC F value Pr(>F) 5.13 56.8 outcome 2 10.58 58.2 2.13 0.23 treatment 2 5.13 52.8 0.00 1.00 > add1(glm.D93, scope=~outcome*treatment, test="Rao") ## Pearson Chi-square Single term additions Model: counts ~ outcome + treatment Df Deviance AIC Rao score Pr(>Chi) 5.13 56.8 outcome:treatment 4 0.00 59.6 5.17 0.27 > ## Don't show: > options(od) > ## End Don't show > > > > cleanEx() > nameEx("addmargins") > ### * addmargins > > flush(stderr()); flush(stdout()) > > ### Name: addmargins > ### Title: Puts Arbitrary Margins on Multidimensional Tables or Arrays > ### Aliases: addmargins > ### Keywords: manip array > > ### ** Examples > > Aye <- sample(c("Yes", "Si", "Oui"), 177, replace = TRUE) > Bee <- sample(c("Hum", "Buzz"), 177, replace = TRUE) > Sea <- sample(c("White", "Black", "Red", "Dead"), 177, replace = TRUE) > (A <- table(Aye, Bee, Sea)) , , Sea = Black Bee Aye Buzz Hum Oui 2 11 Si 8 13 Yes 7 6 , , Sea = Dead Bee Aye Buzz Hum Oui 5 10 Si 10 10 Yes 7 6 , , Sea = Red Bee Aye Buzz Hum Oui 5 6 Si 11 5 Yes 7 7 , , Sea = White Bee Aye Buzz Hum Oui 7 9 Si 4 12 Yes 3 6 > addmargins(A) , , Sea = Black Bee Aye Buzz Hum Sum Oui 2 11 13 Si 8 13 21 Yes 7 6 13 Sum 17 30 47 , , Sea = Dead Bee Aye Buzz Hum Sum Oui 5 10 15 Si 10 10 20 Yes 7 6 13 Sum 22 26 48 , , Sea = Red Bee Aye Buzz Hum Sum Oui 5 6 11 Si 11 5 16 Yes 7 7 14 Sum 23 18 41 , , Sea = White Bee Aye Buzz Hum Sum Oui 7 9 16 Si 4 12 16 Yes 3 6 9 Sum 14 27 41 , , Sea = Sum Bee Aye Buzz Hum Sum Oui 19 36 55 Si 33 40 73 Yes 24 25 49 Sum 76 101 177 > ## Don't show: > stopifnot(is.table(addmargins(A))) > ## End Don't show > ftable(A) Sea Black Dead Red White Aye Bee Oui Buzz 2 5 5 7 Hum 11 10 6 9 Si Buzz 8 10 11 4 Hum 13 10 5 12 Yes Buzz 7 7 7 3 Hum 6 6 7 6 > ftable(addmargins(A)) Sea Black Dead Red White Sum Aye Bee Oui Buzz 2 5 5 7 19 Hum 11 10 6 9 36 Sum 13 15 11 16 55 Si Buzz 8 10 11 4 33 Hum 13 10 5 12 40 Sum 21 20 16 16 73 Yes Buzz 7 7 7 3 24 Hum 6 6 7 6 25 Sum 13 13 14 9 49 Sum Buzz 17 22 23 14 76 Hum 30 26 18 27 101 Sum 47 48 41 41 177 > > # Non-commutative functions - note differences between resulting tables: > ftable(addmargins(A, c(1,3), + FUN = list(Sum = sum, list(Min = min, Max = max)))) Margins computed over dimensions in the following order: 1: Aye 2: Sea Sea Black Dead Red White Min Max Aye Bee Oui Buzz 2 5 5 7 2 7 Hum 11 10 6 9 6 11 Si Buzz 8 10 11 4 4 11 Hum 13 10 5 12 5 13 Yes Buzz 7 7 7 3 3 7 Hum 6 6 7 6 6 7 Sum Buzz 17 22 23 14 14 23 Hum 30 26 18 27 18 30 > ftable(addmargins(A, c(3,1), + FUN = list(list(Min = min, Max = max), Sum = sum))) Margins computed over dimensions in the following order: 1: Sea 2: Aye Sea Black Dead Red White Min Max Aye Bee Oui Buzz 2 5 5 7 2 7 Hum 11 10 6 9 6 11 Si Buzz 8 10 11 4 4 11 Hum 13 10 5 12 5 13 Yes Buzz 7 7 7 3 3 7 Hum 6 6 7 6 6 7 Sum Buzz 17 22 23 14 9 25 Hum 30 26 18 27 17 31 > > # Weird function needed to return the N when computing percentages > sqsm <- function(x) sum(x)^2/100 > B <- table(Sea, Bee) > round(sweep(addmargins(B, 1, list(list(All = sum, N = sqsm))), 2, + apply(B, 2, sum)/100, "/"), 1) Bee Sea Buzz Hum Black 22.4 29.7 Dead 28.9 25.7 Red 30.3 17.8 White 18.4 26.7 All 100.0 100.0 N 76.0 101.0 > round(sweep(addmargins(B, 2, list(list(All = sum, N = sqsm))), 1, + apply(B, 1, sum)/100, "/"), 1) Bee Sea Buzz Hum All N Black 36.2 63.8 100.0 47.0 Dead 45.8 54.2 100.0 48.0 Red 56.1 43.9 100.0 41.0 White 34.1 65.9 100.0 41.0 > > # A total over Bee requires formation of the Bee-margin first: > mB <- addmargins(B, 2, FUN = list(list(Total = sum))) > round(ftable(sweep(addmargins(mB, 1, list(list(All = sum, N = sqsm))), 2, + apply(mB,2,sum)/100, "/")), 1) Bee Buzz Hum Total Sea Black 22.4 29.7 26.6 Dead 28.9 25.7 27.1 Red 30.3 17.8 23.2 White 18.4 26.7 23.2 All 100.0 100.0 100.0 N 76.0 101.0 177.0 > > ## Zero.Printing table+margins: > set.seed(1) > x <- sample( 1:7, 20, replace=TRUE) > y <- sample( 1:7, 20, replace=TRUE) > tx <- addmargins( table(x, y) ) > print(tx, zero.print = ".") y x 1 2 3 4 5 6 7 Sum 1 . . 1 . . . . 1 2 . 1 . 1 1 . 1 4 3 . 2 . . . 1 . 3 4 . . . . 1 . . 1 5 . . 1 1 1 . 1 4 6 . . 1 . . 2 . 3 7 3 . 1 . . . . 4 Sum 3 3 4 2 3 3 2 20 > > > > cleanEx() > nameEx("aggregate") > ### * aggregate > > flush(stderr()); flush(stdout()) > > ### Name: aggregate > ### Title: Compute Summary Statistics of Data Subsets > ### Aliases: aggregate aggregate.default aggregate.data.frame > ### aggregate.formula aggregate.ts > ### Keywords: category array > > ### ** Examples > > ## Compute the averages for the variables in 'state.x77', grouped > ## according to the region (Northeast, South, North Central, West) that > ## each state belongs to. > aggregate(state.x77, list(Region = state.region), mean) Region Population Income Illiteracy Life Exp Murder HS Grad 1 Northeast 5495.111 4570.222 1.000000 71.26444 4.722222 53.96667 2 South 4208.125 4011.938 1.737500 69.70625 10.581250 44.34375 3 North Central 4803.000 4611.083 0.700000 71.76667 5.275000 54.51667 4 West 2915.308 4702.615 1.023077 71.23462 7.215385 62.00000 Frost Area 1 132.7778 18141.00 2 64.6250 54605.12 3 138.8333 62652.00 4 102.1538 134463.00 > > ## Compute the averages according to region and the occurrence of more > ## than 130 days of frost. > aggregate(state.x77, + list(Region = state.region, + Cold = state.x77[,"Frost"] > 130), + mean) Region Cold Population Income Illiteracy Life Exp Murder 1 Northeast FALSE 8802.8000 4780.400 1.1800000 71.12800 5.580000 2 South FALSE 4208.1250 4011.938 1.7375000 69.70625 10.581250 3 North Central FALSE 7233.8333 4633.333 0.7833333 70.95667 8.283333 4 West FALSE 4582.5714 4550.143 1.2571429 71.70000 6.828571 5 Northeast TRUE 1360.5000 4307.500 0.7750000 71.43500 3.650000 6 North Central TRUE 2372.1667 4588.833 0.6166667 72.57667 2.266667 7 West TRUE 970.1667 4880.500 0.7500000 70.69167 7.666667 HS Grad Frost Area 1 52.06000 110.6000 21838.60 2 44.34375 64.6250 54605.12 3 53.36667 120.0000 56736.50 4 60.11429 51.0000 91863.71 5 56.35000 160.5000 13519.00 6 55.66667 157.6667 68567.50 7 64.20000 161.8333 184162.17 > ## (Note that no state in 'South' is THAT cold.) > > > ## example with character variables and NAs > testDF <- data.frame(v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9), + v2 = c(11,33,55,77,88,33,55,NA,44,55,77,99) ) > by1 <- c("red","blue",1,2,NA,"big",1,2,"red",1,NA,12) > by2 <- c("wet","dry",99,95,NA,"damp",95,99,"red",99,NA,NA) > aggregate(x = testDF, by = list(by1, by2), FUN = "mean") Group.1 Group.2 v1 v2 1 1 95 5 55 2 2 95 7 77 3 1 99 5 55 4 2 99 NA NA 5 big damp 3 33 6 blue dry 3 33 7 red red 4 44 8 red wet 1 11 > > # and if you want to treat NAs as a group > fby1 <- factor(by1, exclude = "") > fby2 <- factor(by2, exclude = "") > aggregate(x = testDF, by = list(fby1, fby2), FUN = "mean") Group.1 Group.2 v1 v2 1 1 95 5.0 55.0 2 2 95 7.0 77.0 3 1 99 5.0 55.0 4 2 99 NA NA 5 big damp 3.0 33.0 6 blue dry 3.0 33.0 7 red red 4.0 44.0 8 red wet 1.0 11.0 9 12 9.0 99.0 10 7.5 82.5 > > > ## Formulas, one ~ one, one ~ many, many ~ one, and many ~ many: > aggregate(weight ~ feed, data = chickwts, mean) feed weight 1 casein 323.5833 2 horsebean 160.2000 3 linseed 218.7500 4 meatmeal 276.9091 5 soybean 246.4286 6 sunflower 328.9167 > aggregate(breaks ~ wool + tension, data = warpbreaks, mean) wool tension breaks 1 A L 44.55556 2 B L 28.22222 3 A M 24.00000 4 B M 28.77778 5 A H 24.55556 6 B H 18.77778 > aggregate(cbind(Ozone, Temp) ~ Month, data = airquality, mean) Month Ozone Temp 1 5 23.61538 66.73077 2 6 29.44444 78.22222 3 7 59.11538 83.88462 4 8 59.96154 83.96154 5 9 31.44828 76.89655 > aggregate(cbind(ncases, ncontrols) ~ alcgp + tobgp, data = esoph, sum) alcgp tobgp ncases ncontrols 1 0-39g/day 0-9g/day 9 261 2 40-79 0-9g/day 34 179 3 80-119 0-9g/day 19 61 4 120+ 0-9g/day 16 24 5 0-39g/day 10-19 10 84 6 40-79 10-19 17 85 7 80-119 10-19 19 49 8 120+ 10-19 12 18 9 0-39g/day 20-29 5 42 10 40-79 20-29 15 62 11 80-119 20-29 6 16 12 120+ 20-29 7 12 13 0-39g/day 30+ 5 28 14 40-79 30+ 9 29 15 80-119 30+ 7 12 16 120+ 30+ 10 13 > > ## Dot notation: > aggregate(. ~ Species, data = iris, mean) Species Sepal.Length Sepal.Width Petal.Length Petal.Width 1 setosa 5.006 3.428 1.462 0.246 2 versicolor 5.936 2.770 4.260 1.326 3 virginica 6.588 2.974 5.552 2.026 > aggregate(len ~ ., data = ToothGrowth, mean) supp dose len 1 OJ 0.5 13.23 2 VC 0.5 7.98 3 OJ 1.0 22.70 4 VC 1.0 16.77 5 OJ 2.0 26.06 6 VC 2.0 26.14 > > ## Often followed by xtabs(): > ag <- aggregate(len ~ ., data = ToothGrowth, mean) > xtabs(len ~ ., data = ag) dose supp 0.5 1 2 OJ 13.23 22.70 26.06 VC 7.98 16.77 26.14 > > > ## Compute the average annual approval ratings for American presidents. > aggregate(presidents, nfrequency = 1, FUN = mean) Time Series: Start = 1945 End = 1974 Frequency = 1 [1] NA 47.00 51.00 NA 58.50 41.75 28.75 NA 67.00 65.00 72.75 72.25 [13] 65.25 52.25 61.50 62.75 76.00 71.50 64.75 72.75 66.50 52.25 45.00 41.00 [25] 61.25 58.00 50.50 NA 44.75 25.25 > ## Give the summer less weight. > aggregate(presidents, nfrequency = 1, + FUN = weighted.mean, w = c(1, 1, 0.5, 1)) Time Series: Start = 1945 End = 1974 Frequency = 1 [1] NA 47.57143 50.57143 NA 58.71429 41.14286 28.28571 NA [9] 65.85714 64.14286 71.85714 73.00000 65.57143 52.85714 61.57143 63.00000 [17] 76.71429 72.85714 65.14286 73.28571 66.14286 51.71429 46.00000 41.85714 [25] 60.71429 57.57143 50.00000 NA 45.42857 25.42857 > > > > cleanEx() > nameEx("alias") > ### * alias > > flush(stderr()); flush(stdout()) > > ### Name: alias > ### Title: Find Aliases (Dependencies) in a Model > ### Aliases: alias alias.formula alias.lm > ### Keywords: models > > ### ** Examples > > ## From Venables and Ripley (2002) p.165. > utils::data(npk, package="MASS") > > op <- options(contrasts=c("contr.helmert", "contr.poly")) > npk.aov <- aov(yield ~ block + N*P*K, npk) > alias(npk.aov) Model : yield ~ block + N * P * K Complete : (Intercept) block1 block2 block3 block4 block5 N1 P1 K1 N1:P1 N1:P1:K1 0 1 1/3 1/6 -3/10 -1/5 0 0 0 0 N1:K1 P1:K1 N1:P1:K1 0 0 > options(op)# reset > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("anova.glm") > ### * anova.glm > > flush(stderr()); flush(stdout()) > > ### Name: anova.glm > ### Title: Analysis of Deviance for Generalized Linear Model Fits > ### Aliases: anova.glm anova.glmlist > ### Keywords: models regression > > ### ** Examples > > ## --- Continuing the Example from '?glm': > ## Don't show: > require(utils) > example("glm", echo = FALSE) treatment outcome counts 1 1 1 18 2 1 2 17 3 1 3 15 4 2 1 20 5 2 2 10 6 2 3 20 7 3 1 25 8 3 2 13 9 3 3 12 > ## End Don't show > anova(glm.D93) Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev NULL 8 10.5814 outcome 2 5.4523 6 5.1291 treatment 2 0.0000 4 5.1291 > anova(glm.D93, test = "Cp") Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev Cp NULL 8 10.5814 12.581 outcome 2 5.4523 6 5.1291 11.129 treatment 2 0.0000 4 5.1291 15.129 > anova(glm.D93, test = "Chisq") Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev Pr(>Chi) NULL 8 10.5814 outcome 2 5.4523 6 5.1291 0.06547 . treatment 2 0.0000 4 5.1291 1.00000 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > glm.D93a <- update(glm.D93, ~treatment*outcome) ## equivalent to Pearson Chi-square > anova(glm.D93, glm.D93a, test = "Rao") Analysis of Deviance Table Model 1: counts ~ outcome + treatment Model 2: counts ~ treatment + outcome + treatment:outcome Resid. Df Resid. Dev Df Deviance Rao Pr(>Chi) 1 4 5.1291 2 0 0.0000 4 5.1291 5.1732 0.27 > > > > cleanEx() > nameEx("anova.lm") > ### * anova.lm > > flush(stderr()); flush(stdout()) > > ### Name: anova.lm > ### Title: ANOVA for Linear Model Fits > ### Aliases: anova.lm anova.lmlist > ### Keywords: regression models > > ### ** Examples > > ## sequential table > fit <- lm(sr ~ ., data = LifeCycleSavings) > anova(fit) Analysis of Variance Table Response: sr Df Sum Sq Mean Sq F value Pr(>F) pop15 1 204.12 204.118 14.1157 0.0004922 *** pop75 1 53.34 53.343 3.6889 0.0611255 . dpi 1 12.40 12.401 0.8576 0.3593551 ddpi 1 63.05 63.054 4.3605 0.0424711 * Residuals 45 650.71 14.460 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ## same effect via separate models > fit0 <- lm(sr ~ 1, data = LifeCycleSavings) > fit1 <- update(fit0, . ~ . + pop15) > fit2 <- update(fit1, . ~ . + pop75) > fit3 <- update(fit2, . ~ . + dpi) > fit4 <- update(fit3, . ~ . + ddpi) > anova(fit0, fit1, fit2, fit3, fit4, test="F") Analysis of Variance Table Model 1: sr ~ 1 Model 2: sr ~ pop15 Model 3: sr ~ pop15 + pop75 Model 4: sr ~ pop15 + pop75 + dpi Model 5: sr ~ pop15 + pop75 + dpi + ddpi Res.Df RSS Df Sum of Sq F Pr(>F) 1 49 983.63 2 48 779.51 1 204.118 14.1157 0.0004922 *** 3 47 726.17 1 53.343 3.6889 0.0611255 . 4 46 713.77 1 12.401 0.8576 0.3593551 5 45 650.71 1 63.054 4.3605 0.0424711 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(fit4, fit2, fit0, test="F") # unconventional order Analysis of Variance Table Model 1: sr ~ pop15 + pop75 + dpi + ddpi Model 2: sr ~ pop15 + pop75 Model 3: sr ~ 1 Res.Df RSS Df Sum of Sq F Pr(>F) 1 45 650.71 2 47 726.17 -2 -75.455 2.6090 0.0847088 . 3 49 983.63 -2 -257.460 8.9023 0.0005527 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > cleanEx() > nameEx("anova.mlm") > ### * anova.mlm > > flush(stderr()); flush(stdout()) > > ### Name: anova.mlm > ### Title: Comparisons between Multivariate Linear Models > ### Aliases: anova.mlm anova.mlmlist > ### Keywords: regression models multivariate > > ### ** Examples > > require(graphics) > utils::example(SSD) # Brings in the mlmfit and reacttime objects SSD> # Lifted from Baron+Li: SSD> # "Notes on the use of R for psychology experiments and questionnaires" SSD> # Maxwell and Delaney, p. 497 SSD> reacttime <- matrix(c( SSD+ 420, 420, 480, 480, 600, 780, SSD+ 420, 480, 480, 360, 480, 600, SSD+ 480, 480, 540, 660, 780, 780, SSD+ 420, 540, 540, 480, 780, 900, SSD+ 540, 660, 540, 480, 660, 720, SSD+ 360, 420, 360, 360, 480, 540, SSD+ 480, 480, 600, 540, 720, 840, SSD+ 480, 600, 660, 540, 720, 900, SSD+ 540, 600, 540, 480, 720, 780, SSD+ 480, 420, 540, 540, 660, 780), SSD+ ncol = 6, byrow = TRUE, SSD+ dimnames=list(subj=1:10, SSD+ cond=c("deg0NA", "deg4NA", "deg8NA", SSD+ "deg0NP", "deg4NP", "deg8NP"))) SSD> mlmfit <- lm(reacttime~1) SSD> SSD(mlmfit) $SSD cond cond deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP deg0NA 29160 30600 26640 23760 32400 25560 deg4NA 30600 66600 32400 7200 36000 30600 deg8NA 26640 32400 56160 41040 57600 69840 deg0NP 23760 7200 41040 70560 72000 63360 deg4NP 32400 36000 57600 72000 108000 100800 deg8NP 25560 30600 69840 63360 100800 122760 $call lm(formula = reacttime ~ 1) $df [1] 9 attr(,"class") [1] "SSD" SSD> estVar(mlmfit) cond cond deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP deg0NA 3240 3400 2960 2640 3600 2840 deg4NA 3400 7400 3600 800 4000 3400 deg8NA 2960 3600 6240 4560 6400 7760 deg0NP 2640 800 4560 7840 8000 7040 deg4NP 3600 4000 6400 8000 12000 11200 deg8NP 2840 3400 7760 7040 11200 13640 > > mlmfit0 <- update(mlmfit, ~0) > > ### Traditional tests of intrasubj. contrasts > ## Using MANOVA techniques on contrasts: > anova(mlmfit, mlmfit0, X=~1) Analysis of Variance Table Model 1: reacttime ~ 1 Model 2: reacttime ~ 1 - 1 Contrasts orthogonal to ~1 Res.Df Df Gen.var. Pillai approx F num Df den Df Pr(>F) 1 9 1249.6 2 10 1 2013.2 0.9456 17.381 5 5 0.003534 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ## Assuming sphericity > anova(mlmfit, mlmfit0, X=~1, test="Spherical") Analysis of Variance Table Model 1: reacttime ~ 1 Model 2: reacttime ~ 1 - 1 Contrasts orthogonal to ~1 Greenhouse-Geisser epsilon: 0.4855 Huynh-Feldt epsilon: 0.6778 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 9 1249.6 2 10 1 2013.2 38.028 5 45 4.4711e-15 2.532e-08 7.393e-11 > > > ### tests using intra-subject 3x2 design > idata <- data.frame(deg=gl(3,1,6,labels=c(0,4,8)), + noise=gl(2,3,6,labels=c("A","P"))) > > anova(mlmfit, mlmfit0, X = ~ deg + noise, + idata = idata, test = "Spherical") Analysis of Variance Table Model 1: reacttime ~ 1 Model 2: reacttime ~ 1 - 1 Contrasts orthogonal to ~deg + noise Greenhouse-Geisser epsilon: 0.904 Huynh-Feldt epsilon: 1.118 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 9 316.58 2 10 1 996.34 45.31 2 18 9.4241e-08 3.4539e-07 9.4241e-08 > anova(mlmfit, mlmfit0, M = ~ deg + noise, X = ~ noise, + idata = idata, test="Spherical" ) Analysis of Variance Table Model 1: reacttime ~ 1 Model 2: reacttime ~ 1 - 1 Contrasts orthogonal to ~noise Contrasts spanned by ~deg + noise Greenhouse-Geisser epsilon: 0.9616 Huynh-Feldt epsilon: 1.2176 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 9 1007.0 2 10 1 2703.2 40.719 2 18 2.0868e-07 3.4017e-07 2.0868e-07 > anova(mlmfit, mlmfit0, M = ~ deg + noise, X = ~ deg, + idata = idata, test="Spherical" ) Analysis of Variance Table Model 1: reacttime ~ 1 Model 2: reacttime ~ 1 - 1 Contrasts orthogonal to ~deg Contrasts spanned by ~deg + noise Greenhouse-Geisser epsilon: 1 Huynh-Feldt epsilon: 1 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 9 1410 2 10 1 6030 33.766 1 9 0.00025597 0.00025597 0.00025597 > > f <- factor(rep(1:2,5)) # bogus, just for illustration > mlmfit2 <- update(mlmfit, ~f) > anova(mlmfit2, mlmfit, mlmfit0, X = ~1, test = "Spherical") Analysis of Variance Table Model 1: reacttime ~ f Model 2: reacttime ~ 1 Model 3: reacttime ~ 1 - 1 Contrasts orthogonal to ~1 Greenhouse-Geisser epsilon: 0.4691 Huynh-Feldt epsilon: 0.6758 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 8 1337.3 2 9 1 1249.6 0.2743 5 40 0.92452 0.79608 0.86456 3 10 1 2013.2 34.9615 5 40 0.00000 0.00000 0.00000 > anova(mlmfit2, X = ~1, test = "Spherical") Analysis of Variance Table Contrasts orthogonal to ~1 Greenhouse-Geisser epsilon: 0.4691 Huynh-Feldt epsilon: 0.6758 Df F num Df den Df Pr(>F) G-G Pr H-F Pr (Intercept) 1 34.9615 5 40 0.00000 0.00000 0.00000 f 1 0.2743 5 40 0.92452 0.79608 0.86456 Residuals 8 > # one-model form, eqiv. to previous > > ### There seems to be a strong interaction in these data > plot(colMeans(reacttime)) > > > > cleanEx() > nameEx("ansari.test") > ### * ansari.test > > flush(stderr()); flush(stdout()) > > ### Name: ansari.test > ### Title: Ansari-Bradley Test > ### Aliases: ansari.test ansari.test.default ansari.test.formula > ### Keywords: htest > > ### ** Examples > > ## Hollander & Wolfe (1973, p. 86f): > ## Serum iron determination using Hyland control sera > ramsay <- c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99, + 101, 96, 97, 102, 107, 113, 116, 113, 110, 98) > jung.parekh <- c(107, 108, 106, 98, 105, 103, 110, 105, 104, + 100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99) > ansari.test(ramsay, jung.parekh) Warning in ansari.test.default(ramsay, jung.parekh) : cannot compute exact p-value with ties Ansari-Bradley test data: ramsay and jung.parekh AB = 185.5, p-value = 0.1815 alternative hypothesis: true ratio of scales is not equal to 1 > > ansari.test(rnorm(10), rnorm(10, 0, 2), conf.int = TRUE) Ansari-Bradley test data: rnorm(10) and rnorm(10, 0, 2) AB = 69, p-value = 0.03831 alternative hypothesis: true ratio of scales is not equal to 1 95 percent confidence interval: 0.1852324 0.9712857 sample estimates: ratio of scales 0.4007458 > > ## try more points - failed in 2.4.1 > ansari.test(rnorm(100), rnorm(100, 0, 2), conf.int = TRUE) Ansari-Bradley test data: rnorm(100) and rnorm(100, 0, 2) AB = 6180, p-value = 3.347e-08 alternative hypothesis: true ratio of scales is not equal to 1 95 percent confidence interval: 0.3330596 0.5693532 sample estimates: ratio of scales 0.4346784 > > > > cleanEx() > nameEx("aov") > ### * aov > > flush(stderr()); flush(stdout()) > > ### Name: aov > ### Title: Fit an Analysis of Variance Model > ### Aliases: aov print.aov print.aovlist Error > ### Keywords: models regression > > ### ** Examples > > ## From Venables and Ripley (2002) p.165. > utils::data(npk, package="MASS") > > ## Set orthogonal contrasts. > op <- options(contrasts=c("contr.helmert", "contr.poly")) > ( npk.aov <- aov(yield ~ block + N*P*K, npk) ) Call: aov(formula = yield ~ block + N * P * K, data = npk) Terms: block N P K N:P N:K P:K Sum of Squares 343.2950 189.2817 8.4017 95.2017 21.2817 33.1350 0.4817 Deg. of Freedom 5 1 1 1 1 1 1 Residuals Sum of Squares 185.2867 Deg. of Freedom 12 Residual standard error: 3.929447 1 out of 13 effects not estimable Estimated effects are balanced > coefficients(npk.aov) (Intercept) block1 block2 block3 block4 block5 54.8750000 1.7125000 1.6791667 -1.8229167 -1.0137500 0.2950000 N1 P1 K1 N1:P1 N1:K1 P1:K1 2.8083333 -0.5916667 -1.9916667 -0.9416667 -1.1750000 0.1416667 > > ## to show the effects of re-ordering terms contrast the two fits > aov(yield ~ block + N * P + K, npk) Call: aov(formula = yield ~ block + N * P + K, data = npk) Terms: block N P K N:P Residuals Sum of Squares 343.2950 189.2817 8.4017 95.2017 21.2817 218.9033 Deg. of Freedom 5 1 1 1 1 14 Residual standard error: 3.954232 Estimated effects are balanced > aov(terms(yield ~ block + N * P + K, keep.order=TRUE), npk) Call: aov(formula = terms(yield ~ block + N * P + K, keep.order = TRUE), data = npk) Terms: block N P N:P K Residuals Sum of Squares 343.2950 189.2817 8.4017 21.2817 95.2017 218.9033 Deg. of Freedom 5 1 1 1 1 14 Residual standard error: 3.954232 Estimated effects are balanced > > > ## as a test, not particularly sensible statistically > npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) > npk.aovE Call: aov(formula = yield ~ N * P * K + Error(block), data = npk) Grand Mean: 54.875 Stratum 1: block Terms: N:P:K Residuals Sum of Squares 37.00167 306.29333 Deg. of Freedom 1 4 Residual standard error: 8.750619 Estimated effects are balanced Stratum 2: Within Terms: N P K N:P N:K P:K Sum of Squares 189.28167 8.40167 95.20167 21.28167 33.13500 0.48167 Deg. of Freedom 1 1 1 1 1 1 Residuals Sum of Squares 185.28667 Deg. of Freedom 12 Residual standard error: 3.929447 Estimated effects are balanced > summary(npk.aovE) Error: block Df Sum Sq Mean Sq F value Pr(>F) N:P:K 1 37.0 37.00 0.483 0.525 Residuals 4 306.3 76.57 Error: Within Df Sum Sq Mean Sq F value Pr(>F) N 1 189.28 189.28 12.259 0.00437 ** P 1 8.40 8.40 0.544 0.47490 K 1 95.20 95.20 6.166 0.02880 * N:P 1 21.28 21.28 1.378 0.26317 N:K 1 33.14 33.14 2.146 0.16865 P:K 1 0.48 0.48 0.031 0.86275 Residuals 12 185.29 15.44 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > options(op)# reset to previous > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("approxfun") > ### * approxfun > > flush(stderr()); flush(stdout()) > > ### Name: approxfun > ### Title: Interpolation Functions > ### Aliases: approx approxfun > ### Keywords: arith dplot > > ### ** Examples > > require(graphics) > > x <- 1:10 > y <- rnorm(10) > par(mfrow = c(2,1)) > plot(x, y, main = "approx(.) and approxfun(.)") > points(approx(x, y), col = 2, pch = "*") > points(approx(x, y, method = "constant"), col = 4, pch = "*") > > f <- approxfun(x, y) > curve(f(x), 0, 11, col = "green2") > points(x, y) > is.function(fc <- approxfun(x, y, method = "const")) # TRUE [1] TRUE > curve(fc(x), 0, 10, col = "darkblue", add = TRUE) > ## different extrapolation on left and right side : > plot(approxfun(x, y, rule = 2:1), 0, 11, + col = "tomato", add = TRUE, lty = 3, lwd = 2) > > ## Show treatment of 'ties' : > > x <- c(2,2:4,4,4,5,5,7,7,7) > y <- c(1:6, 5:4, 3:1) > approx(x,y, xout=x)$y # warning [1] 1.5 1.5 3.0 5.0 5.0 5.0 4.5 4.5 2.0 2.0 2.0 > (ay <- approx(x,y, xout=x, ties = "ordered")$y) [1] 2 2 3 6 6 6 4 4 1 1 1 > stopifnot(ay == c(2,2,3,6,6,6,4,4,1,1,1)) > approx(x,y, xout=x, ties = min)$y [1] 1 1 3 4 4 4 4 4 1 1 1 > approx(x,y, xout=x, ties = max)$y [1] 2 2 3 6 6 6 5 5 3 3 3 > > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("ar") > ### * ar > > flush(stderr()); flush(stdout()) > > ### Name: ar > ### Title: Fit Autoregressive Models to Time Series > ### Aliases: ar ar.burg ar.burg.default ar.burg.mts ar.yw ar.yw.default > ### ar.yw.mts ar.mle print.ar predict.ar > ### Keywords: ts > > ### ** Examples > > ar(lh) Call: ar(x = lh) Coefficients: 1 2 3 0.6534 -0.0636 -0.2269 Order selected 3 sigma^2 estimated as 0.1959 > ar(lh, method="burg") Call: ar(x = lh, method = "burg") Coefficients: 1 2 3 0.6588 -0.0608 -0.2234 Order selected 3 sigma^2 estimated as 0.1786 > ar(lh, method="ols") Call: ar(x = lh, method = "ols") Coefficients: 1 0.586 Intercept: 0.006234 (0.06551) Order selected 1 sigma^2 estimated as 0.2016 > ar(lh, FALSE, 4) # fit ar(4) Call: ar(x = lh, aic = FALSE, order.max = 4) Coefficients: 1 2 3 4 0.6767 -0.0571 -0.2941 0.1028 Order selected 4 sigma^2 estimated as 0.1983 > > (sunspot.ar <- ar(sunspot.year)) Call: ar(x = sunspot.year) Coefficients: 1 2 3 4 5 6 7 8 1.1305 -0.3524 -0.1745 0.1403 -0.1358 0.0963 -0.0556 0.0076 9 0.1941 Order selected 9 sigma^2 estimated as 267.5 > predict(sunspot.ar, n.ahead=25) $pred Time Series: Start = 1989 End = 2013 Frequency = 1 [1] 135.25933 148.09051 133.98476 106.61344 71.21921 40.84057 18.70100 [8] 11.52416 27.24208 56.99888 87.86705 107.62926 111.05437 98.05484 [15] 74.84085 48.80128 27.65441 18.15075 23.15355 40.04723 61.95906 [22] 80.79092 90.11420 87.44131 74.42284 $se Time Series: Start = 1989 End = 2013 Frequency = 1 [1] 16.35519 24.68467 28.95653 29.97401 30.07714 30.15629 30.35971 30.58793 [9] 30.71100 30.74276 31.42565 32.96467 34.48910 35.33601 35.51890 35.52034 [17] 35.65505 35.90628 36.07084 36.08139 36.16818 36.56324 37.16527 37.64820 [25] 37.83954 > ## try the other methods too > > ar(ts.union(BJsales, BJsales.lead)) Call: ar(x = ts.union(BJsales, BJsales.lead)) $ar , , 1 BJsales BJsales.lead BJsales 0.9499 0.8222 BJsales.lead 0.0276 0.4970 , , 2 BJsales BJsales.lead BJsales 0.02041 -1.133 BJsales.lead -0.02193 0.294 , , 3 BJsales BJsales.lead BJsales -0.186490 3.9415 BJsales.lead -0.002946 0.1264 $var.pred BJsales BJsales.lead BJsales 13.9431 0.7733 BJsales.lead 0.7733 0.1231 > ## Burg is quite different here, as is OLS (see ar.ols) > ar(ts.union(BJsales, BJsales.lead), method="burg") Call: ar(x = ts.union(BJsales, BJsales.lead), method = "burg") $ar , , 1 BJsales BJsales.lead BJsales 1.21197 0.07312 BJsales.lead 0.07411 0.45684 , , 2 BJsales BJsales.lead BJsales -0.26022 -0.1120 BJsales.lead -0.06904 0.3111 , , 3 BJsales BJsales.lead BJsales -0.01754 3.93591 BJsales.lead 0.01792 0.09632 , , 4 BJsales BJsales.lead BJsales -0.07746 -1.33836 BJsales.lead -0.01158 -0.09118 $var.pred BJsales BJsales.lead BJsales 0.38426 0.01315 BJsales.lead 0.01315 0.07657 > > > > cleanEx() > nameEx("ar.ols") > ### * ar.ols > > flush(stderr()); flush(stdout()) > > ### Name: ar.ols > ### Title: Fit Autoregressive Models to Time Series by OLS > ### Aliases: ar.ols > ### Keywords: ts > > ### ** Examples > > ar(lh, method="burg") Call: ar(x = lh, method = "burg") Coefficients: 1 2 3 0.6588 -0.0608 -0.2234 Order selected 3 sigma^2 estimated as 0.1786 > ar.ols(lh) Call: ar.ols(x = lh) Coefficients: 1 0.586 Intercept: 0.006234 (0.06551) Order selected 1 sigma^2 estimated as 0.2016 > ar.ols(lh, FALSE, 4) # fit ar(4) Call: ar.ols(x = lh, aic = FALSE, order.max = 4) Coefficients: 1 2 3 4 0.6761 -0.0571 -0.3001 0.0967 Intercept: 0.0004346 (0.06642) Order selected 4 sigma^2 estimated as 0.1924 > > ar.ols(ts.union(BJsales, BJsales.lead)) Call: ar.ols(x = ts.union(BJsales, BJsales.lead)) $ar , , 1 BJsales BJsales.lead BJsales 0.40408 -0.05144 BJsales.lead 0.07348 0.54524 , , 2 BJsales BJsales.lead BJsales 0.2367 0.05899 BJsales.lead -0.1224 0.28690 , , 3 BJsales BJsales.lead BJsales 0.09673 4.7413 BJsales.lead 0.10469 0.0701 , , 4 BJsales BJsales.lead BJsales 0.048758 1.5671 BJsales.lead 0.003482 -0.1838 , , 5 BJsales BJsales.lead BJsales 0.23307 -0.1574 BJsales.lead -0.02707 0.2623 , , 6 BJsales BJsales.lead BJsales 0.07494 -0.4313 BJsales.lead -0.08729 -0.2166 , , 7 BJsales BJsales.lead BJsales -0.09946 -0.6233 BJsales.lead 0.27123 -0.2584 , , 8 BJsales BJsales.lead BJsales 0.06725 -1.36469 BJsales.lead -0.21501 0.06115 , , 9 BJsales BJsales.lead BJsales -0.11531 -1.464 BJsales.lead 0.03536 0.270 , , 10 BJsales BJsales.lead BJsales -0.002037 -0.6472 BJsales.lead 0.001284 -1.0667 , , 11 BJsales BJsales.lead BJsales 0.004065 -0.7009 BJsales.lead 0.002098 0.4890 $x.intercept BJsales BJsales.lead 0.19630 -0.07022 $var.pred BJsales BJsales.lead BJsales 0.0400996 -0.0009311 BJsales.lead -0.0009311 0.0682137 > > x <- diff(log(EuStockMarkets)) > ar.ols(x, order.max=6, demean=FALSE, intercept=TRUE) Call: ar.ols(x = x, order.max = 6, demean = FALSE, intercept = TRUE) $ar , , 1 DAX SMI CAC FTSE DAX 0.004560 -0.095781 0.039975 0.04856 SMI -0.009204 -0.007142 0.037758 0.06826 CAC -0.026624 -0.113688 0.063807 0.09154 FTSE -0.010299 -0.089246 -0.003195 0.16409 $x.intercept DAX SMI CAC FTSE 0.0006941 0.0007813 0.0004866 0.0004388 $var.pred DAX SMI CAC FTSE DAX 1.056e-04 6.683e-05 8.274e-05 5.192e-05 SMI 6.683e-05 8.496e-05 6.252e-05 4.254e-05 CAC 8.274e-05 6.252e-05 1.207e-04 5.615e-05 FTSE 5.192e-05 4.254e-05 5.615e-05 6.224e-05 > > > > cleanEx() > nameEx("arima") > ### * arima > > flush(stderr()); flush(stdout()) > > ### Name: arima > ### Title: ARIMA Modelling of Time Series > ### Aliases: arima > ### Keywords: ts > > ### ** Examples > > arima(lh, order = c(1,0,0)) Call: arima(x = lh, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.5739 2.4133 s.e. 0.1161 0.1466 sigma^2 estimated as 0.1975: log likelihood = -29.38, aic = 64.76 > arima(lh, order = c(3,0,0)) Call: arima(x = lh, order = c(3, 0, 0)) Coefficients: ar1 ar2 ar3 intercept 0.6448 -0.0634 -0.2198 2.3931 s.e. 0.1394 0.1668 0.1421 0.0963 sigma^2 estimated as 0.1787: log likelihood = -27.09, aic = 64.18 > arima(lh, order = c(1,0,1)) Call: arima(x = lh, order = c(1, 0, 1)) Coefficients: ar1 ma1 intercept 0.4522 0.1982 2.4101 s.e. 0.1769 0.1705 0.1358 sigma^2 estimated as 0.1923: log likelihood = -28.76, aic = 65.52 > > arima(lh, order = c(3,0,0), method = "CSS") Call: arima(x = lh, order = c(3, 0, 0), method = "CSS") Coefficients: ar1 ar2 ar3 intercept 0.6578 -0.0658 -0.2348 2.3918 s.e. 0.1414 0.1702 0.1473 0.0983 sigma^2 estimated as 0.1905: part log likelihood = -28.31 > > arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1))) Call: arima(x = USAccDeaths, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1))) Coefficients: ma1 sma1 -0.4303 -0.5528 s.e. 0.1228 0.1784 sigma^2 estimated as 99347: log likelihood = -425.44, aic = 856.88 > arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)), + method = "CSS") # drops first 13 observations. Call: arima(x = USAccDeaths, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1)), method = "CSS") Coefficients: ma1 sma1 -0.3732 -0.4549 s.e. 0.1366 0.1436 sigma^2 estimated as 110330: part log likelihood = -426.25 > # for a model with as few years as this, we want full ML > > arima(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron)-1920) Call: arima(x = LakeHuron, order = c(2, 0, 0), xreg = time(LakeHuron) - 1920) Coefficients: ar1 ar2 intercept time(LakeHuron) - 1920 1.0048 -0.2913 579.0993 -0.0216 s.e. 0.0976 0.1004 0.2370 0.0081 sigma^2 estimated as 0.4566: log likelihood = -101.2, aic = 212.4 > > ## presidents contains NAs > ## graphs in example(acf) suggest order 1 or 3 > require(graphics) > (fit1 <- arima(presidents, c(1, 0, 0))) Call: arima(x = presidents, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.8242 56.1505 s.e. 0.0555 4.6434 sigma^2 estimated as 85.47: log likelihood = -416.89, aic = 839.78 > tsdiag(fit1) > (fit3 <- arima(presidents, c(3, 0, 0))) # smaller AIC Call: arima(x = presidents, order = c(3, 0, 0)) Coefficients: ar1 ar2 ar3 intercept 0.7496 0.2523 -0.1890 56.2223 s.e. 0.0936 0.1140 0.0946 4.2845 sigma^2 estimated as 81.12: log likelihood = -414.08, aic = 838.16 > tsdiag(fit3) > > > > cleanEx() > nameEx("arima.sim") > ### * arima.sim > > flush(stderr()); flush(stdout()) > > ### Name: arima.sim > ### Title: Simulate from an ARIMA Model > ### Aliases: arima.sim > ### Keywords: ts > > ### ** Examples > > require(graphics) > > arima.sim(n = 63, list(ar = c(0.8897, -0.4858), ma = c(-0.2279, 0.2488)), + sd = sqrt(0.1796)) Time Series: Start = 1 End = 63 Frequency = 1 [1] 0.55638574 0.24800500 -0.81744783 -0.38508849 -0.23890454 -0.02072714 [7] -0.51654590 -0.52651638 -0.14927781 0.60798558 0.48272646 0.45160066 [13] 0.09619832 -0.67131480 -0.69254708 -0.56224691 -0.19460368 0.53033791 [19] 0.77731125 0.40648873 -0.02697856 0.08101569 0.22706682 -0.10950180 [25] -0.38234797 -0.13676254 0.27995530 0.23211869 0.53580489 0.43571147 [31] -0.07756004 -0.03502285 -0.56957457 0.26261214 1.09213953 0.64829971 [37] -0.15198589 -0.14659775 -0.27894630 0.91394089 0.68582187 0.71550492 [43] 0.24452551 -0.37501019 -0.29768875 -0.94420587 0.11988146 0.29844068 [49] 1.26774646 0.99077203 0.14790214 0.02779843 -0.57682386 -0.90337297 [55] -0.37740926 -0.24511724 0.03927715 0.13868087 -0.15259447 -0.37935616 [61] -0.32790595 0.34491331 -0.30754547 > # mildly long-tailed > arima.sim(n = 63, list(ar=c(0.8897, -0.4858), ma=c(-0.2279, 0.2488)), + rand.gen = function(n, ...) sqrt(0.1796) * rt(n, df = 5)) Time Series: Start = 1 End = 63 Frequency = 1 [1] -0.08101433 -0.37596592 -1.05656365 -0.99217317 -0.15199924 -0.06140144 [7] -0.55852200 -0.54700937 -0.72098523 -0.98045523 -0.80880380 -0.61217798 [13] -0.66268921 -0.29302949 -0.83022714 -0.08803618 -0.37932440 0.10532621 [19] 0.08033289 -0.29269083 -0.69193397 0.81677306 -0.25402288 -0.08812258 [25] -0.34117754 0.29026870 -0.54848673 -0.44974248 -0.34110521 -0.17826307 [31] -0.35396760 0.98465366 0.21136827 0.05042017 -0.02400316 -0.42642295 [37] -0.61906692 1.92955621 -0.36050863 -0.68488280 0.10141464 0.64328982 [43] 0.31906603 0.17275054 -0.13570368 -0.13451166 -0.11717037 0.02330814 [49] 0.29918521 0.05938999 -0.20355761 -0.02439309 -1.14548572 -0.94045141 [55] 0.44845239 1.76898773 1.78579981 1.16734413 0.33858833 -0.41153063 [61] -0.31037109 -0.31929663 0.17496536 > > # An ARIMA simulation > ts.sim <- arima.sim(list(order = c(1,1,0), ar = 0.7), n = 200) > ts.plot(ts.sim) > > > > cleanEx() > nameEx("arima0") > ### * arima0 > > flush(stderr()); flush(stdout()) > > ### Name: arima0 > ### Title: ARIMA Modelling of Time Series - Preliminary Version > ### Aliases: arima0 print.arima0 predict.arima0 > ### Keywords: ts > > ### ** Examples > > ## Not run: arima0(lh, order = c(1,0,0)) > arima0(lh, order = c(3,0,0)) Call: arima0(x = lh, order = c(3, 0, 0)) Coefficients: ar1 ar2 ar3 intercept 0.6448 -0.0634 -0.2198 2.3931 s.e. 0.1394 0.1668 0.1421 0.0963 sigma^2 estimated as 0.1787: log likelihood = -27.09, aic = 64.18 > arima0(lh, order = c(1,0,1)) Call: arima0(x = lh, order = c(1, 0, 1)) Coefficients: ar1 ma1 intercept 0.4521 0.1983 2.4101 s.e. 0.1357 0.1777 0.1357 sigma^2 estimated as 0.1923: log likelihood = -28.76, aic = 65.52 > predict(arima0(lh, order = c(3,0,0)), n.ahead = 12) $pred Time Series: Start = 49 End = 60 Frequency = 1 [1] 2.460173 2.270829 2.198597 2.260696 2.346933 2.414479 2.438918 2.431440 [9] 2.410223 2.391645 2.382653 2.382697 $se Time Series: Start = 49 End = 60 Frequency = 1 [1] 0.4226823 0.5029332 0.5245256 0.5247161 0.5305499 0.5369159 0.5388045 [8] 0.5388448 0.5391043 0.5395174 0.5396991 0.5397140 > > arima0(lh, order = c(3,0,0), method = "CSS") Call: arima0(x = lh, order = c(3, 0, 0), method = "CSS") Coefficients: ar1 ar2 ar3 intercept 0.6580 -0.0660 -0.2339 2.3999 s.e. 0.1414 0.1702 0.1469 0.0981 sigma^2 estimated as 0.1905: part log likelihood = -28.31 > > # for a model with as few years as this, we want full ML > (fit <- arima0(USAccDeaths, order = c(0,1,1), + seasonal = list(order=c(0,1,1)), delta = -1)) Call: arima0(x = USAccDeaths, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1)), delta = -1) Coefficients: ma1 sma1 -0.4304 -0.5526 s.e. 0.1228 0.1785 sigma^2 estimated as 99355: log likelihood = -425.44, aic = 856.88 > predict(fit, n.ahead = 6) $pred Jan Feb Mar Apr May Jun 1979 8336.028 7531.760 8314.593 8616.864 9488.916 9859.727 $se Jan Feb Mar Apr May Jun 1979 315.4607 362.9949 404.9878 443.0180 478.0322 510.6511 > > arima0(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron)-1920) Call: arima0(x = LakeHuron, order = c(2, 0, 0), xreg = time(LakeHuron) - 1920) Coefficients: ar1 ar2 intercept xreg 1.0048 -0.2913 579.0985 -0.0216 s.e. 0.0976 0.1004 0.2370 0.0081 sigma^2 estimated as 0.4566: log likelihood = -101.2, aic = 212.4 > ## Not run: > ##D ## presidents contains NAs > ##D ## graphs in example(acf) suggest order 1 or 3 > ##D (fit1 <- arima0(presidents, c(1, 0, 0), delta = -1)) # avoid warning > ##D tsdiag(fit1) > ##D (fit3 <- arima0(presidents, c(3, 0, 0), delta = -1)) # smaller AIC > ##D tsdiag(fit3) > ## End(Not run) > > > > cleanEx() > nameEx("as.hclust") > ### * as.hclust > > flush(stderr()); flush(stdout()) > > ### Name: as.hclust > ### Title: Convert Objects to Class hclust > ### Aliases: as.hclust as.hclust.default as.hclust.twins > ### Keywords: multivariate cluster > > ### ** Examples > > x <- matrix(rnorm(30), ncol=3) > hc <- hclust(dist(x), method="complete") > > if(require("cluster", quietly=TRUE)) {# is a recommended package + ag <- agnes(x, method="complete") + hcag <- as.hclust(ag) + ## The dendrograms order slightly differently: + op <- par(mfrow=c(1,2)) + plot(hc) ; mtext("hclust", side=1) + plot(hcag); mtext("agnes", side=1) + } > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() detaching ‘package:cluster’ > nameEx("asOneSidedFormula") > ### * asOneSidedFormula > > flush(stderr()); flush(stdout()) > > ### Name: asOneSidedFormula > ### Title: Convert to One-Sided Formula > ### Aliases: asOneSidedFormula > ### Keywords: models > > ### ** Examples > > asOneSidedFormula("age") ~age > asOneSidedFormula(~ age) ~age > > > > cleanEx() > nameEx("ave") > ### * ave > > flush(stderr()); flush(stdout()) > > ### Name: ave > ### Title: Group Averages Over Level Combinations of Factors > ### Aliases: ave > ### Keywords: univar > > ### ** Examples > > require(graphics) > > ave(1:3)# no grouping -> grand mean [1] 2 2 2 > > attach(warpbreaks) > ave(breaks, wool) [1] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 [9] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 [17] 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 31.03704 [25] 31.03704 31.03704 31.03704 25.25926 25.25926 25.25926 25.25926 25.25926 [33] 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 [41] 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 [49] 25.25926 25.25926 25.25926 25.25926 25.25926 25.25926 > ave(breaks, tension) [1] 36.38889 36.38889 36.38889 36.38889 36.38889 36.38889 36.38889 36.38889 [9] 36.38889 26.38889 26.38889 26.38889 26.38889 26.38889 26.38889 26.38889 [17] 26.38889 26.38889 21.66667 21.66667 21.66667 21.66667 21.66667 21.66667 [25] 21.66667 21.66667 21.66667 36.38889 36.38889 36.38889 36.38889 36.38889 [33] 36.38889 36.38889 36.38889 36.38889 26.38889 26.38889 26.38889 26.38889 [41] 26.38889 26.38889 26.38889 26.38889 26.38889 21.66667 21.66667 21.66667 [49] 21.66667 21.66667 21.66667 21.66667 21.66667 21.66667 > ave(breaks, tension, FUN = function(x)mean(x, trim=.1)) [1] 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 [10] 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 [19] 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 [28] 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 35.6875 [37] 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 26.3125 [46] 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 21.0625 > plot(breaks, main = + "ave( Warpbreaks ) for wool x tension combinations") > lines(ave(breaks, wool, tension ), type='s', col = "blue") > lines(ave(breaks, wool, tension, FUN=median), type='s', col = "green") > legend(40,70, c("mean","median"), lty=1,col=c("blue","green"), bg="gray90") > detach() > > > > cleanEx() > nameEx("bandwidth") > ### * bandwidth > > flush(stderr()); flush(stdout()) > > ### Name: bandwidth > ### Title: Bandwidth Selectors for Kernel Density Estimation > ### Aliases: bw.nrd0 bw.nrd bw.ucv bw.bcv bw.SJ > ### Keywords: distribution smooth > > ### ** Examples > > require(graphics) > > plot(density(precip, n = 1000)) > rug(precip) > lines(density(precip, bw="nrd"), col = 2) > lines(density(precip, bw="ucv"), col = 3) > lines(density(precip, bw="bcv"), col = 4) Warning in bw.bcv(x) : minimum occurred at one end of the range > lines(density(precip, bw="SJ-ste"), col = 5) > lines(density(precip, bw="SJ-dpi"), col = 6) > legend(55, 0.035, + legend = c("nrd0", "nrd", "ucv", "bcv", "SJ-ste", "SJ-dpi"), + col = 1:6, lty = 1) > > > > cleanEx() > nameEx("bartlett.test") > ### * bartlett.test > > flush(stderr()); flush(stdout()) > > ### Name: bartlett.test > ### Title: Bartlett Test of Homogeneity of Variances > ### Aliases: bartlett.test bartlett.test.default bartlett.test.formula > ### Keywords: htest > > ### ** Examples > > require(graphics) > > plot(count ~ spray, data = InsectSprays) > bartlett.test(InsectSprays$count, InsectSprays$spray) Bartlett test of homogeneity of variances data: InsectSprays$count and InsectSprays$spray Bartlett's K-squared = 25.9598, df = 5, p-value = 9.085e-05 > bartlett.test(count ~ spray, data = InsectSprays) Bartlett test of homogeneity of variances data: count by spray Bartlett's K-squared = 25.9598, df = 5, p-value = 9.085e-05 > > > > cleanEx() > nameEx("binom.test") > ### * binom.test > > flush(stderr()); flush(stdout()) > > ### Name: binom.test > ### Title: Exact Binomial Test > ### Aliases: binom.test > ### Keywords: htest > > ### ** Examples > > ## Conover (1971), p. 97f. > ## Under (the assumption of) simple Mendelian inheritance, a cross > ## between plants of two particular genotypes produces progeny 1/4 of > ## which are "dwarf" and 3/4 of which are "giant", respectively. > ## In an experiment to determine if this assumption is reasonable, a > ## cross results in progeny having 243 dwarf and 682 giant plants. > ## If "giant" is taken as success, the null hypothesis is that p = > ## 3/4 and the alternative that p != 3/4. > binom.test(c(682, 243), p = 3/4) Exact binomial test data: c(682, 243) number of successes = 682, number of trials = 925, p-value = 0.3825 alternative hypothesis: true probability of success is not equal to 0.75 95 percent confidence interval: 0.7076683 0.7654066 sample estimates: probability of success 0.7372973 > binom.test(682, 682 + 243, p = 3/4) # The same. Exact binomial test data: 682 and 682 + 243 number of successes = 682, number of trials = 925, p-value = 0.3825 alternative hypothesis: true probability of success is not equal to 0.75 95 percent confidence interval: 0.7076683 0.7654066 sample estimates: probability of success 0.7372973 > ## => Data are in agreement with the null hypothesis. > > > > cleanEx() > nameEx("biplot.princomp") > ### * biplot.princomp > > flush(stderr()); flush(stdout()) > > ### Name: biplot.princomp > ### Title: Biplot for Principal Components > ### Aliases: biplot.princomp biplot.prcomp > ### Keywords: multivariate hplot > > ### ** Examples > > require(graphics) > biplot(princomp(USArrests)) > > > > cleanEx() > nameEx("birthday") > ### * birthday > > flush(stderr()); flush(stdout()) > > ### Name: birthday > ### Title: Probability of coincidences > ### Aliases: qbirthday pbirthday > ### Keywords: distribution > > ### ** Examples > > require(graphics) > > ## the standard version > qbirthday() # 23 [1] 23 > ## probability of > 2 people with the same birthday > pbirthday(23, coincident = 3) [1] 0.01441541 > > ## examples from Diaconis & Mosteller p. 858. > ## 'coincidence' is that husband, wife, daughter all born on the 16th > qbirthday(classes = 30, coincident = 3) # approximately 18 [1] 18 > qbirthday(coincident = 4) # exact value 187 [1] 187 > qbirthday(coincident = 10) # exact value 1181 [1] 1179 > > ## same 4-digit PIN number > qbirthday(classes = 10^4) [1] 119 > > ## 0.9 probability of three or more coincident birthdays > qbirthday(coincident = 3, prob = 0.9) [1] 135 > > ## Chance of 4 or more coincident birthdays in 150 people > pbirthday(150, coincident = 4) [1] 0.2690146 > > ## 100 or more coincident birthdays in 1000 people: very rare > pbirthday(1000, coincident = 100) [1] 1.531434e-113 > > > > cleanEx() > nameEx("box.test") > ### * box.test > > flush(stderr()); flush(stdout()) > > ### Name: Box.test > ### Title: Box-Pierce and Ljung-Box Tests > ### Aliases: Box.test > ### Keywords: ts > > ### ** Examples > > x <- rnorm (100) > Box.test (x, lag = 1) Box-Pierce test data: x X-squared = 0.0013, df = 1, p-value = 0.9709 > Box.test (x, lag = 1, type="Ljung") Box-Ljung test data: x X-squared = 0.0014, df = 1, p-value = 0.9704 > > > > cleanEx() > nameEx("cancor") > ### * cancor > > flush(stderr()); flush(stdout()) > > ### Name: cancor > ### Title: Canonical Correlations > ### Aliases: cancor > ### Keywords: multivariate > > ### ** Examples > > > cleanEx() > nameEx("case.names") > ### * case.names > > flush(stderr()); flush(stdout()) > > ### Name: case+variable.names > ### Title: Case and Variable Names of Fitted Models > ### Aliases: case.names case.names.lm variable.names variable.names.lm > ### Keywords: regression models > > ### ** Examples > > x <- 1:20 > y <- x + (x/4 - 2)^3 + rnorm(20, sd=3) > names(y) <- paste("O",x,sep=".") > ww <- rep(1,20); ww[13] <- 0 > summary(lmxy <- lm(y ~ x + I(x^2)+I(x^3) + I((x-10)^2), + weights = ww), cor = TRUE) Call: lm(formula = y ~ x + I(x^2) + I(x^3) + I((x - 10)^2), weights = ww) Weighted Residuals: Min 1Q Median 3Q Max -6.7160 -0.7047 0.0728 1.0174 3.9947 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -10.967768 3.104498 -3.533 0.003013 ** x 5.524412 1.255354 4.401 0.000516 *** I(x^2) -0.542292 0.138460 -3.917 0.001374 ** I(x^3) 0.020905 0.004372 4.782 0.000242 *** I((x - 10)^2) NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.838 on 15 degrees of freedom Multiple R-squared: 0.9702, Adjusted R-squared: 0.9643 F-statistic: 163 on 3 and 15 DF, p-value: 1.14e-11 Correlation of Coefficients: (Intercept) x I(x^2) x -0.90 I(x^2) 0.80 -0.97 I(x^3) -0.73 0.93 -0.99 > variable.names(lmxy) [1] "(Intercept)" "x" "I(x^2)" "I(x^3)" > variable.names(lmxy, full= TRUE)# includes the last [1] "(Intercept)" "x" "I(x^2)" "I(x^3)" [5] "I((x - 10)^2)" > case.names(lmxy) [1] "O.1" "O.2" "O.3" "O.4" "O.5" "O.6" "O.7" "O.8" "O.9" "O.10" [11] "O.11" "O.12" "O.14" "O.15" "O.16" "O.17" "O.18" "O.19" "O.20" > case.names(lmxy, full = TRUE)# includes the 0-weight case [1] "O.1" "O.2" "O.3" "O.4" "O.5" "O.6" "O.7" "O.8" "O.9" "O.10" [11] "O.11" "O.12" "O.13" "O.14" "O.15" "O.16" "O.17" "O.18" "O.19" "O.20" > > > > cleanEx() > nameEx("chisq.test") > ### * chisq.test > > flush(stderr()); flush(stdout()) > > ### Name: chisq.test > ### Title: Pearson's Chi-squared Test for Count Data > ### Aliases: chisq.test > ### Keywords: htest distribution > > ### ** Examples > > > ## From Agresti(2007) p.39 > M <- as.table(rbind(c(762, 327, 468), c(484,239,477))) > dimnames(M) <- list(gender=c("M","F"), + party=c("Democrat","Independent", "Republican")) > (Xsq <- chisq.test(M)) # Prints test summary Pearson's Chi-squared test data: M X-squared = 30.0701, df = 2, p-value = 2.954e-07 > Xsq$observed # observed counts (same as M) party gender Democrat Independent Republican M 762 327 468 F 484 239 477 > Xsq$expected # expected counts under the null party gender Democrat Independent Republican M 703.6714 319.6453 533.6834 F 542.3286 246.3547 411.3166 > Xsq$residuals # Pearson residuals party gender Democrat Independent Republican M 2.1988558 0.4113702 -2.8432397 F -2.5046695 -0.4685829 3.2386734 > Xsq$stdres # standardized residuals party gender Democrat Independent Republican M 4.5020535 0.6994517 -5.3159455 F -4.5020535 -0.6994517 5.3159455 > > > ## Effect of simulating p-values > x <- matrix(c(12, 5, 7, 7), ncol = 2) > chisq.test(x)$p.value # 0.4233 [1] 0.4233054 > chisq.test(x, simulate.p.value = TRUE, B = 10000)$p.value [1] 0.2935706 > # around 0.29! > > ## Testing for population probabilities > ## Case A. Tabulated data > x <- c(A = 20, B = 15, C = 25) > chisq.test(x) Chi-squared test for given probabilities data: x X-squared = 2.5, df = 2, p-value = 0.2865 > chisq.test(as.table(x)) # the same Chi-squared test for given probabilities data: as.table(x) X-squared = 2.5, df = 2, p-value = 0.2865 > x <- c(89,37,30,28,2) > p <- c(40,20,20,15,5) > try( + chisq.test(x, p = p) # gives an error + ) Error in chisq.test(x, p = p) : probabilities must sum to 1. > chisq.test(x, p = p, rescale.p = TRUE) Chi-squared test for given probabilities data: x X-squared = 9.9901, df = 4, p-value = 0.04059 > # works > p <- c(0.40,0.20,0.20,0.19,0.01) > # Expected count in category 5 > # is 1.86 < 5 ==> chi square approx. > chisq.test(x, p = p) # maybe doubtful, but is ok! Warning in chisq.test(x, p = p) : Chi-squared approximation may be incorrect Chi-squared test for given probabilities data: x X-squared = 5.7947, df = 4, p-value = 0.215 > chisq.test(x, p = p, simulate.p.value = TRUE) Chi-squared test for given probabilities with simulated p-value (based on 2000 replicates) data: x X-squared = 5.7947, df = NA, p-value = 0.2029 > > ## Case B. Raw data > x <- trunc(5 * runif(100)) > chisq.test(table(x)) # NOT 'chisq.test(x)'! Chi-squared test for given probabilities data: table(x) X-squared = 4.3, df = 4, p-value = 0.3669 > > > > cleanEx() > nameEx("cmdscale") > ### * cmdscale > > flush(stderr()); flush(stdout()) > > ### Name: cmdscale > ### Title: Classical (Metric) Multidimensional Scaling > ### Aliases: cmdscale > ### Keywords: multivariate > > ### ** Examples > > require(graphics) > > loc <- cmdscale(eurodist) > x <- loc[, 1] > y <- -loc[, 2] # reflect so North is at the top > ## note asp = 1, to ensure Euclidean distances are represented correctly > plot(x, y, type = "n", xlab = "", ylab = "", asp = 1, axes = FALSE, + main = "cmdscale(eurodist)") > text(x, y, rownames(loc), cex = 0.6) > > > > cleanEx() > nameEx("coef") > ### * coef > > flush(stderr()); flush(stdout()) > > ### Name: coef > ### Title: Extract Model Coefficients > ### Aliases: coef coefficients > ### Keywords: regression models > > ### ** Examples > > x <- 1:5; coef(lm(c(1:3,7,6) ~ x)) (Intercept) x -0.7 1.5 > > > > cleanEx() > nameEx("complete.cases") > ### * complete.cases > > flush(stderr()); flush(stdout()) > > ### Name: complete.cases > ### Title: Find Complete Cases > ### Aliases: complete.cases > ### Keywords: NA logic > > ### ** Examples > > x <- airquality[, -1] # x is a regression design matrix > y <- airquality[, 1] # y is the corresponding response > > stopifnot(complete.cases(y) != is.na(y)) > ok <- complete.cases(x,y) > sum(!ok) # how many are not "ok" ? [1] 42 > x <- x[ok,] > y <- y[ok] > > > > cleanEx() > nameEx("confint") > ### * confint > > flush(stderr()); flush(stdout()) > > ### Name: confint > ### Title: Confidence Intervals for Model Parameters > ### Aliases: confint confint.default > ### Keywords: models > > ### ** Examples > > fit <- lm(100/mpg ~ disp + hp + wt + am, data=mtcars) > confint(fit) 2.5 % 97.5 % (Intercept) -0.774822875 2.256118188 disp -0.002867999 0.008273849 hp -0.001400580 0.011949674 wt 0.380088737 1.622517536 am -0.614677730 0.926307310 > confint(fit, "wt") 2.5 % 97.5 % wt 0.3800887 1.622518 > > ## from example(glm) (needs MASS to be present on the system) > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9); treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > confint(glm.D93) Waiting for profiling to be done... 2.5 % 97.5 % (Intercept) 2.6958215 3.36655581 outcome2 -0.8577018 -0.06255840 outcome3 -0.6753696 0.08244089 treatment2 -0.3932548 0.39325483 treatment3 -0.3932548 0.39325483 > confint.default(glm.D93) # based on asymptotic normality 2.5 % 97.5 % (Intercept) 2.7095672 3.37947764 outcome2 -0.8505027 -0.05800787 outcome3 -0.6707552 0.08478093 treatment2 -0.3919928 0.39199279 treatment3 -0.3919928 0.39199279 > > > > cleanEx() > nameEx("constrOptim") > ### * constrOptim > > flush(stderr()); flush(stdout()) > > ### Name: constrOptim > ### Title: Linearly Constrained Optimization > ### Aliases: constrOptim > ### Keywords: optimize > > ### ** Examples > > > cleanEx() > nameEx("contrast") > ### * contrast > > flush(stderr()); flush(stdout()) > > ### Name: contrast > ### Title: (Possibly Sparse) Contrast Matrices > ### Aliases: contr.helmert contr.poly contr.sum contr.treatment contr.SAS > ### Keywords: design regression array > > ### ** Examples > > (cH <- contr.helmert(4)) [,1] [,2] [,3] 1 -1 -1 -1 2 1 -1 -1 3 0 2 -1 4 0 0 3 > apply(cH, 2,sum) # column sums are 0 [1] 0 0 0 > crossprod(cH) # diagonal -- columns are orthogonal [,1] [,2] [,3] [1,] 2 0 0 [2,] 0 6 0 [3,] 0 0 12 > contr.helmert(4, contrasts = FALSE) # just the 4 x 4 identity matrix 1 2 3 4 1 1 0 0 0 2 0 1 0 0 3 0 0 1 0 4 0 0 0 1 > > (cT <- contr.treatment(5)) 2 3 4 5 1 0 0 0 0 2 1 0 0 0 3 0 1 0 0 4 0 0 1 0 5 0 0 0 1 > all(crossprod(cT) == diag(4)) # TRUE: even orthonormal [1] TRUE > > (cT. <- contr.SAS(5)) 1 2 3 4 1 1 0 0 0 2 0 1 0 0 3 0 0 1 0 4 0 0 0 1 5 0 0 0 0 > all(crossprod(cT.) == diag(4)) # TRUE [1] TRUE > > zapsmall(cP <- contr.poly(3)) # Linear and Quadratic .L .Q [1,] -0.7071068 0.4082483 [2,] 0.0000000 -0.8164966 [3,] 0.7071068 0.4082483 > zapsmall(crossprod(cP), digits=15) # orthonormal up to fuzz .L .Q .L 1 0 .Q 0 1 > > > > cleanEx() > nameEx("contrasts") > ### * contrasts > > flush(stderr()); flush(stdout()) > > ### Name: contrasts > ### Title: Get and Set Contrast Matrices > ### Aliases: contrasts contrasts<- > ### Keywords: design regression > > ### ** Examples > > utils::example(factor) factor> (ff <- factor(substring("statistics", 1:10, 1:10), levels=letters)) [1] s t a t i s t i c s Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z factor> as.integer(ff) # the internal codes [1] 19 20 1 20 9 19 20 9 3 19 factor> (f. <- factor(ff))# drops the levels that do not occur [1] s t a t i s t i c s Levels: a c i s t factor> ff[, drop=TRUE] # the same, more transparently [1] s t a t i s t i c s Levels: a c i s t factor> factor(letters[1:20], labels="letter") [1] letter1 letter2 letter3 letter4 letter5 letter6 letter7 letter8 [9] letter9 letter10 letter11 letter12 letter13 letter14 letter15 letter16 [17] letter17 letter18 letter19 letter20 20 Levels: letter1 letter2 letter3 letter4 letter5 letter6 letter7 ... letter20 factor> class(ordered(4:1)) # "ordered", inheriting from "factor" [1] "ordered" "factor" factor> z <- factor(LETTERS[3:1], ordered = TRUE) factor> ## and "relational" methods work: factor> stopifnot(sort(z)[c(1,3)] == range(z), min(z) < max(z)) factor> ## Don't show: factor> of <- ordered(ff) factor> stopifnot(identical(range(of, rev(of)), of[3:2]), factor+ identical(max(of), of[2])) factor> ## End Don't show factor> factor> ## suppose you want "NA" as a level, and to allow missing values. factor> (x <- factor(c(1, 2, NA), exclude = NULL)) [1] 1 2 Levels: 1 2 factor> is.na(x)[2] <- TRUE factor> x # [1] 1 [1] 1 Levels: 1 2 factor> is.na(x) [1] FALSE TRUE FALSE factor> # [1] FALSE TRUE FALSE factor> factor> ## Using addNA() factor> Month <- airquality$Month factor> table(addNA(Month)) 5 6 7 8 9 31 30 31 31 30 0 factor> table(addNA(Month, ifany=TRUE)) 5 6 7 8 9 31 30 31 31 30 > fff <- ff[, drop=TRUE] # reduce to 5 levels. > contrasts(fff) # treatment contrasts by default c i s t a 0 0 0 0 c 1 0 0 0 i 0 1 0 0 s 0 0 1 0 t 0 0 0 1 > contrasts(C(fff, sum)) [,1] [,2] [,3] [,4] a 1 0 0 0 c 0 1 0 0 i 0 0 1 0 s 0 0 0 1 t -1 -1 -1 -1 > contrasts(fff, contrasts = FALSE) # the 5x5 identity matrix a c i s t a 1 0 0 0 0 c 0 1 0 0 0 i 0 0 1 0 0 s 0 0 0 1 0 t 0 0 0 0 1 > > contrasts(fff) <- contr.sum(5); contrasts(fff) # set sum contrasts [,1] [,2] [,3] [,4] a 1 0 0 0 c 0 1 0 0 i 0 0 1 0 s 0 0 0 1 t -1 -1 -1 -1 > contrasts(fff, 2) <- contr.sum(5); contrasts(fff) # set 2 contrasts [,1] [,2] a 1 0 c 0 1 i 0 0 s 0 0 t -1 -1 > # supply 2 contrasts, compute 2 more to make full set of 4. > contrasts(fff) <- contr.sum(5)[,1:2]; contrasts(fff) [,1] [,2] [,3] [,4] a 1 0 -0.2471257 0.2688164 c 0 1 -0.2471257 0.2688164 i 0 0 -0.1498721 -0.8817814 s 0 0 0.8912491 0.0753323 t -1 -1 -0.2471257 0.2688164 > > ## using sparse contrasts: % useful, once model.matrix() works with these : > ffs <- fff > contrasts(ffs) <- contr.sum(5, sparse=TRUE)[,1:2]; contrasts(ffs) [,1] [,2] [,3] [,4] a 1 0 -0.2471257 0.2688164 c 0 1 -0.2471257 0.2688164 i 0 0 -0.1498721 -0.8817814 s 0 0 0.8912491 0.0753323 t -1 -1 -0.2471257 0.2688164 > stopifnot(all.equal(ffs, fff)) > contrasts(ffs) <- contr.sum(5, sparse=TRUE); contrasts(ffs) 5 x 4 sparse Matrix of class "dgCMatrix" a 1 . . . c . 1 . . i . . 1 . s . . . 1 t -1 -1 -1 -1 > > > > cleanEx() > nameEx("convolve") > ### * convolve > > flush(stderr()); flush(stdout()) > > ### Name: convolve > ### Title: Convolution of Sequences via FFT > ### Aliases: convolve > ### Keywords: math dplot > > ### ** Examples > > require(graphics) > > x <- c(0,0,0,100,0,0,0) > y <- c(0,0,1, 2 ,1,0,0)/4 > zapsmall(convolve(x,y)) # *NOT* what you first thought. [1] 50 25 0 0 0 0 25 > zapsmall(convolve(x, y[3:5], type="f")) # rather [1] 0 25 50 25 0 > x <- rnorm(50) > y <- rnorm(50) > # Circular convolution *has* this symmetry: > all.equal(convolve(x,y, conj = FALSE), rev(convolve(rev(y),x))) [1] TRUE > > n <- length(x <- -20:24) > y <- (x-10)^2/1000 + rnorm(x)/8 > > Han <- function(y) # Hanning + convolve(y, c(1,2,1)/4, type = "filter") > > plot(x,y, main="Using convolve(.) for Hanning filters") > lines(x[-c(1 , n) ], Han(y), col="red") > lines(x[-c(1:2, (n-1):n)], Han(Han(y)), lwd=2, col="dark blue") > > > > cleanEx() > nameEx("cophenetic") > ### * cophenetic > > flush(stderr()); flush(stdout()) > > ### Name: cophenetic > ### Title: Cophenetic Distances for a Hierarchical Clustering > ### Aliases: cophenetic cophenetic.default cophenetic.dendrogram > ### Keywords: cluster multivariate > > ### ** Examples > > require(graphics) > > d1 <- dist(USArrests) > hc <- hclust(d1, "ave") > d2 <- cophenetic(hc) > cor(d1,d2) # 0.7659 [1] 0.7658983 > > ## Example from Sneath & Sokal, Fig. 5-29, p.279 > d0 <- c(1,3.8,4.4,5.1, 4,4.2,5, 2.6,5.3, 5.4) > attributes(d0) <- list(Size = 5, diag=TRUE) > class(d0) <- "dist" > names(d0) <- letters[1:5] > d0 1 2 3 4 2 1.0 3 3.8 4.0 4 4.4 4.2 2.6 5 5.1 5.0 5.3 5.4 > utils::str(upgma <- hclust(d0, method = "average")) List of 7 $ merge : int [1:4, 1:2] -1 -3 1 -5 -2 -4 2 3 $ height : num [1:4] 1 2.6 4.1 5.2 $ order : int [1:5] 5 1 2 3 4 $ labels : NULL $ method : chr "average" $ call : language hclust(d = d0, method = "average") $ dist.method: NULL - attr(*, "class")= chr "hclust" > plot(upgma, hang = -1) > # > (d.coph <- cophenetic(upgma)) 1 2 3 4 2 1.0 3 4.1 4.1 4 4.1 4.1 2.6 5 5.2 5.2 5.2 5.2 > cor(d0, d.coph) # 0.9911 [1] 0.9911351 > > > > cleanEx() > nameEx("cor") > ### * cor > > flush(stderr()); flush(stdout()) > > ### Name: cor > ### Title: Correlation, Variance and Covariance (Matrices) > ### Aliases: var cov cor cov2cor > ### Keywords: univar multivariate array > > ### ** Examples > > var(1:10)# 9.166667 [1] 9.166667 > > var(1:5,1:5)# 2.5 [1] 2.5 > > ## Two simple vectors > cor(1:10,2:11)# == 1 [1] 1 > > ## Correlation Matrix of Multivariate sample: > (Cl <- cor(longley)) GNP.deflator GNP Unemployed Armed.Forces Population GNP.deflator 1.0000000 0.9915892 0.6206334 0.4647442 0.9791634 GNP 0.9915892 1.0000000 0.6042609 0.4464368 0.9910901 Unemployed 0.6206334 0.6042609 1.0000000 -0.1774206 0.6865515 Armed.Forces 0.4647442 0.4464368 -0.1774206 1.0000000 0.3644163 Population 0.9791634 0.9910901 0.6865515 0.3644163 1.0000000 Year 0.9911492 0.9952735 0.6682566 0.4172451 0.9939528 Employed 0.9708985 0.9835516 0.5024981 0.4573074 0.9603906 Year Employed GNP.deflator 0.9911492 0.9708985 GNP 0.9952735 0.9835516 Unemployed 0.6682566 0.5024981 Armed.Forces 0.4172451 0.4573074 Population 0.9939528 0.9603906 Year 1.0000000 0.9713295 Employed 0.9713295 1.0000000 > ## Graphical Correlation Matrix: > symnum(Cl) # highly correlated GNP. GNP U A P Y E GNP.deflator 1 GNP B 1 Unemployed , , 1 Armed.Forces . . 1 Population B B , . 1 Year B B , . B 1 Employed B B . . B B 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > > ## Spearman's rho and Kendall's tau > symnum(clS <- cor(longley, method = "spearman")) GNP. GNP U A P Y E GNP.deflator 1 GNP B 1 Unemployed , , 1 Armed.Forces . 1 Population B B , 1 Year B B , 1 1 Employed B B . B B 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(clK <- cor(longley, method = "kendall")) GNP. GNP U A P Y E GNP.deflator 1 GNP B 1 Unemployed . . 1 Armed.Forces 1 Population B B . 1 Year B B . 1 1 Employed * * . + + 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > ## How much do they differ? > i <- lower.tri(Cl) > cor(cbind(P = Cl[i], S = clS[i], K = clK[i])) P S K P 1.0000000 0.9802390 0.9572562 S 0.9802390 1.0000000 0.9742171 K 0.9572562 0.9742171 1.0000000 > > > ## cov2cor() scales a covariance matrix by its diagonal > ## to become the correlation matrix. > cov2cor # see the function definition {and learn ..} function (V) { p <- (d <- dim(V))[1L] if (!is.numeric(V) || length(d) != 2L || p != d[2L]) stop("'V' is not a square numeric matrix") Is <- sqrt(1/diag(V)) if (any(!is.finite(Is))) warning("diag(.) had 0 or NA entries; non-finite result is doubtful") r <- V r[] <- Is * V * rep(Is, each = p) r[cbind(1L:p, 1L:p)] <- 1 r } > stopifnot(all.equal(Cl, cov2cor(cov(longley))), + all.equal(cor(longley, method="kendall"), + cov2cor(cov(longley, method="kendall")))) > > ##--- Missing value treatment: > C1 <- cov(swiss) > range(eigen(C1, only.values=TRUE)$values) # 6.19 1921 [1] 6.191249 1921.562488 > swM <- swiss > swM[1,2] <- swM[7,3] <- swM[25,5] <- NA # create 3 "missing" > try(cov(swM)) # Error: missing obs... Fertility Agriculture Examination Education Catholic Fertility 156.04250 NA NA -79.729510 NA Agriculture NA NA NA NA NA Examination NA NA NA NA NA Education -79.72951 NA NA 92.456059 NA Catholic NA NA NA NA NA Infant.Mortality 15.15619 NA NA -2.781684 NA Infant.Mortality Fertility 15.156193 Agriculture NA Examination NA Education -2.781684 Catholic NA Infant.Mortality 8.483802 > C2 <- cov(swM, use = "complete") > range(eigen(C2, only.values=TRUE)$values) # 6.46 1930 [1] 6.462385 1930.505982 > C3 <- cov(swM, use = "pairwise") > range(eigen(C3, only.values=TRUE)$values) # 6.19 1938 [1] 6.194469 1938.033663 > > symnum(cor(swM, method = "kendall", use = "complete")) F A Ex Ed C I Fertility 1 Agriculture 1 Examination . . 1 Education . . . 1 Catholic . 1 Infant.Mortality 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > ## Kendall's tau doesn't change much: > symnum(cor(swiss, method = "kendall")) F A Ex Ed C I Fertility 1 Agriculture 1 Examination . . 1 Education . . . 1 Catholic . 1 Infant.Mortality . 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > > > > cleanEx() > nameEx("cor.test") > ### * cor.test > > flush(stderr()); flush(stdout()) > > ### Name: cor.test > ### Title: Test for Association/Correlation Between Paired Samples > ### Aliases: cor.test cor.test.default cor.test.formula > ### Keywords: htest > > ### ** Examples > > ## Hollander & Wolfe (1973), p. 187f. > ## Assessment of tuna quality. We compare the Hunter L measure of > ## lightness to the averages of consumer panel scores (recoded as > ## integer values from 1 to 6 and averaged over 80 such values) in > ## 9 lots of canned tuna. > > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) > > ## The alternative hypothesis of interest is that the > ## Hunter L value is positively associated with the panel score. > > cor.test(x, y, method = "kendall", alternative = "greater") Kendall's rank correlation tau data: x and y T = 26, p-value = 0.05972 alternative hypothesis: true tau is greater than 0 sample estimates: tau 0.4444444 > ## => p=0.05972 > > cor.test(x, y, method = "kendall", alternative = "greater", + exact = FALSE) # using large sample approximation Kendall's rank correlation tau data: x and y z = 1.6681, p-value = 0.04765 alternative hypothesis: true tau is greater than 0 sample estimates: tau 0.4444444 > ## => p=0.04765 > > ## Compare this to > cor.test(x, y, method = "spearm", alternative = "g") Spearman's rank correlation rho data: x and y S = 48, p-value = 0.0484 alternative hypothesis: true rho is greater than 0 sample estimates: rho 0.6 > cor.test(x, y, alternative = "g") Pearson's product-moment correlation data: x and y t = 1.8411, df = 7, p-value = 0.05409 alternative hypothesis: true correlation is greater than 0 95 percent confidence interval: -0.02223023 1.00000000 sample estimates: cor 0.5711816 > > ## Formula interface. > require(graphics) > pairs(USJudgeRatings) > cor.test(~ CONT + INTG, data = USJudgeRatings) Pearson's product-moment correlation data: CONT and INTG t = -0.8605, df = 41, p-value = 0.3945 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.4168591 0.1741182 sample estimates: cor -0.1331909 > > > > cleanEx() > nameEx("cov.wt") > ### * cov.wt > > flush(stderr()); flush(stdout()) > > ### Name: cov.wt > ### Title: Weighted Covariance Matrices > ### Aliases: cov.wt > ### Keywords: multivariate > > ### ** Examples > > (xy <- cbind(x = 1:10, y = c(1:3, 8:5, 8:10))) x y [1,] 1 1 [2,] 2 2 [3,] 3 3 [4,] 4 8 [5,] 5 7 [6,] 6 6 [7,] 7 5 [8,] 8 8 [9,] 9 9 [10,] 10 10 > w1 <- c(0,0,0,1,1,1,1,1,0,0) > cov.wt(xy, wt = w1) # i.e. method = "unbiased" $cov x y x 2.5 -0.5 y -0.5 1.7 $center x y 6.0 6.8 $n.obs [1] 10 $wt [1] 0.0 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.0 0.0 > cov.wt(xy, wt = w1, method = "ML", cor = TRUE) $cov x y x 2.0 -0.40 y -0.4 1.36 $center x y 6.0 6.8 $n.obs [1] 10 $wt [1] 0.0 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.0 0.0 $cor x y x 1.0000000 -0.2425356 y -0.2425356 1.0000000 > > > > cleanEx() > nameEx("cpgram") > ### * cpgram > > flush(stderr()); flush(stdout()) > > ### Name: cpgram > ### Title: Plot Cumulative Periodogram > ### Aliases: cpgram > ### Keywords: ts hplot > > ### ** Examples > > require(graphics) > > par(pty = "s", mfrow = c(1,2)) > cpgram(lh) > lh.ar <- ar(lh, order.max = 9) > cpgram(lh.ar$resid, main = "AR(3) fit to lh") > > cpgram(ldeaths) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("cutree") > ### * cutree > > flush(stderr()); flush(stdout()) > > ### Name: cutree > ### Title: Cut a Tree into Groups of Data > ### Aliases: cutree > ### Keywords: multivariate cluster > > ### ** Examples > > hc <- hclust(dist(USArrests)) > > cutree(hc, k=1:5) #k = 1 is trivial 1 2 3 4 5 Alabama 1 1 1 1 1 Alaska 1 1 1 1 1 Arizona 1 1 1 1 1 Arkansas 1 2 2 2 2 California 1 1 1 1 1 Colorado 1 2 2 2 2 Connecticut 1 2 3 3 3 Delaware 1 1 1 1 1 Florida 1 1 1 4 4 Georgia 1 2 2 2 2 Hawaii 1 2 3 3 5 Idaho 1 2 3 3 3 Illinois 1 1 1 1 1 Indiana 1 2 3 3 3 Iowa 1 2 3 3 5 Kansas 1 2 3 3 3 Kentucky 1 2 3 3 3 Louisiana 1 1 1 1 1 Maine 1 2 3 3 5 Maryland 1 1 1 1 1 Massachusetts 1 2 2 2 2 Michigan 1 1 1 1 1 Minnesota 1 2 3 3 5 Mississippi 1 1 1 1 1 Missouri 1 2 2 2 2 Montana 1 2 3 3 3 Nebraska 1 2 3 3 3 Nevada 1 1 1 1 1 New Hampshire 1 2 3 3 5 New Jersey 1 2 2 2 2 New Mexico 1 1 1 1 1 New York 1 1 1 1 1 North Carolina 1 1 1 4 4 North Dakota 1 2 3 3 5 Ohio 1 2 3 3 3 Oklahoma 1 2 2 2 2 Oregon 1 2 2 2 2 Pennsylvania 1 2 3 3 3 Rhode Island 1 2 2 2 2 South Carolina 1 1 1 1 1 South Dakota 1 2 3 3 5 Tennessee 1 2 2 2 2 Texas 1 2 2 2 2 Utah 1 2 3 3 3 Vermont 1 2 3 3 5 Virginia 1 2 2 2 2 Washington 1 2 2 2 2 West Virginia 1 2 3 3 5 Wisconsin 1 2 3 3 5 Wyoming 1 2 2 2 2 > cutree(hc, h=250) Alabama Alaska Arizona Arkansas California 1 1 1 2 1 Colorado Connecticut Delaware Florida Georgia 2 2 1 1 2 Hawaii Idaho Illinois Indiana Iowa 2 2 1 2 2 Kansas Kentucky Louisiana Maine Maryland 2 2 1 2 1 Massachusetts Michigan Minnesota Mississippi Missouri 2 1 2 1 2 Montana Nebraska Nevada New Hampshire New Jersey 2 2 1 2 2 New Mexico New York North Carolina North Dakota Ohio 1 1 1 2 2 Oklahoma Oregon Pennsylvania Rhode Island South Carolina 2 2 2 2 1 South Dakota Tennessee Texas Utah Vermont 2 2 2 2 2 Virginia Washington West Virginia Wisconsin Wyoming 2 2 2 2 2 > > ## Compare the 2 and 4 grouping: > g24 <- cutree(hc, k = c(2,4)) > table(grp2=g24[,"2"], grp4=g24[,"4"]) grp4 grp2 1 2 3 4 1 14 0 0 2 2 0 14 20 0 > > > > cleanEx() > nameEx("decompose") > ### * decompose > > flush(stderr()); flush(stdout()) > > ### Name: decompose > ### Title: Classical Seasonal Decomposition by Moving Averages > ### Aliases: decompose plot.decomposed.ts > ### Keywords: ts > > ### ** Examples > > require(graphics) > > m <- decompose(co2) > m$figure [1] -0.05359649 0.61055921 1.37564693 2.51682018 3.00028509 2.32921053 [7] 0.81293860 -1.25052632 -3.05458333 -3.25194079 -2.06969298 -0.96512061 > plot(m) > > ## example taken from Kendall/Stuart > x <- c(-50, 175, 149, 214, 247, 237, 225, 329, 729, 809, + 530, 489, 540, 457, 195, 176, 337, 239, 128, 102, 232, 429, 3, + 98, 43, -141, -77, -13, 125, 361, -45, 184) > x <- ts(x, start = c(1951, 1), end = c(1958, 4), frequency = 4) > m <- decompose(x) > ## seasonal figure: 6.25, 8.62, -8.84, -6.03 > round(decompose(x)$figure / 10, 2) [1] 6.25 8.62 -8.84 -6.03 > > > > cleanEx() > nameEx("delete.response") > ### * delete.response > > flush(stderr()); flush(stdout()) > > ### Name: delete.response > ### Title: Modify Terms Objects > ### Aliases: reformulate drop.terms delete.response [.terms > ### Keywords: programming > > ### ** Examples > > ff <- y ~ z + x + w > tt <- terms(ff) > tt y ~ z + x + w attr(,"variables") list(y, z, x, w) attr(,"factors") z x w y 0 0 0 z 1 0 0 x 0 1 0 w 0 0 1 attr(,"term.labels") [1] "z" "x" "w" attr(,"order") [1] 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > delete.response(tt) ~z + x + w attr(,"variables") list(z, x, w) attr(,"factors") z x w z 1 0 0 x 0 1 0 w 0 0 1 attr(,"term.labels") [1] "z" "x" "w" attr(,"order") [1] 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 0 attr(,".Environment") > drop.terms(tt, 2:3, keep.response = TRUE) y ~ z attr(,"variables") list(y, z) attr(,"factors") z y 0 z 1 attr(,"term.labels") [1] "z" attr(,"order") [1] 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > tt[-1] y ~ x + w attr(,"variables") list(y, x, w) attr(,"factors") x w y 0 0 x 1 0 w 0 1 attr(,"term.labels") [1] "x" "w" attr(,"order") [1] 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > tt[2:3] y ~ x + w attr(,"variables") list(y, x, w) attr(,"factors") x w y 0 0 x 1 0 w 0 1 attr(,"term.labels") [1] "x" "w" attr(,"order") [1] 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > reformulate(attr(tt, "term.labels")) ~z + x + w > > ## keep LHS : > reformulate("x*w", ff[[2]]) y ~ x * w > fS <- surv(ft, case) ~ a + b > reformulate(c("a", "b*f"), fS[[2]]) surv(ft, case) ~ a + b * f > > stopifnot(identical( ~ var, reformulate("var")), + identical(~ a + b + c, reformulate(letters[1:3])), + identical( y ~ a + b, reformulate(letters[1:2], "y")) + ) > > > > cleanEx() > nameEx("dendrapply") > ### * dendrapply > > flush(stderr()); flush(stdout()) > > ### Name: dendrapply > ### Title: Apply a Function to All Nodes of a Dendrogram > ### Aliases: dendrapply > ### Keywords: iteration > > ### ** Examples > > require(graphics) > > ## a smallish simple dendrogram > dhc <- as.dendrogram(hc <- hclust(dist(USArrests), "ave")) > (dhc21 <- dhc[[2]][[1]]) 'dendrogram' with 2 branches and 14 members total, at height 44.83793 > > ## too simple: > dendrapply(dhc21, function(n) utils::str(attributes(n))) List of 4 $ members : int 14 $ midpoint: num 6.8 $ height : num 44.8 $ class : chr "dendrogram" List of 4 $ members : int 8 $ midpoint: num 3.34 $ height : num 26.7 $ class : chr "dendrogram" List of 4 $ members : int 5 $ midpoint: num 0.938 $ height : num 16.4 $ class : chr "dendrogram" List of 5 $ members: int 1 $ height : num 0 $ label : chr "Washington" $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 4 $ midpoint: num 0.875 $ height : num 12.9 $ class : chr "dendrogram" List of 5 $ members: int 1 $ height : num 0 $ label : chr "Oregon" $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 3 $ midpoint: num 0.75 $ height : num 10.7 $ class : chr "dendrogram" List of 5 $ members: int 1 $ height : num 0 $ label : chr "Wyoming" $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 2 $ midpoint: num 0.5 $ height : num 7.36 $ class : chr "dendrogram" List of 5 $ label : chr "Oklahoma" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 5 $ label : chr "Virginia" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 3 $ midpoint: num 0.75 $ height : num 22.6 $ class : chr "dendrogram" List of 5 $ members: int 1 $ height : num 0 $ label : chr "Rhode Island" $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 2 $ midpoint: num 0.5 $ height : num 11.5 $ class : chr "dendrogram" List of 5 $ label : chr "Massachusetts" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 5 $ label : chr "New Jersey" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 6 $ midpoint: num 2.25 $ height : num 29.1 $ class : chr "dendrogram" List of 4 $ members : int 3 $ midpoint: num 0.75 $ height : num 20.2 $ class : chr "dendrogram" List of 5 $ members: int 1 $ height : num 0 $ label : chr "Missouri" $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 2 $ midpoint: num 0.5 $ height : num 12.6 $ class : chr "dendrogram" List of 5 $ label : chr "Arkansas" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 5 $ label : chr "Tennessee" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 3 $ midpoint: num 0.75 $ height : num 24 $ class : chr "dendrogram" List of 5 $ members: int 1 $ height : num 0 $ label : chr "Georgia" $ leaf : logi TRUE $ class : chr "dendrogram" List of 4 $ members : int 2 $ midpoint: num 0.5 $ height : num 14.5 $ class : chr "dendrogram" List of 5 $ label : chr "Colorado" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" List of 5 $ label : chr "Texas" $ members: int 1 $ height : num 0 $ leaf : logi TRUE $ class : chr "dendrogram" [[1]] [[1]][[1]] [[1]][[1]][[1]] NULL [[1]][[1]][[2]] [[1]][[1]][[2]][[1]] NULL [[1]][[1]][[2]][[2]] [[1]][[1]][[2]][[2]][[1]] NULL [[1]][[1]][[2]][[2]][[2]] [[1]][[1]][[2]][[2]][[2]][[1]] NULL [[1]][[1]][[2]][[2]][[2]][[2]] NULL [[1]][[2]] [[1]][[2]][[1]] NULL [[1]][[2]][[2]] [[1]][[2]][[2]][[1]] NULL [[1]][[2]][[2]][[2]] NULL [[2]] [[2]][[1]] [[2]][[1]][[1]] NULL [[2]][[1]][[2]] [[2]][[1]][[2]][[1]] NULL [[2]][[1]][[2]][[2]] NULL [[2]][[2]] [[2]][[2]][[1]] NULL [[2]][[2]][[2]] [[2]][[2]][[2]][[1]] NULL [[2]][[2]][[2]][[2]] NULL > > ## toy example to set colored leaf labels : > local({ + colLab <<- function(n) { + if(is.leaf(n)) { + a <- attributes(n) + i <<- i+1 + attr(n, "nodePar") <- + c(a$nodePar, list(lab.col = mycols[i], lab.font= i%%3)) + } + n + } + mycols <- grDevices::rainbow(attr(dhc21,"members")) + i <- 0 + }) > dL <- dendrapply(dhc21, colLab) > op <- par(mfrow=2:1) > plot(dhc21) > plot(dL) ## --> colored labels! > par(op) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("dendrogram") > ### * dendrogram > > flush(stderr()); flush(stdout()) > > ### Name: dendrogram > ### Title: General Tree Structures > ### Aliases: dendrogram as.dendrogram as.dendrogram.dendrogram > ### as.dendrogram.hclust as.hclust.dendrogram cut.dendrogram > ### [[.dendrogram merge.dendrogram plot.dendrogram print.dendrogram > ### rev.dendrogram str.dendrogram is.leaf > ### Keywords: multivariate tree hplot > > ### ** Examples > > require(graphics); require(utils) > > hc <- hclust(dist(USArrests), "ave") > (dend1 <- as.dendrogram(hc)) # "print()" method 'dendrogram' with 2 branches and 50 members total, at height 152.314 > str(dend1) # "str()" method --[dendrogram w/ 2 branches and 50 members at h = 152] |--[dendrogram w/ 2 branches and 16 members at h = 77.6] | |--[dendrogram w/ 2 branches and 2 members at h = 38.5] | | |--leaf "Florida" | | `--leaf "North Carolina" | `--[dendrogram w/ 2 branches and 14 members at h = 44.3] | |--[dendrogram w/ 2 branches and 4 members at h = 28] | | |--leaf "California" | | `--[dendrogram w/ 2 branches and 3 members at h = 15.5] | | |--leaf "Maryland" | | `--[dendrogram w/ 2 branches and 2 members at h = 13.9] | | |--leaf "Arizona" | | `--leaf "New Mexico" | `--[dendrogram w/ 2 branches and 10 members at h = 39.4] | |--[dendrogram w/ 2 branches and 7 members at h = 26.4] | | |--[dendrogram w/ 2 branches and 3 members at h = 16.9] | | | |--leaf "Delaware" | | | `--[dendrogram w/ 2 branches and 2 members at h = 15.5] | | | |--leaf "Alabama" | | | `--leaf "Louisiana" | | `--[dendrogram w/ 2 branches and 4 members at h = 18.4] | | |--[dendrogram w/ 2 branches and 2 members at h = 6.24] | | | |--leaf "Illinois" | | | `--leaf "New York" | | `--[dendrogram w/ 2 branches and 2 members at h = 13.3] | | |--leaf "Michigan" | | `--leaf "Nevada" | `--[dendrogram w/ 2 branches and 3 members at h = 28.1] | |--leaf "Alaska" | `--[dendrogram w/ 2 branches and 2 members at h = 21.2] | |--leaf "Mississippi" | `--leaf "South Carolina" `--[dendrogram w/ 2 branches and 34 members at h = 89.2] |--[dendrogram w/ 2 branches and 14 members at h = 44.8] | |--[dendrogram w/ 2 branches and 8 members at h = 26.7] | | |--[dendrogram w/ 2 branches and 5 members at h = 16.4] | | | |--leaf "Washington" | | | `--[dendrogram w/ 2 branches and 4 members at h = 12.9] | | | |--leaf "Oregon" | | | `--[dendrogram w/ 2 branches and 3 members at h = 10.7] | | | |--leaf "Wyoming" | | | `--[dendrogram w/ 2 branches and 2 members at h = 7.36] | | | |--leaf "Oklahoma" | | | `--leaf "Virginia" | | `--[dendrogram w/ 2 branches and 3 members at h = 22.6] | | |--leaf "Rhode Island" | | `--[dendrogram w/ 2 branches and 2 members at h = 11.5] | | |--leaf "Massachusetts" | | `--leaf "New Jersey" | `--[dendrogram w/ 2 branches and 6 members at h = 29.1] | |--[dendrogram w/ 2 branches and 3 members at h = 20.2] | | |--leaf "Missouri" | | `--[dendrogram w/ 2 branches and 2 members at h = 12.6] | | |--leaf "Arkansas" | | `--leaf "Tennessee" | `--[dendrogram w/ 2 branches and 3 members at h = 24] | |--leaf "Georgia" | `--[dendrogram w/ 2 branches and 2 members at h = 14.5] | |--leaf "Colorado" | `--leaf "Texas" `--[dendrogram w/ 2 branches and 20 members at h = 54.7] |--[dendrogram w/ 2 branches and 10 members at h = 20.6] | |--[dendrogram w/ 2 branches and 4 members at h = 15] | | |--leaf "Idaho" | | `--[dendrogram w/ 2 branches and 3 members at h = 12.4] | | |--leaf "Nebraska" | | `--[dendrogram w/ 2 branches and 2 members at h = 3.83] | | |--leaf "Kentucky" | | `--leaf "Montana" | `--[dendrogram w/ 2 branches and 6 members at h = 15.1] | |--[dendrogram w/ 2 branches and 2 members at h = 6.64] | | |--leaf "Ohio" | | `--leaf "Utah" | `--[dendrogram w/ 2 branches and 4 members at h = 13.4] | |--[dendrogram w/ 2 branches and 2 members at h = 3.93] | | |--leaf "Indiana" | | `--leaf "Kansas" | `--[dendrogram w/ 2 branches and 2 members at h = 8.03] | |--leaf "Connecticut" | `--leaf "Pennsylvania" `--[dendrogram w/ 2 branches and 10 members at h = 41.1] |--leaf "Hawaii" `--[dendrogram w/ 2 branches and 9 members at h = 33.1] |--[dendrogram w/ 2 branches and 3 members at h = 10.8] | |--leaf "West Virginia" | `--[dendrogram w/ 2 branches and 2 members at h = 8.54] | |--leaf "Maine" | `--leaf "South Dakota" `--[dendrogram w/ 2 branches and 6 members at h = 27.8] |--[dendrogram w/ 2 branches and 2 members at h = 13] | |--leaf "North Dakota" | `--leaf "Vermont" `--[dendrogram w/ 2 branches and 4 members at h = 19] |--leaf "Minnesota" `--[dendrogram w/ 2 branches and 3 members at h = 10.2] |--leaf "Wisconsin" `--[dendrogram w/ 2 branches and 2 members at h = 2.29] |--leaf "Iowa" `--leaf "New Hampshire" > str(dend1, max = 2, last.str= "'") # only the first two sub-levels --[dendrogram w/ 2 branches and 50 members at h = 152] |--[dendrogram w/ 2 branches and 16 members at h = 77.6] | |--[dendrogram w/ 2 branches and 2 members at h = 38.5] .. | '--[dendrogram w/ 2 branches and 14 members at h = 44.3] .. '--[dendrogram w/ 2 branches and 34 members at h = 89.2] |--[dendrogram w/ 2 branches and 14 members at h = 44.8] .. '--[dendrogram w/ 2 branches and 20 members at h = 54.7] .. > oo <- options(str.dendrogram.last = "\\") # yet another possibility > str(dend1, max = 2) # only the first two sub-levels --[dendrogram w/ 2 branches and 50 members at h = 152] |--[dendrogram w/ 2 branches and 16 members at h = 77.6] | |--[dendrogram w/ 2 branches and 2 members at h = 38.5] .. | --[dendrogram w/ 2 branches and 14 members at h = 44.3] .. --[dendrogram w/ 2 branches and 34 members at h = 89.2] |--[dendrogram w/ 2 branches and 14 members at h = 44.8] .. --[dendrogram w/ 2 branches and 20 members at h = 54.7] .. > options(oo)# .. resetting them > > op <- par(mfrow= c(2,2), mar = c(5,2,1,4)) > plot(dend1) > ## "triangle" type and show inner nodes: > plot(dend1, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), + type = "t", center=TRUE) > plot(dend1, edgePar=list(col = 1:2, lty = 2:3), + dLeaf=1, edge.root = TRUE) > plot(dend1, nodePar=list(pch = 2:1,cex=.4*2:1, col = 2:3), + horiz=TRUE) > > ## simple test for as.hclust() as the inverse of as.dendrogram(): > stopifnot(identical(as.hclust(dend1)[1:4], hc[1:4])) > > dend2 <- cut(dend1, h=70) > plot(dend2$upper) > ## leaves are wrong horizontally: > plot(dend2$upper, nodePar=list(pch = c(1,7), col = 2:1)) > ## dend2$lower is *NOT* a dendrogram, but a list of .. : > plot(dend2$lower[[3]], nodePar=list(col=4), horiz = TRUE, type = "tr") > ## "inner" and "leaf" edges in different type & color : > plot(dend2$lower[[2]], nodePar=list(col=1),# non empty list + edgePar = list(lty=1:2, col=2:1), edge.root=TRUE) > par(op) > d3 <- dend2$lower[[2]][[2]][[1]] > stopifnot(identical(d3, dend2$lower[[2]][[c(2,1)]])) > str(d3, last.str="'") --[dendrogram w/ 2 branches and 7 members at h = 26.4] |--[dendrogram w/ 2 branches and 3 members at h = 16.9] | |--leaf "Delaware" | '--[dendrogram w/ 2 branches and 2 members at h = 15.5] | |--leaf "Alabama" | '--leaf "Louisiana" '--[dendrogram w/ 2 branches and 4 members at h = 18.4] |--[dendrogram w/ 2 branches and 2 members at h = 6.24] | |--leaf "Illinois" | '--leaf "New York" '--[dendrogram w/ 2 branches and 2 members at h = 13.3] |--leaf "Michigan" '--leaf "Nevada" > > ## merge() to join dendrograms: > (d13 <- merge(dend2$lower[[1]], dend2$lower[[3]])) 'dendrogram' with 2 branches and 16 members total, at height 49.32173 > ## merge() all parts back (using default 'height' instead of original one): > den.1 <- Reduce(merge, dend2$lower) > ## or merge() all four parts at same height --> 4 branches (!) > d. <- merge(dend2$lower[[1]], dend2$lower[[2]], dend2$lower[[3]], + dend2$lower[[4]]) > ## (with a warning) or the same using do.call : > stopifnot(identical(d., do.call(merge, dend2$lower))) > plot(d., main="merge(d1, d2, d3, d4) |-> dendrogram with a 4-split") > > ## "Zoom" in to the first dendrogram : > plot(dend1, xlim = c(1,20), ylim = c(1,50)) > > nP <- list(col=3:2, cex=c(2.0, 0.75), pch= 21:22, + bg= c("light blue", "pink"), + lab.cex = 0.75, lab.col = "tomato") > plot(d3, nodePar= nP, edgePar = list(col="gray", lwd=2), horiz = TRUE) > addE <- function(n) { + if(!is.leaf(n)) { + attr(n, "edgePar") <- list(p.col="plum") + attr(n, "edgetext") <- paste(attr(n,"members"),"members") + } + n + } > d3e <- dendrapply(d3, addE) > plot(d3e, nodePar= nP) > plot(d3e, nodePar= nP, leaflab = "textlike") > > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("density") > ### * density > > flush(stderr()); flush(stdout()) > > ### Name: density > ### Title: Kernel Density Estimation > ### Aliases: density density.default print.density > ### Keywords: distribution smooth > > ### ** Examples > > require(graphics) > > plot(density(c(-20,rep(0,98),20)), xlim = c(-4,4))# IQR = 0 > > # The Old Faithful geyser data > d <- density(faithful$eruptions, bw = "sj") > d Call: density.default(x = faithful$eruptions, bw = "sj") Data: faithful$eruptions (272 obs.); Bandwidth 'bw' = 0.14 x y Min. :1.180 Min. :0.0001834 1st Qu.:2.265 1st Qu.:0.0422638 Median :3.350 Median :0.1709243 Mean :3.350 Mean :0.2301726 3rd Qu.:4.435 3rd Qu.:0.4134348 Max. :5.520 Max. :0.5945634 > plot(d) > > plot(d, type = "n") > polygon(d, col = "wheat") > > ## Missing values: > x <- xx <- faithful$eruptions > x[i.out <- sample(length(x), 10)] <- NA > doR <- density(x, bw = 0.15, na.rm = TRUE) > lines(doR, col = "blue") > points(xx[i.out], rep(0.01, 10)) > > ## Weighted observations: > fe <- sort(faithful$eruptions) # has quite a few non-unique values > ## use 'counts / n' as weights: > dw <- density(unique(fe), weights = table(fe)/length(fe), bw = d$bw) > utils::str(dw) ## smaller n: only 126, but identical estimate: List of 7 $ x : num [1:512] 1.18 1.19 1.2 1.21 1.21 ... $ y : num [1:512] 0.000183 0.000223 0.00027 0.000328 0.000397 ... $ bw : num 0.14 $ n : int 126 $ call : language density.default(x = unique(fe), bw = d$bw, weights = table(fe)/length(fe)) $ data.name: chr "unique(fe)" $ has.na : logi FALSE - attr(*, "class")= chr "density" > stopifnot(all.equal(d[1:3], dw[1:3])) > > ## simulation from a density() fit: > # a kernel density fit is an equally-weighted mixture. > fit <- density(xx) > N <- 1e6 > x.new <- rnorm(N, sample(xx, size = N, replace = TRUE), fit$bw) > plot(fit) > lines(density(x.new), col="blue") > > > (kernels <- eval(formals(density.default)$kernel)) [1] "gaussian" "epanechnikov" "rectangular" "triangular" "biweight" [6] "cosine" "optcosine" > > ## show the kernels in the R parametrization > plot (density(0, bw = 1), xlab = "", + main="R's density() kernels with bw = 1") > for(i in 2:length(kernels)) + lines(density(0, bw = 1, kernel = kernels[i]), col = i) > legend(1.5,.4, legend = kernels, col = seq(kernels), + lty = 1, cex = .8, y.intersp = 1) > > ## show the kernels in the S parametrization > plot(density(0, from=-1.2, to=1.2, width=2, kernel="gaussian"), type="l", + ylim = c(0, 1), xlab="", main="R's density() kernels with width = 1") > for(i in 2:length(kernels)) + lines(density(0, width = 2, kernel = kernels[i]), col = i) > legend(0.6, 1.0, legend = kernels, col = seq(kernels), lty = 1) > > ##-------- Semi-advanced theoretic from here on ------------- > > (RKs <- cbind(sapply(kernels, + function(k) density(kernel = k, give.Rkern = TRUE)))) [,1] gaussian 0.2820948 epanechnikov 0.2683282 rectangular 0.2886751 triangular 0.2721655 biweight 0.2699746 cosine 0.2711340 optcosine 0.2684756 > 100*round(RKs["epanechnikov",]/RKs, 4) ## Efficiencies [,1] gaussian 95.12 epanechnikov 100.00 rectangular 92.95 triangular 98.59 biweight 99.39 cosine 98.97 optcosine 99.95 > > bw <- bw.SJ(precip) ## sensible automatic choice > plot(density(precip, bw = bw), + main = "same sd bandwidths, 7 different kernels") > for(i in 2:length(kernels)) + lines(density(precip, bw = bw, kernel = kernels[i]), col = i) > > ## Bandwidth Adjustment for "Exactly Equivalent Kernels" > h.f <- sapply(kernels, function(k)density(kernel = k, give.Rkern = TRUE)) > (h.f <- (h.f["gaussian"] / h.f)^ .2) gaussian epanechnikov rectangular triangular biweight cosine 1.0000000 1.0100567 0.9953989 1.0071923 1.0088217 1.0079575 optcosine 1.0099458 > ## -> 1, 1.01, .995, 1.007,... close to 1 => adjustment barely visible.. > > plot(density(precip, bw = bw), + main = "equivalent bandwidths, 7 different kernels") > for(i in 2:length(kernels)) + lines(density(precip, bw = bw, adjust = h.f[i], kernel = kernels[i]), + col = i) > legend(55, 0.035, legend = kernels, col = seq(kernels), lty = 1) > > > > cleanEx() > nameEx("deriv") > ### * deriv > > flush(stderr()); flush(stdout()) > > ### Name: deriv > ### Title: Symbolic and Algorithmic Derivatives of Simple Expressions > ### Aliases: D deriv deriv.default deriv.formula deriv3 deriv3.default > ### deriv3.formula > ### Keywords: math nonlinear > > ### ** Examples > > ## formula argument : > dx2x <- deriv(~ x^2, "x") ; dx2x expression({ .value <- x^2 .grad <- array(0, c(length(.value), 1L), list(NULL, c("x"))) .grad[, "x"] <- 2 * x attr(.value, "gradient") <- .grad .value }) > ## Not run: > ##D expression({ > ##D .value <- x^2 > ##D .grad <- array(0, c(length(.value), 1), list(NULL, c("x"))) > ##D .grad[, "x"] <- 2 * x > ##D attr(.value, "gradient") <- .grad > ##D .value > ##D }) > ## End(Not run) > mode(dx2x) [1] "expression" > x <- -1:2 > eval(dx2x) [1] 1 0 1 4 attr(,"gradient") x [1,] -2 [2,] 0 [3,] 2 [4,] 4 > > ## Something 'tougher': > trig.exp <- expression(sin(cos(x + y^2))) > ( D.sc <- D(trig.exp, "x") ) -(cos(cos(x + y^2)) * sin(x + y^2)) > all.equal(D(trig.exp[[1]], "x"), D.sc) [1] TRUE > > ( dxy <- deriv(trig.exp, c("x", "y")) ) expression({ .expr2 <- x + y^2 .expr3 <- cos(.expr2) .expr5 <- cos(.expr3) .expr6 <- sin(.expr2) .value <- sin(.expr3) .grad <- array(0, c(length(.value), 2L), list(NULL, c("x", "y"))) .grad[, "x"] <- -(.expr5 * .expr6) .grad[, "y"] <- -(.expr5 * (.expr6 * (2 * y))) attr(.value, "gradient") <- .grad .value }) > y <- 1 > eval(dxy) [1] 0.8414710 0.5143953 -0.4042392 -0.8360219 attr(,"gradient") x y [1,] 0.0000000 0.000000 [2,] -0.7216061 -1.443212 [3,] -0.8316919 -1.663384 [4,] -0.0774320 -0.154864 > eval(D.sc) [1] 0.0000000 -0.7216061 -0.8316919 -0.0774320 > > ## function returned: > deriv((y ~ sin(cos(x) * y)), c("x","y"), func = TRUE) function (x, y) { .expr1 <- cos(x) .expr2 <- .expr1 * y .expr4 <- cos(.expr2) .value <- sin(.expr2) .grad <- array(0, c(length(.value), 2L), list(NULL, c("x", "y"))) .grad[, "x"] <- -(.expr4 * (sin(x) * y)) .grad[, "y"] <- .expr4 * .expr1 attr(.value, "gradient") <- .grad .value } > > ## function with defaulted arguments: > (fx <- deriv(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"), + function(b0, b1, th, x = 1:7){} ) ) function (b0, b1, th, x = 1:7) { .expr3 <- 2^(-x/th) .value <- b0 + b1 * .expr3 .grad <- array(0, c(length(.value), 3L), list(NULL, c("b0", "b1", "th"))) .grad[, "b0"] <- 1 .grad[, "b1"] <- .expr3 .grad[, "th"] <- b1 * (.expr3 * (log(2) * (x/th^2))) attr(.value, "gradient") <- .grad .value } > fx(2,3,4) [1] 4.522689 4.121320 3.783811 3.500000 3.261345 3.060660 2.891905 attr(,"gradient") b0 b1 th [1,] 1 0.8408964 0.1092872 [2,] 1 0.7071068 0.1837984 [3,] 1 0.5946036 0.2318331 [4,] 1 0.5000000 0.2599302 [5,] 1 0.4204482 0.2732180 [6,] 1 0.3535534 0.2756976 [7,] 1 0.2973018 0.2704720 > > ## Higher derivatives > deriv3(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"), + c("b0", "b1", "th", "x") ) function (b0, b1, th, x) { .expr3 <- 2^(-x/th) .expr6 <- log(2) .expr7 <- th^2 .expr9 <- .expr6 * (x/.expr7) .expr10 <- .expr3 * .expr9 .value <- b0 + b1 * .expr3 .grad <- array(0, c(length(.value), 3L), list(NULL, c("b0", "b1", "th"))) .hessian <- array(0, c(length(.value), 3L, 3L), list(NULL, c("b0", "b1", "th"), c("b0", "b1", "th"))) .grad[, "b0"] <- 1 .grad[, "b1"] <- .expr3 .hessian[, "b1", "b1"] <- 0 .hessian[, "b1", "th"] <- .hessian[, "th", "b1"] <- .expr10 .grad[, "th"] <- b1 * .expr10 .hessian[, "th", "th"] <- b1 * (.expr10 * .expr9 - .expr3 * (.expr6 * (x * (2 * th)/.expr7^2))) attr(.value, "gradient") <- .grad attr(.value, "hessian") <- .hessian .value } > > ## Higher derivatives: > DD <- function(expr,name, order = 1) { + if(order < 1) stop("'order' must be >= 1") + if(order == 1) D(expr,name) + else DD(D(expr, name), name, order - 1) + } > DD(expression(sin(x^2)), "x", 3) -(sin(x^2) * (2 * x) * 2 + ((cos(x^2) * (2 * x) * (2 * x) + sin(x^2) * 2) * (2 * x) + sin(x^2) * (2 * x) * 2)) > ## showing the limits of the internal "simplify()" : > ## Not run: > ##D -sin(x^2) * (2 * x) * 2 + ((cos(x^2) * (2 * x) * (2 * x) + sin(x^2) * > ##D 2) * (2 * x) + sin(x^2) * (2 * x) * 2) > ## End(Not run) > > > > cleanEx() > nameEx("diffinv") > ### * diffinv > > flush(stderr()); flush(stdout()) > > ### Name: diffinv > ### Title: Discrete Integration: Inverse of Differencing > ### Aliases: diffinv diffinv.default diffinv.ts > ### Keywords: ts > > ### ** Examples > > s <- 1:10 > d <- diff(s) > diffinv(d, xi = 1) [1] 1 2 3 4 5 6 7 8 9 10 > > > > cleanEx() > nameEx("dist") > ### * dist > > flush(stderr()); flush(stdout()) > > ### Name: dist > ### Title: Distance Matrix Computation > ### Aliases: dist print.dist format.dist labels.dist as.matrix.dist as.dist > ### as.dist.default > ### Keywords: multivariate cluster > > ### ** Examples > > require(graphics) > > x <- matrix(rnorm(100), nrow=5) > dist(x) 1 2 3 4 2 5.701817 3 6.013119 5.032069 4 7.276905 5.325473 5.811861 5 6.619295 5.306750 4.945987 6.612081 > dist(x, diag = TRUE) 1 2 3 4 5 1 0.000000 2 5.701817 0.000000 3 6.013119 5.032069 0.000000 4 7.276905 5.325473 5.811861 0.000000 5 6.619295 5.306750 4.945987 6.612081 0.000000 > dist(x, upper = TRUE) 1 2 3 4 5 1 5.701817 6.013119 7.276905 6.619295 2 5.701817 5.032069 5.325473 5.306750 3 6.013119 5.032069 5.811861 4.945987 4 7.276905 5.325473 5.811861 6.612081 5 6.619295 5.306750 4.945987 6.612081 > m <- as.matrix(dist(x)) > d <- as.dist(m) > stopifnot(d == dist(x)) > > ## Use correlations between variables "as distance" > dd <- as.dist((1 - cor(USJudgeRatings))/2) > round(1000 * dd) # (prints more nicely) CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS INTG 567 DMNR 577 18 DILG 494 64 82 CFMG 432 93 93 21 DECI 457 99 98 22 9 PREP 494 61 72 11 21 21 FAMI 513 66 79 21 32 29 5 ORAL 506 44 47 23 25 26 8 9 WRIT 522 46 53 20 29 27 7 5 3 PHYS 473 129 106 94 60 64 76 78 54 72 RTEN 517 31 28 35 36 38 25 29 9 16 47 > plot(hclust(dd)) # to see a dendrogram of clustered variables > > ## example of binary and canberra distances. > x <- c(0, 0, 1, 1, 1, 1) > y <- c(1, 0, 1, 1, 0, 1) > dist(rbind(x,y), method= "binary") x y 0.4 > ## answer 0.4 = 2/5 > dist(rbind(x,y), method= "canberra") x y 2.4 > ## answer 2 * (6/5) > > ## To find the names > labels(eurodist) [1] "Athens" "Barcelona" "Brussels" "Calais" [5] "Cherbourg" "Cologne" "Copenhagen" "Geneva" [9] "Gibraltar" "Hamburg" "Hook of Holland" "Lisbon" [13] "Lyons" "Madrid" "Marseilles" "Milan" [17] "Munich" "Paris" "Rome" "Stockholm" [21] "Vienna" > > ## Examples involving "Inf" : > ## 1) > x[6] <- Inf > (m2 <- rbind(x,y)) [,1] [,2] [,3] [,4] [,5] [,6] x 0 0 1 1 1 Inf y 1 0 1 1 0 1 > dist(m2, method="binary")# warning, answer 0.5 = 2/4 Warning in dist(m2, method = "binary") : treating non-finite values as NA x y 0.5 > ## These all give "Inf": > stopifnot(Inf == dist(m2, method= "euclidean"), + Inf == dist(m2, method= "maximum"), + Inf == dist(m2, method= "manhattan")) > ## "Inf" is same as very large number: > x1 <- x; x1[6] <- 1e100 > stopifnot(dist(cbind(x ,y), method="canberra") == + print(dist(cbind(x1,y), method="canberra"))) 1 2 3 4 5 2 2 3 1 2 4 1 2 0 5 2 2 1 1 6 1 2 1 1 2 > > ## 2) > y[6] <- Inf #-> 6-th pair is excluded > dist(rbind(x,y), method="binary") # warning; 0.5 Warning in dist(rbind(x, y), method = "binary") : treating non-finite values as NA x y 0.5 > dist(rbind(x,y), method="canberra") # 3 x y 3 > dist(rbind(x,y), method="maximum") # 1 x y 1 > dist(rbind(x,y), method="manhattan")# 2.4 x y 2.4 > > > > cleanEx() > nameEx("dummy.coef") > ### * dummy.coef > > flush(stderr()); flush(stdout()) > > ### Name: dummy.coef > ### Title: Extract Coefficients in Original Coding > ### Aliases: dummy.coef dummy.coef.lm dummy.coef.aovlist > ### Keywords: models > > ### ** Examples > > options(contrasts=c("contr.helmert", "contr.poly")) > ## From Venables and Ripley (2002) p.165. > utils::data(npk, package="MASS") > npk.aov <- aov(yield ~ block + N*P*K, npk) > dummy.coef(npk.aov) Full coefficients are (Intercept): 54.875 block: 1 2 3 4 5 6 -0.850 2.575 5.900 -4.750 -4.350 1.475 N: 0 1 -2.808333 2.808333 P: 0 1 0.5916667 -0.5916667 K: 0 1 1.991667 -1.991667 N:P: 0:0 1:0 0:1 1:1 -0.9416667 0.9416667 0.9416667 -0.9416667 N:K: 0:0 1:0 0:1 1:1 -1.175 1.175 1.175 -1.175 P:K: 0:0 1:0 0:1 1:1 0.1416667 -0.1416667 -0.1416667 0.1416667 N:P:K: 0:0:0 1:0:0 0:1:0 1:1:0 0:0:1 1:0:1 0:1:1 0 0 0 0 0 0 0 (Intercept): block: N: P: K: N:P: N:K: P:K: N:P:K: 1:1:1 0 > > npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) > dummy.coef(npk.aovE) Error: (Intercept) (Intercept): 54.875 Error: block N:P:K: 0:0:0 1:0:0 0:1:0 1:1:0 0:0:1 1:0:1 0:1:1 -1.241667 1.241667 1.241667 -1.241667 1.241667 -1.241667 -1.241667 N:P:K: 1:1:1 1.241667 Error: Within N: 0 1 -2.808333 2.808333 P: 0 1 0.5916667 -0.5916667 K: 0 1 1.991667 -1.991667 N:P: 0:0 1:0 0:1 1:1 -0.9416667 0.9416667 0.9416667 -0.9416667 N:K: 0:0 1:0 0:1 1:1 -1.175 1.175 1.175 -1.175 P:K: 0:0 1:0 0:1 1:1 0.1416667 -0.1416667 -0.1416667 0.1416667 > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("ecdf") > ### * ecdf > > flush(stderr()); flush(stdout()) > > ### Name: ecdf > ### Title: Empirical Cumulative Distribution Function > ### Aliases: ecdf plot.ecdf print.ecdf summary.ecdf quantile.ecdf > ### Keywords: dplot hplot > > ### ** Examples > > ##-- Simple didactical ecdf example : > x <- rnorm(12) > Fn <- ecdf(x) > Fn # a *function* Empirical CDF Call: ecdf(x) x[1:12] = -0.83563, -0.82047, -0.62645, ..., 1.5118, 1.5953 > Fn(x) # returns the percentiles for x [1] 0.25000000 0.41666667 0.08333333 1.00000000 0.50000000 0.16666667 [7] 0.66666667 0.83333333 0.75000000 0.33333333 0.91666667 0.58333333 > tt <- seq(-2,2, by = 0.1) > 12 * Fn(tt) # Fn is a 'simple' function {with values k/12} [1] 0 0 0 0 0 0 0 0 0 0 0 0 2 2 3 3 3 4 4 4 4 4 5 5 7 [26] 8 9 9 10 10 10 10 10 10 10 10 12 12 12 12 12 > summary(Fn) Empirical CDF: 12 unique values with summary Min. 1st Qu. Median Mean 3rd Qu. Max. -0.8356 -0.3857 0.3597 0.2686 0.6164 1.5950 > ##--> see below for graphics > knots(Fn)# the unique data values {12 of them if there were no ties} [1] -0.8356286 -0.8204684 -0.6264538 -0.3053884 0.1836433 0.3295078 [7] 0.3898432 0.4874291 0.5757814 0.7383247 1.5117812 1.5952808 > > y <- round(rnorm(12),1); y[3] <- y[1] > Fn12 <- ecdf(y) > Fn12 Empirical CDF Call: ecdf(y) x[1:8] = -2.2, -2, -0.6, ..., 0.8, 0.9 > knots(Fn12)# unique values (always less than 12!) [1] -2.2 -2.0 -0.6 0.0 0.1 0.6 0.8 0.9 > summary(Fn12) Empirical CDF: 8 unique values with summary Min. 1st Qu. Median Mean 3rd Qu. Max. -2.20 -0.95 0.05 -0.30 0.65 0.90 > summary.stepfun(Fn12) Step function with continuity 'f'= 0 , 8 knots with summary Min. 1st Qu. Median Mean 3rd Qu. Max. -2.20 -0.95 0.05 -0.30 0.65 0.90 and 9 plateau levels (y) with summary Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.1667 0.5000 0.4630 0.6667 1.0000 > > ## Advanced: What's inside the function closure? > print(ls.Fn12 <- ls(environment(Fn12))) [1] "f" "method" "n" "nobs" "x" "y" "yleft" "yright" > ##[1] "f" "method" "n" "x" "y" "yleft" "yright" > utils::ls.str(environment(Fn12)) f : num 0 method : int 2 n : int 8 nobs : int 12 x : num [1:8] -2.2 -2 -0.6 0 0.1 0.6 0.8 0.9 y : num [1:8] 0.0833 0.1667 0.3333 0.5 0.5833 ... yleft : num 0 yright : num 1 > stopifnot(all.equal(quantile(Fn12), quantile(y))) > > ###----------------- Plotting -------------------------- > require(graphics) > > op <- par(mfrow=c(3,1), mgp=c(1.5, 0.8,0), mar= .1+c(3,3,2,1)) > > F10 <- ecdf(rnorm(10)) > summary(F10) Empirical CDF: 10 unique values with summary Min. 1st Qu. Median Mean 3rd Qu. Max. -1.47100 -0.14250 -0.05497 0.04667 0.41040 1.35900 > > plot(F10) > plot(F10, verticals= TRUE, do.points = FALSE) > > plot(Fn12 , lwd = 2) ; mtext("lwd = 2", adj=1) > xx <- unique(sort(c(seq(-3, 2, length=201), knots(Fn12)))) > lines(xx, Fn12(xx), col='blue') > abline(v=knots(Fn12),lty=2,col='gray70') > > plot(xx, Fn12(xx), type='o', cex=.1)#- plot.default {ugly} > plot(Fn12, col.hor='red', add= TRUE) #- plot method > abline(v=knots(Fn12),lty=2,col='gray70') > ## luxury plot > plot(Fn12, verticals=TRUE, col.points='blue', + col.hor='red', col.vert='bisque') > > ##-- this works too (automatic call to ecdf(.)): > plot.ecdf(rnorm(24)) > title("via simple plot.ecdf(x)", adj=1) > > par(op) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("eff.aovlist") > ### * eff.aovlist > > flush(stderr()); flush(stdout()) > > ### Name: eff.aovlist > ### Title: Compute Efficiencies of Multistratum Analysis of Variance > ### Aliases: eff.aovlist > ### Keywords: models > > ### ** Examples > > ## An example from Yates (1932), > ## a 2^3 design in 2 blocks replicated 4 times > > Block <- gl(8, 4) > A <- factor(c(0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1, + 0,1,0,1,0,1,0,1,0,1,0,1)) > B <- factor(c(0,0,1,1,0,0,1,1,0,1,0,1,1,0,1,0,0,0,1,1, + 0,0,1,1,0,0,1,1,0,0,1,1)) > C <- factor(c(0,1,1,0,1,0,0,1,0,0,1,1,0,0,1,1,0,1,0,1, + 1,0,1,0,0,0,1,1,1,1,0,0)) > Yield <- c(101, 373, 398, 291, 312, 106, 265, 450, 106, 306, 324, 449, + 272, 89, 407, 338, 87, 324, 279, 471, 323, 128, 423, 334, + 131, 103, 445, 437, 324, 361, 302, 272) > aovdat <- data.frame(Block, A, B, C, Yield) > > old <- getOption("contrasts") > options(contrasts=c("contr.helmert", "contr.poly")) > (fit <- aov(Yield ~ A*B*C + Error(Block), data = aovdat)) Call: aov(formula = Yield ~ A * B * C + Error(Block), data = aovdat) Grand Mean: 291.5938 Stratum 1: Block Terms: A:B A:C B:C A:B:C Residuals Sum of Squares 780.1250 276.1250 2556.1250 112.5000 774.0938 Deg. of Freedom 1 1 1 1 3 Residual standard error: 16.06335 Estimated effects are balanced Stratum 2: Within Terms: A B C A:B A:C B:C Sum of Squares 3465.28 161170.03 278817.78 28.17 1802.67 11528.17 Deg. of Freedom 1 1 1 1 1 1 A:B:C Residuals Sum of Squares 45.37 5423.28 Deg. of Freedom 1 17 Residual standard error: 17.86103 Estimated effects are balanced > eff.aovlist(fit) A B C A:B A:C B:C A:B:C Block 0 0 0 0.25 0.25 0.25 0.25 Within 1 1 1 0.75 0.75 0.75 0.75 > options(contrasts = old) > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("effects") > ### * effects > > flush(stderr()); flush(stdout()) > > ### Name: effects > ### Title: Effects from Fitted Model > ### Aliases: effects effects.lm effects.glm > ### Keywords: models regression > > ### ** Examples > > y <- c(1:3,7,5) > x <- c(1:3,6:7) > ( ee <- effects(lm(y ~ x)) ) (Intercept) x -8.0498447 4.3655709 0.1483334 1.6144112 -1.2302295 attr(,"assign") [1] 0 1 attr(,"class") [1] "coef" > c( round(ee - effects(lm(y+10 ~ I(x-3.8))), 3) ) (Intercept) x 22.361 0.000 0.000 0.000 0.000 > # just the first is different > > > > cleanEx() > nameEx("embed") > ### * embed > > flush(stderr()); flush(stdout()) > > ### Name: embed > ### Title: Embedding a Time Series > ### Aliases: embed > ### Keywords: ts > > ### ** Examples > > x <- 1:10 > embed (x, 3) [,1] [,2] [,3] [1,] 3 2 1 [2,] 4 3 2 [3,] 5 4 3 [4,] 6 5 4 [5,] 7 6 5 [6,] 8 7 6 [7,] 9 8 7 [8,] 10 9 8 > > > > cleanEx() > nameEx("expand.model.frame") > ### * expand.model.frame > > flush(stderr()); flush(stdout()) > > ### Name: expand.model.frame > ### Title: Add new variables to a model frame > ### Aliases: expand.model.frame > ### Keywords: manip regression > > ### ** Examples > > model <- lm(log(Volume) ~ log(Girth) + log(Height), data=trees) > expand.model.frame(model, ~ Girth) # prints data.frame like log(Volume) log(Girth) log(Height) Girth 1 2.332144 2.116256 4.248495 8.3 2 2.332144 2.151762 4.174387 8.6 3 2.322388 2.174752 4.143135 8.8 4 2.797281 2.351375 4.276666 10.5 5 2.933857 2.370244 4.394449 10.7 6 2.980619 2.379546 4.418841 10.8 7 2.747271 2.397895 4.189655 11.0 8 2.901422 2.397895 4.317488 11.0 9 3.117950 2.406945 4.382027 11.1 10 2.990720 2.415914 4.317488 11.2 11 3.186353 2.424803 4.369448 11.3 12 3.044522 2.433613 4.330733 11.4 13 3.063391 2.433613 4.330733 11.4 14 3.058707 2.459589 4.234107 11.7 15 2.949688 2.484907 4.317488 12.0 16 3.100092 2.557227 4.304065 12.9 17 3.520461 2.557227 4.442651 12.9 18 3.310543 2.587764 4.454347 13.3 19 3.246491 2.617396 4.262680 13.7 20 3.214868 2.624669 4.158883 13.8 21 3.540959 2.639057 4.356709 14.0 22 3.456317 2.653242 4.382027 14.2 23 3.591818 2.674149 4.304065 14.5 24 3.645450 2.772589 4.276666 16.0 25 3.751854 2.791165 4.343805 16.3 26 4.014580 2.850707 4.394449 17.3 27 4.019980 2.862201 4.406719 17.5 28 4.065602 2.884801 4.382027 17.9 29 3.941582 2.890372 4.382027 18.0 30 3.931826 2.890372 4.382027 18.0 31 4.343805 3.025291 4.465908 20.6 > > dd <- data.frame(x=1:5, y=rnorm(5), z=c(1,2,NA,4,5)) > model <- glm(y ~ x, data=dd, subset=1:4, na.action=na.omit) > expand.model.frame(model, "z", na.expand=FALSE) # = default y x z 1 -0.6264538 1 1 2 0.1836433 2 2 4 1.5952808 4 4 > expand.model.frame(model, "z", na.expand=TRUE) y x z 1 -0.6264538 1 1 2 0.1836433 2 2 3 -0.8356286 3 NA 4 1.5952808 4 4 > > > > cleanEx() > nameEx("extractAIC") > ### * extractAIC > > flush(stderr()); flush(stdout()) > > ### Name: extractAIC > ### Title: Extract AIC from a Fitted Model > ### Aliases: extractAIC > ### Keywords: models > > ### ** Examples > > > cleanEx() > nameEx("factanal") > ### * factanal > > flush(stderr()); flush(stdout()) > > ### Name: factanal > ### Title: Factor Analysis > ### Aliases: factanal > ### Keywords: multivariate > > ### ** Examples > > # A little demonstration, v2 is just v1 with noise, > # and same for v4 vs. v3 and v6 vs. v5 > # Last four cases are there to add noise > # and introduce a positive manifold (g factor) > v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6) > v2 <- c(1,2,1,1,1,1,2,1,2,1,3,4,3,3,3,4,6,5) > v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) > v4 <- c(3,3,4,3,3,1,1,2,1,1,1,1,2,1,1,5,6,4) > v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) > v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) > m1 <- cbind(v1,v2,v3,v4,v5,v6) > cor(m1) v1 v2 v3 v4 v5 v6 v1 1.0000000 0.9393083 0.5128866 0.4320310 0.4664948 0.4086076 v2 0.9393083 1.0000000 0.4124441 0.4084281 0.4363925 0.4326113 v3 0.5128866 0.4124441 1.0000000 0.8770750 0.5128866 0.4320310 v4 0.4320310 0.4084281 0.8770750 1.0000000 0.4320310 0.4323259 v5 0.4664948 0.4363925 0.5128866 0.4320310 1.0000000 0.9473451 v6 0.4086076 0.4326113 0.4320310 0.4323259 0.9473451 1.0000000 > factanal(m1, factors = 3) # varimax is the default Call: factanal(x = m1, factors = 3) Uniquenesses: v1 v2 v3 v4 v5 v6 0.005 0.101 0.005 0.224 0.084 0.005 Loadings: Factor1 Factor2 Factor3 v1 0.944 0.182 0.267 v2 0.905 0.235 0.159 v3 0.236 0.210 0.946 v4 0.180 0.242 0.828 v5 0.242 0.881 0.286 v6 0.193 0.959 0.196 Factor1 Factor2 Factor3 SS loadings 1.893 1.886 1.797 Proportion Var 0.316 0.314 0.300 Cumulative Var 0.316 0.630 0.929 The degrees of freedom for the model is 0 and the fit was 0.4755 > # The following shows the g factor as PC1 > > ## formula interface > factanal(~v1+v2+v3+v4+v5+v6, factors = 3, + scores = "Bartlett")$scores Factor1 Factor2 Factor3 1 -0.9039949 -0.9308984 0.9475392 2 -0.8685952 -0.9328721 0.9352330 3 -0.9082818 -0.9320093 0.9616422 4 -1.0021975 -0.2529689 0.8178552 5 -0.9039949 -0.9308984 0.9475392 6 -0.7452711 0.7273960 -0.7884733 7 -0.7098714 0.7254223 -0.8007795 8 -0.7495580 0.7262851 -0.7743704 9 -0.8080740 1.4033517 -0.9304636 10 -0.7452711 0.7273960 -0.7884733 11 0.9272282 -0.9307506 -0.8371538 12 0.9626279 -0.9327243 -0.8494600 13 0.9229413 -0.9318615 -0.8230509 14 0.8290256 -0.2528211 -0.9668378 15 0.9272282 -0.9307506 -0.8371538 16 0.4224366 2.0453079 1.2864761 17 1.4713902 1.2947716 0.5451562 18 1.8822320 0.3086244 1.9547752 > > > > cleanEx() > nameEx("factor.scope") > ### * factor.scope > > flush(stderr()); flush(stdout()) > > ### Name: factor.scope > ### Title: Compute Allowed Changes in Adding to or Dropping from a Formula > ### Aliases: add.scope drop.scope factor.scope > ### Keywords: models > > ### ** Examples > > add.scope( ~ a + b + c + a:b, ~ (a + b + c)^3) [1] "a:c" "b:c" > # [1] "a:c" "b:c" > drop.scope( ~ a + b + c + a:b) [1] "c" "a:b" > # [1] "c" "a:b" > > > > cleanEx() > nameEx("family") > ### * family > > flush(stderr()); flush(stdout()) > > ### Name: family > ### Title: Family Objects for Models > ### Aliases: family binomial gaussian Gamma inverse.gaussian poisson quasi > ### quasibinomial quasipoisson print.family > ### Keywords: models > > ### ** Examples > > require(utils) # for str > > nf <- gaussian()# Normal family > nf Family: gaussian Link function: identity > str(nf)# internal STRucture List of 11 $ family : chr "gaussian" $ link : chr "identity" $ linkfun :function (mu) $ linkinv :function (eta) $ variance :function (mu) $ dev.resids:function (y, mu, wt) $ aic :function (y, n, mu, wt, dev) $ mu.eta :function (eta) $ initialize: expression({ n <- rep.int(1, nobs) if (is.null(etastart) && is.null(start) && is.null(mustart) && ((family$link == "inverse" && any(y == 0)) || (family$link == "log" && any(y <= 0)))) stop("cannot find valid starting values: please specify some") mustart <- y }) $ validmu :function (mu) $ valideta :function (eta) - attr(*, "class")= chr "family" > > gf <- Gamma() > gf Family: Gamma Link function: inverse > str(gf) List of 12 $ family : chr "Gamma" $ link : chr "inverse" $ linkfun :function (mu) $ linkinv :function (eta) $ variance :function (mu) $ dev.resids:function (y, mu, wt) $ aic :function (y, n, mu, wt, dev) $ mu.eta :function (eta) $ initialize: expression({ if (any(y <= 0)) stop("non-positive values not allowed for the gamma family") n <- rep.int(1, nobs) mustart <- y }) $ validmu :function (mu) $ valideta :function (eta) $ simulate :function (object, nsim) - attr(*, "class")= chr "family" > gf$linkinv function (eta) 1/eta > gf$variance(-3:4) #- == (.)^2 [1] 9 4 1 0 1 4 9 16 > > > ## quasipoisson. compare with example(glm) > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > d.AD <- data.frame(treatment, outcome, counts) > glm.qD93 <- glm(counts ~ outcome + treatment, family=quasipoisson()) > > ## Example of user-specified link, a logit model for p^days > ## See Shaffer, T. 2004. Auk 121(2): 526-540. > logexp <- function(days = 1) + { + linkfun <- function(mu) qlogis(mu^(1/days)) + linkinv <- function(eta) plogis(eta)^days + mu.eta <- function(eta) days * plogis(eta)^(days-1) * binomial()$mu_eta + valideta <- function(eta) TRUE + link <- paste0("logexp(", days, ")") + structure(list(linkfun = linkfun, linkinv = linkinv, + mu.eta = mu.eta, valideta = valideta, name = link), + class = "link-glm") + } > binomial(logexp(3)) Family: binomial Link function: logexp(3) > ## in practice this would be used with a vector of 'days', in > ## which case use an offset of 0 in the corresponding formula > ## to get the null deviance right. > > ## Binomial with identity link: often not a good idea. > ## Not run: binomial(link=make.link("identity")) > > ## tests of quasi > x <- rnorm(100) > y <- rpois(100, exp(1+x)) > glm(y ~x, family=quasi(variance="mu", link="log")) Call: glm(formula = y ~ x, family = quasi(variance = "mu", link = "log")) Coefficients: (Intercept) x 0.8596 1.0875 Degrees of Freedom: 99 Total (i.e. Null); 98 Residual Null Deviance: 481.9 Residual Deviance: 101.7 AIC: NA > # which is the same as > glm(y ~x, family=poisson) Call: glm(formula = y ~ x, family = poisson) Coefficients: (Intercept) x 0.8596 1.0875 Degrees of Freedom: 99 Total (i.e. Null); 98 Residual Null Deviance: 481.9 Residual Deviance: 101.7 AIC: 364 > glm(y ~x, family=quasi(variance="mu^2", link="log")) Call: glm(formula = y ~ x, family = quasi(variance = "mu^2", link = "log")) Coefficients: (Intercept) x 0.6902 1.4546 Degrees of Freedom: 99 Total (i.e. Null); 98 Residual Null Deviance: 83.85 Residual Deviance: 32.45 AIC: NA > ## Not run: glm(y ~x, family=quasi(variance="mu^3", link="log")) # fails > y <- rbinom(100, 1, plogis(x)) > # needs to set a starting value for the next fit > glm(y ~x, family=quasi(variance="mu(1-mu)", link="logit"), start=c(0,1)) Call: glm(formula = y ~ x, family = quasi(variance = "mu(1-mu)", link = "logit"), start = c(0, 1)) Coefficients: (Intercept) x -0.08334 0.38518 Degrees of Freedom: 99 Total (i.e. Null); 98 Residual Null Deviance: 138.6 Residual Deviance: 135.8 AIC: NA > > > > cleanEx() > nameEx("fft") > ### * fft > > flush(stderr()); flush(stdout()) > > ### Name: fft > ### Title: Fast Discrete Fourier Transform > ### Aliases: fft mvfft > ### Keywords: math dplot > > ### ** Examples > > x <- 1:4 > fft(x) [1] 10+0i -2+2i -2+0i -2-2i > fft(fft(x), inverse = TRUE)/length(x) [1] 1+0i 2+0i 3+0i 4+0i > > > > cleanEx() > nameEx("filter") > ### * filter > > flush(stderr()); flush(stdout()) > > ### Name: filter > ### Title: Linear Filtering on a Time Series > ### Aliases: filter > ### Keywords: ts > > ### ** Examples > > x <- 1:100 > filter(x, rep(1, 3)) Time Series: Start = 1 End = 100 Frequency = 1 [1] NA 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 [19] 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 105 108 [37] 111 114 117 120 123 126 129 132 135 138 141 144 147 150 153 156 159 162 [55] 165 168 171 174 177 180 183 186 189 192 195 198 201 204 207 210 213 216 [73] 219 222 225 228 231 234 237 240 243 246 249 252 255 258 261 264 267 270 [91] 273 276 279 282 285 288 291 294 297 NA > filter(x, rep(1, 3), sides = 1) Time Series: Start = 1 End = 100 Frequency = 1 [1] NA NA 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 [19] 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 105 [37] 108 111 114 117 120 123 126 129 132 135 138 141 144 147 150 153 156 159 [55] 162 165 168 171 174 177 180 183 186 189 192 195 198 201 204 207 210 213 [73] 216 219 222 225 228 231 234 237 240 243 246 249 252 255 258 261 264 267 [91] 270 273 276 279 282 285 288 291 294 297 > filter(x, rep(1, 3), sides = 1, circular = TRUE) Time Series: Start = 1 End = 100 Frequency = 1 [1] 200 103 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 [19] 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 105 [37] 108 111 114 117 120 123 126 129 132 135 138 141 144 147 150 153 156 159 [55] 162 165 168 171 174 177 180 183 186 189 192 195 198 201 204 207 210 213 [73] 216 219 222 225 228 231 234 237 240 243 246 249 252 255 258 261 264 267 [91] 270 273 276 279 282 285 288 291 294 297 > > filter(presidents, rep(1,3)) Qtr1 Qtr2 Qtr3 Qtr4 1945 NA NA 244 220 1946 188 156 125 110 1947 127 149 169 145 1948 130 NA NA NA 1949 NA 183 165 153 1950 133 128 122 121 1951 99 92 79 80 1952 80 NA NA NA 1953 165 208 209 206 1954 192 203 189 199 1955 196 218 220 228 1956 220 214 213 221 1957 216 204 182 180 1958 166 157 149 157 1959 171 180 189 198 1960 199 194 180 190 1961 212 226 232 228 1962 228 212 207 212 1963 214 202 183 199 1964 210 222 211 209 1965 204 204 195 194 1966 171 165 146 144 1967 140 134 136 120 1968 131 120 128 138 1969 168 189 186 187 1970 175 180 166 164 1971 151 153 151 152 1972 159 NA NA NA 1973 NA 152 111 95 1974 80 77 73 NA > > > > cleanEx() > nameEx("fisher.test") > ### * fisher.test > > flush(stderr()); flush(stdout()) > > ### Name: fisher.test > ### Title: Fisher's Exact Test for Count Data > ### Aliases: fisher.test > ### Keywords: htest > > ### ** Examples > > ## Agresti (1990, p. 61f; 2002, p. 91) Fisher's Tea Drinker > ## A British woman claimed to be able to distinguish whether milk or > ## tea was added to the cup first. To test, she was given 8 cups of > ## tea, in four of which milk was added first. The null hypothesis > ## is that there is no association between the true order of pouring > ## and the woman's guess, the alternative that there is a positive > ## association (that the odds ratio is greater than 1). > TeaTasting <- + matrix(c(3, 1, 1, 3), + nrow = 2, + dimnames = list(Guess = c("Milk", "Tea"), + Truth = c("Milk", "Tea"))) > fisher.test(TeaTasting, alternative = "greater") Fisher's Exact Test for Count Data data: TeaTasting p-value = 0.2429 alternative hypothesis: true odds ratio is greater than 1 95 percent confidence interval: 0.3135693 Inf sample estimates: odds ratio 6.408309 > ## => p=0.2429, association could not be established > > ## Fisher (1962, 1970), Criminal convictions of like-sex twins > Convictions <- + matrix(c(2, 10, 15, 3), + nrow = 2, + dimnames = + list(c("Dizygotic", "Monozygotic"), + c("Convicted", "Not convicted"))) > Convictions Convicted Not convicted Dizygotic 2 15 Monozygotic 10 3 > fisher.test(Convictions, alternative = "less") Fisher's Exact Test for Count Data data: Convictions p-value = 0.0004652 alternative hypothesis: true odds ratio is less than 1 95 percent confidence interval: 0.0000000 0.2849601 sample estimates: odds ratio 0.04693661 > fisher.test(Convictions, conf.int = FALSE) Fisher's Exact Test for Count Data data: Convictions p-value = 0.0005367 alternative hypothesis: true odds ratio is not equal to 1 sample estimates: odds ratio 0.04693661 > fisher.test(Convictions, conf.level = 0.95)$conf.int [1] 0.003325764 0.363182271 attr(,"conf.level") [1] 0.95 > fisher.test(Convictions, conf.level = 0.99)$conf.int [1] 0.001386333 0.578851645 attr(,"conf.level") [1] 0.99 > > ## A r x c table Agresti (2002, p. 57) Job Satisfaction > Job <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4, + dimnames = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"), + satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS"))) > fisher.test(Job) Fisher's Exact Test for Count Data data: Job p-value = 0.7827 alternative hypothesis: two.sided > fisher.test(Job, simulate.p.value=TRUE, B=1e5) Fisher's Exact Test for Count Data with simulated p-value (based on 1e+05 replicates) data: Job p-value = 0.7842 alternative hypothesis: two.sided > > > > cleanEx() > nameEx("fivenum") > ### * fivenum > > flush(stderr()); flush(stdout()) > > ### Name: fivenum > ### Title: Tukey Five-Number Summaries > ### Aliases: fivenum > ### Keywords: univar robust distribution > > ### ** Examples > > fivenum(c(rnorm(100),-1:1/0)) [1] -Inf -0.5425200 0.1139092 0.6969634 Inf > > > > cleanEx() > nameEx("fligner.test") > ### * fligner.test > > flush(stderr()); flush(stdout()) > > ### Name: fligner.test > ### Title: Fligner-Killeen Test of Homogeneity of Variances > ### Aliases: fligner.test fligner.test.default fligner.test.formula > ### Keywords: htest > > ### ** Examples > > require(graphics) > > plot(count ~ spray, data = InsectSprays) > fligner.test(InsectSprays$count, InsectSprays$spray) Fligner-Killeen test of homogeneity of variances data: InsectSprays$count and InsectSprays$spray Fligner-Killeen:med chi-squared = 14.4828, df = 5, p-value = 0.01282 > fligner.test(count ~ spray, data = InsectSprays) Fligner-Killeen test of homogeneity of variances data: count by spray Fligner-Killeen:med chi-squared = 14.4828, df = 5, p-value = 0.01282 > ## Compare this to bartlett.test() > > > > cleanEx() > nameEx("formula") > ### * formula > > flush(stderr()); flush(stdout()) > > ### Name: formula > ### Title: Model Formulae > ### Aliases: formula formula.default formula.formula formula.terms > ### formula.data.frame as.formula print.formula [.formula > ### Keywords: models > > ### ** Examples > > class(fo <- y ~ x1*x2) # "formula" [1] "formula" > fo y ~ x1 * x2 > typeof(fo)# R internal : "language" [1] "language" > terms(fo) y ~ x1 * x2 attr(,"variables") list(y, x1, x2) attr(,"factors") x1 x2 x1:x2 y 0 0 0 x1 1 0 1 x2 0 1 1 attr(,"term.labels") [1] "x1" "x2" "x1:x2" attr(,"order") [1] 1 1 2 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > > environment(fo) > environment(as.formula("y ~ x")) > environment(as.formula("y ~ x", env=new.env())) > > > ## Create a formula for a model with a large number of variables: > xnam <- paste0("x", 1:25) > (fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+")))) y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20 + x21 + x22 + x23 + x24 + x25 > > > > cleanEx() > nameEx("formula.nls") > ### * formula.nls > > flush(stderr()); flush(stdout()) > > ### Name: formula.nls > ### Title: Extract Model Formula from nls Object > ### Aliases: formula.nls > ### Keywords: models > > ### ** Examples > > fm1 <- nls(circumference ~ A/(1+exp((B-age)/C)), Orange, + start = list(A=160, B=700, C = 350)) > formula(fm1) circumference ~ A/(1 + exp((B - age)/C)) > > > > cleanEx() > nameEx("friedman.test") > ### * friedman.test > > flush(stderr()); flush(stdout()) > > ### Name: friedman.test > ### Title: Friedman Rank Sum Test > ### Aliases: friedman.test friedman.test.default friedman.test.formula > ### Keywords: htest > > ### ** Examples > > ## Hollander & Wolfe (1973), p. 140ff. > ## Comparison of three methods ("round out", "narrow angle", and > ## "wide angle") for rounding first base. For each of 18 players > ## and the three method, the average time of two runs from a point on > ## the first base line 35ft from home plate to a point 15ft short of > ## second base is recorded. > RoundingTimes <- + matrix(c(5.40, 5.50, 5.55, + 5.85, 5.70, 5.75, + 5.20, 5.60, 5.50, + 5.55, 5.50, 5.40, + 5.90, 5.85, 5.70, + 5.45, 5.55, 5.60, + 5.40, 5.40, 5.35, + 5.45, 5.50, 5.35, + 5.25, 5.15, 5.00, + 5.85, 5.80, 5.70, + 5.25, 5.20, 5.10, + 5.65, 5.55, 5.45, + 5.60, 5.35, 5.45, + 5.05, 5.00, 4.95, + 5.50, 5.50, 5.40, + 5.45, 5.55, 5.50, + 5.55, 5.55, 5.35, + 5.45, 5.50, 5.55, + 5.50, 5.45, 5.25, + 5.65, 5.60, 5.40, + 5.70, 5.65, 5.55, + 6.30, 6.30, 6.25), + nrow = 22, + byrow = TRUE, + dimnames = list(1 : 22, + c("Round Out", "Narrow Angle", "Wide Angle"))) > friedman.test(RoundingTimes) Friedman rank sum test data: RoundingTimes Friedman chi-squared = 11.1429, df = 2, p-value = 0.003805 > ## => strong evidence against the null that the methods are equivalent > ## with respect to speed > > wb <- aggregate(warpbreaks$breaks, + by = list(w = warpbreaks$wool, + t = warpbreaks$tension), + FUN = mean) > wb w t x 1 A L 44.55556 2 B L 28.22222 3 A M 24.00000 4 B M 28.77778 5 A H 24.55556 6 B H 18.77778 > friedman.test(wb$x, wb$w, wb$t) Friedman rank sum test data: wb$x, wb$w and wb$t Friedman chi-squared = 0.3333, df = 1, p-value = 0.5637 > friedman.test(x ~ w | t, data = wb) Friedman rank sum test data: x and w and t Friedman chi-squared = 0.3333, df = 1, p-value = 0.5637 > > > > cleanEx() > nameEx("ftable") > ### * ftable > > flush(stderr()); flush(stdout()) > > ### Name: ftable > ### Title: Flat Contingency Tables > ### Aliases: ftable ftable.default print.ftable > ### Keywords: category > > ### ** Examples > > ## Start with a contingency table. > ftable(Titanic, row.vars = 1:3) Survived No Yes Class Sex Age 1st Male Child 0 5 Adult 118 57 Female Child 0 1 Adult 4 140 2nd Male Child 0 11 Adult 154 14 Female Child 0 13 Adult 13 80 3rd Male Child 35 13 Adult 387 75 Female Child 17 14 Adult 89 76 Crew Male Child 0 0 Adult 670 192 Female Child 0 0 Adult 3 20 > ftable(Titanic, row.vars = 1:2, col.vars = "Survived") Survived No Yes Class Sex 1st Male 118 62 Female 4 141 2nd Male 154 25 Female 13 93 3rd Male 422 88 Female 106 90 Crew Male 670 192 Female 3 20 > ftable(Titanic, row.vars = 2:1, col.vars = "Survived") Survived No Yes Sex Class Male 1st 118 62 2nd 154 25 3rd 422 88 Crew 670 192 Female 1st 4 141 2nd 13 93 3rd 106 90 Crew 3 20 > > ## Start with a data frame. > x <- ftable(mtcars[c("cyl", "vs", "am", "gear")]) > x gear 3 4 5 cyl vs am 4 0 0 0 0 0 1 0 0 1 1 0 1 2 0 1 0 6 1 6 0 0 0 0 0 1 0 2 1 1 0 2 2 0 1 0 0 0 8 0 0 12 0 0 1 0 0 2 1 0 0 0 0 1 0 0 0 > ftable(x, row.vars = c(2, 4)) cyl 4 6 8 am 0 1 0 1 0 1 vs gear 0 3 0 0 0 0 12 0 4 0 0 0 2 0 0 5 0 1 0 1 0 2 1 3 1 0 2 0 0 0 4 2 6 2 0 0 0 5 0 1 0 0 0 0 > > ## Start with expressions, use table()'s "dnn" to change labels > ftable(mtcars$cyl, mtcars$vs, mtcars$am, mtcars$gear, row.vars = c(2, 4), + dnn = c("Cylinders", "V/S", "Transmission", "Gears")) Cylinders 4 6 8 Transmission 0 1 0 1 0 1 V/S Gears 0 3 0 0 0 0 12 0 4 0 0 0 2 0 0 5 0 1 0 1 0 2 1 3 1 0 2 0 0 0 4 2 6 2 0 0 0 5 0 1 0 0 0 0 > > > > cleanEx() > nameEx("ftable.formula") > ### * ftable.formula > > flush(stderr()); flush(stdout()) > > ### Name: ftable.formula > ### Title: Formula Notation for Flat Contingency Tables > ### Aliases: ftable.formula > ### Keywords: category > > ### ** Examples > > Titanic , , Age = Child, Survived = No Sex Class Male Female 1st 0 0 2nd 0 0 3rd 35 17 Crew 0 0 , , Age = Adult, Survived = No Sex Class Male Female 1st 118 4 2nd 154 13 3rd 387 89 Crew 670 3 , , Age = Child, Survived = Yes Sex Class Male Female 1st 5 1 2nd 11 13 3rd 13 14 Crew 0 0 , , Age = Adult, Survived = Yes Sex Class Male Female 1st 57 140 2nd 14 80 3rd 75 76 Crew 192 20 > x <- ftable(Survived ~ ., data = Titanic) > x Survived No Yes Class Sex Age 1st Male Child 0 5 Adult 118 57 Female Child 0 1 Adult 4 140 2nd Male Child 0 11 Adult 154 14 Female Child 0 13 Adult 13 80 3rd Male Child 35 13 Adult 387 75 Female Child 17 14 Adult 89 76 Crew Male Child 0 0 Adult 670 192 Female Child 0 0 Adult 3 20 > ftable(Sex ~ Class + Age, data = x) Sex Male Female Class Age 1st Child 5 1 Adult 175 144 2nd Child 11 13 Adult 168 93 3rd Child 48 31 Adult 462 165 Crew Child 0 0 Adult 862 23 > > > > cleanEx() > nameEx("getInitial") > ### * getInitial > > flush(stderr()); flush(stdout()) > > ### Name: getInitial > ### Title: Get Initial Parameter Estimates > ### Aliases: getInitial getInitial.default getInitial.formula > ### getInitial.selfStart > ### Keywords: models nonlinear manip > > ### ** Examples > > PurTrt <- Puromycin[ Puromycin$state == "treated", ] > print(getInitial( rate ~ SSmicmen( conc, Vm, K ), PurTrt ), digits = 3) Vm K 212.6837 0.0641 > > > > cleanEx() > nameEx("glm") > ### * glm > > flush(stderr()); flush(stdout()) > > ### Name: glm > ### Title: Fitting Generalized Linear Models > ### Aliases: glm glm.fit weights.glm print.glm > ### Keywords: models regression > > ### ** Examples > > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > print(d.AD <- data.frame(treatment, outcome, counts)) treatment outcome counts 1 1 1 18 2 1 2 17 3 1 3 15 4 2 1 20 5 2 2 10 6 2 3 20 7 3 1 25 8 3 2 13 9 3 3 12 > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) > anova(glm.D93) Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev NULL 8 10.5814 outcome 2 5.4523 6 5.1291 treatment 2 0.0000 4 5.1291 > > ## an example with offsets from Venables & Ripley (2002, p.189) > utils::data(anorexia, package="MASS") > > anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt), + family = gaussian, data = anorexia) > summary(anorex.1) Call: glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia) Deviance Residuals: Min 1Q Median 3Q Max -14.1083 -4.2773 -0.5484 5.4838 15.2922 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 49.7711 13.3910 3.717 0.000410 *** Prewt -0.5655 0.1612 -3.509 0.000803 *** TreatCont -4.0971 1.8935 -2.164 0.033999 * TreatFT 4.5631 2.1333 2.139 0.036035 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 48.69504) Null deviance: 4525.4 on 71 degrees of freedom Residual deviance: 3311.3 on 68 degrees of freedom AIC: 489.97 Number of Fisher Scoring iterations: 2 > > # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2) > clotting <- data.frame( + u = c(5,10,15,20,30,40,60,80,100), + lot1 = c(118,58,42,35,27,25,21,19,18), + lot2 = c(69,35,26,21,18,16,13,12,12)) > summary(glm(lot1 ~ log(u), data=clotting, family=Gamma)) Call: glm(formula = lot1 ~ log(u), family = Gamma, data = clotting) Deviance Residuals: Min 1Q Median 3Q Max -0.04008 -0.03756 -0.02637 0.02905 0.08641 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0165544 0.0009275 -17.85 4.28e-07 *** log(u) 0.0153431 0.0004150 36.98 2.75e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Gamma family taken to be 0.002446059) Null deviance: 3.51283 on 8 degrees of freedom Residual deviance: 0.01673 on 7 degrees of freedom AIC: 37.99 Number of Fisher Scoring iterations: 3 > summary(glm(lot2 ~ log(u), data=clotting, family=Gamma)) Call: glm(formula = lot2 ~ log(u), family = Gamma, data = clotting) Deviance Residuals: Min 1Q Median 3Q Max -0.05574 -0.02925 0.01030 0.01714 0.06371 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0239085 0.0013265 -18.02 4.00e-07 *** log(u) 0.0235992 0.0005768 40.91 1.36e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Gamma family taken to be 0.001813354) Null deviance: 3.118557 on 8 degrees of freedom Residual deviance: 0.012672 on 7 degrees of freedom AIC: 27.032 Number of Fisher Scoring iterations: 3 > > ## Not run: > ##D ## for an example of the use of a terms object as a formula > ##D demo(glm.vr) > ## End(Not run) > > > cleanEx() > nameEx("glm.control") > ### * glm.control > > flush(stderr()); flush(stdout()) > > ### Name: glm.control > ### Title: Auxiliary for Controlling GLM Fitting > ### Aliases: glm.control > ### Keywords: optimize models > > ### ** Examples > > > cleanEx() > nameEx("hclust") > ### * hclust > > flush(stderr()); flush(stdout()) > > ### Name: hclust > ### Title: Hierarchical Clustering > ### Aliases: hclust plot.hclust plclust print.hclust > ### Keywords: multivariate cluster > > ### ** Examples > > require(graphics) > > hc <- hclust(dist(USArrests), "ave") > plot(hc) > plot(hc, hang = -1) > > ## Do the same with centroid clustering and squared Euclidean distance, > ## cut the tree into ten clusters and reconstruct the upper part of the > ## tree from the cluster centers. > hc <- hclust(dist(USArrests)^2, "cen") > memb <- cutree(hc, k = 10) > cent <- NULL > for(k in 1:10){ + cent <- rbind(cent, colMeans(USArrests[memb == k, , drop = FALSE])) + } > hc1 <- hclust(dist(cent)^2, method = "cen", members = table(memb)) > opar <- par(mfrow = c(1, 2)) > plot(hc, labels = FALSE, hang = -1, main = "Original Tree") > plot(hc1, labels = FALSE, hang = -1, main = "Re-start from 10 clusters") > par(opar) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("heatmap") > ### * heatmap > > flush(stderr()); flush(stdout()) > > ### Name: heatmap > ### Title: Draw a Heat Map > ### Aliases: heatmap > ### Keywords: hplot > > ### ** Examples > > require(graphics); require(grDevices) > x <- as.matrix(mtcars) > rc <- rainbow(nrow(x), start=0, end=.3) > cc <- rainbow(ncol(x), start=0, end=.3) > hv <- heatmap(x, col = cm.colors(256), scale="column", + RowSideColors = rc, ColSideColors = cc, margins=c(5,10), + xlab = "specification variables", ylab= "Car Models", + main = "heatmap(, ..., scale = \"column\")") > utils::str(hv) # the two re-ordering index vectors List of 4 $ rowInd: int [1:32] 31 17 16 15 5 25 29 24 7 6 ... $ colInd: int [1:11] 2 9 8 11 6 5 10 7 1 4 ... $ Rowv : NULL $ Colv : NULL > > ## no column dendrogram (nor reordering) at all: > heatmap(x, Colv = NA, col = cm.colors(256), scale="column", + RowSideColors = rc, margins=c(5,10), + xlab = "specification variables", ylab= "Car Models", + main = "heatmap(, ..., scale = \"column\")") > ## Don't show: > ## no row dendrogram (nor reordering) at all: > heatmap(x, Rowv = NA, col = cm.colors(256), scale="column", + ColSideColors = cc, margins=c(5,10), + xlab = "xlab", ylab= "ylab")# no main > ## End Don't show > ## "no nothing" > heatmap(x, Rowv = NA, Colv = NA, scale="column", + main = "heatmap(*, NA, NA) ~= image(t(x))") > > round(Ca <- cor(attitude), 2) rating complaints privileges learning raises critical advance rating 1.00 0.83 0.43 0.62 0.59 0.16 0.16 complaints 0.83 1.00 0.56 0.60 0.67 0.19 0.22 privileges 0.43 0.56 1.00 0.49 0.45 0.15 0.34 learning 0.62 0.60 0.49 1.00 0.64 0.12 0.53 raises 0.59 0.67 0.45 0.64 1.00 0.38 0.57 critical 0.16 0.19 0.15 0.12 0.38 1.00 0.28 advance 0.16 0.22 0.34 0.53 0.57 0.28 1.00 > symnum(Ca) # simple graphic rt cm p l rs cr a rating 1 complaints + 1 privileges . . 1 learning , . . 1 raises . , . , 1 critical . 1 advance . . . 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > heatmap(Ca, symm = TRUE, margins=c(6,6))# with reorder() > heatmap(Ca, Rowv=FALSE, symm = TRUE, margins=c(6,6))# _NO_ reorder() > ## slightly artificial with color bar, without and with ordering: > cc <- rainbow(nrow(Ca)) > heatmap(Ca, Rowv=FALSE, symm = TRUE, RowSideColors=cc, ColSideColors=cc, + margins=c(6,6)) > heatmap(Ca, symm = TRUE, RowSideColors=cc, ColSideColors=cc, + margins=c(6,6)) > > ## For variable clustering, rather use distance based on cor(): > symnum( cU <- cor(USJudgeRatings) ) CO I DM DI CF DE PR F O W PH R CONT 1 INTG 1 DMNR B 1 DILG + + 1 CFMG + + B 1 DECI + + B B 1 PREP + + B B B 1 FAMI + + B * * B 1 ORAL * * B B * B B 1 WRIT * + B * * B B B 1 PHYS , , + + + + + + + 1 RTEN * * * * * B * B B * 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > > hU <- heatmap(cU, Rowv = FALSE, symm = TRUE, col = topo.colors(16), + distfun = function(c) as.dist(1 - c), keep.dendro = TRUE) > ## The Correlation matrix with same reordering: > round(100 * cU[hU[[1]], hU[[2]]]) CONT INTG DMNR PHYS DILG CFMG DECI RTEN ORAL WRIT PREP FAMI CONT 100 -13 -15 5 1 14 9 -3 -1 -4 1 -3 INTG -13 100 96 74 87 81 80 94 91 91 88 87 DMNR -15 96 100 79 84 81 80 94 91 89 86 84 PHYS 5 74 79 100 81 88 87 91 89 86 85 84 DILG 1 87 84 81 100 96 96 93 95 96 98 96 CFMG 14 81 81 88 96 100 98 93 95 94 96 94 DECI 9 80 80 87 96 98 100 92 95 95 96 94 RTEN -3 94 94 91 93 93 92 100 98 97 95 94 ORAL -1 91 91 89 95 95 95 98 100 99 98 98 WRIT -4 91 89 86 96 94 95 97 99 100 99 99 PREP 1 88 86 85 98 96 96 95 98 99 100 99 FAMI -3 87 84 84 96 94 94 94 98 99 99 100 > ## The column dendrogram: > utils::str(hU$Colv) --[dendrogram w/ 2 branches and 12 members at h = 1.15] |--leaf "CONT" `--[dendrogram w/ 2 branches and 11 members at h = 0.258] |--[dendrogram w/ 2 branches and 2 members at h = 0.0354] | |--leaf "INTG" | `--leaf "DMNR" `--[dendrogram w/ 2 branches and 9 members at h = 0.187] |--leaf "PHYS" `--[dendrogram w/ 2 branches and 8 members at h = 0.075] |--[dendrogram w/ 2 branches and 3 members at h = 0.0438] | |--leaf "DILG" | `--[dendrogram w/ 2 branches and 2 members at h = 0.0189] | |--leaf "CFMG" | `--leaf "DECI" `--[dendrogram w/ 2 branches and 5 members at h = 0.0584] |--leaf "RTEN" `--[dendrogram w/ 2 branches and 4 members at h = 0.0187] |--[dendrogram w/ 2 branches and 2 members at h = 0.00657] | |--leaf "ORAL" | `--leaf "WRIT" `--[dendrogram w/ 2 branches and 2 members at h = 0.0101] |--leaf "PREP" `--leaf "FAMI" > > > > cleanEx() > nameEx("identify.hclust") > ### * identify.hclust > > flush(stderr()); flush(stdout()) > > ### Name: identify.hclust > ### Title: Identify Clusters in a Dendrogram > ### Aliases: identify.hclust > ### Keywords: cluster iplot > > ### ** Examples > ## Not run: > ##D require(graphics) > ##D > ##D hca <- hclust(dist(USArrests)) > ##D plot(hca) > ##D (x <- identify(hca)) ## Terminate with 2nd mouse button !! > ##D > ##D hci <- hclust(dist(iris[,1:4])) > ##D plot(hci) > ##D identify(hci, function(k) print(table(iris[k,5]))) > ##D > ##D # open a new device (one for dendrogram, one for bars): > ##D get(getOption("device"))() # << make that narrow (& small) > ##D # and *beside* 1st one > ##D nD <- dev.cur() # to be for the barplot > ##D dev.set(dev.prev())# old one for dendrogram > ##D plot(hci) > ##D ## select subtrees in dendrogram and "see" the species distribution: > ##D identify(hci, function(k) barplot(table(iris[k,5]),col=2:4), DEV.FUN = nD) > ## End(Not run) > > > cleanEx() > nameEx("influence.measures") > ### * influence.measures > > flush(stderr()); flush(stdout()) > > ### Name: influence.measures > ### Title: Regression Deletion Diagnostics > ### Aliases: influence.measures print.infl summary.infl hat hatvalues > ### hatvalues.lm rstandard rstandard.lm rstandard.glm rstudent > ### rstudent.lm rstudent.glm dfbeta dfbeta.lm dfbetas dfbetas.lm dffits > ### covratio cooks.distance cooks.distance.lm cooks.distance.glm > ### Keywords: regression > > ### ** Examples > > > cleanEx() > nameEx("integrate") > ### * integrate > > flush(stderr()); flush(stdout()) > > ### Name: integrate > ### Title: Integration of One-Dimensional Functions > ### Aliases: integrate print.integrate > ### Keywords: math utilities > > ### ** Examples > > integrate(dnorm, -1.96, 1.96) 0.9500042 with absolute error < 1e-11 > integrate(dnorm, -Inf, Inf) 1 with absolute error < 9.4e-05 > > ## a slowly-convergent integral > integrand <- function(x) {1/((x+1)*sqrt(x))} > integrate(integrand, lower = 0, upper = Inf) 3.141593 with absolute error < 2.7e-05 > > ## don't do this if you really want the integral from 0 to Inf > integrate(integrand, lower = 0, upper = 10) 2.529038 with absolute error < 3e-04 > integrate(integrand, lower = 0, upper = 100000) 3.135268 with absolute error < 4.2e-07 > integrate(integrand, lower = 0, upper = 1000000, stop.on.error = FALSE) failed with message ‘the integral is probably divergent’ > > ## some functions do not handle vector input properly > f <- function(x) 2.0 > try(integrate(f, 0, 1)) Error in integrate(f, 0, 1) : evaluation of function gave a result of wrong length > integrate(Vectorize(f), 0, 1) ## correct 2 with absolute error < 2.2e-14 > integrate(function(x) rep(2.0, length(x)), 0, 1) ## correct 2 with absolute error < 2.2e-14 > > ## integrate can fail if misused > integrate(dnorm,0,2) 0.4772499 with absolute error < 5.3e-15 > integrate(dnorm,0,20) 0.5 with absolute error < 3.7e-05 > integrate(dnorm,0,200) 0.5 with absolute error < 1.6e-07 > integrate(dnorm,0,2000) 0.5 with absolute error < 4.4e-06 > integrate(dnorm,0,20000) ## fails on many systems 0 with absolute error < 0 > integrate(dnorm,0,Inf) ## works 0.5 with absolute error < 4.7e-05 > > > > cleanEx() > nameEx("interaction.plot") > ### * interaction.plot > > flush(stderr()); flush(stdout()) > > ### Name: interaction.plot > ### Title: Two-way Interaction Plot > ### Aliases: interaction.plot > ### Keywords: hplot > > ### ** Examples > > require(graphics) > > with(ToothGrowth, { + interaction.plot(dose, supp, len, fixed=TRUE) + dose <- ordered(dose) + interaction.plot(dose, supp, len, fixed=TRUE, col = 2:3, leg.bty = "o") + interaction.plot(dose, supp, len, fixed=TRUE, col = 2:3, type = "p") + }) > > with(OrchardSprays, { + interaction.plot(treatment, rowpos, decrease) + interaction.plot(rowpos, treatment, decrease, cex.axis=0.8) + ## order the rows by their mean effect + rowpos <- factor(rowpos, + levels = sort.list(tapply(decrease, rowpos, mean))) + interaction.plot(rowpos, treatment, decrease, col = 2:9, lty = 1) + }) > > with(esoph, { + interaction.plot(agegp, alcgp, ncases/ncontrols, main = "'esoph' Data") + interaction.plot(agegp, tobgp, ncases/ncontrols, trace.label="tobacco", + fixed=TRUE, xaxt = "n") + }) > ## deal with NAs: > esoph[66,] # second to last age group: 65-74 agegp alcgp tobgp ncases ncontrols 66 65-74 0-39g/day 30+ 0 2 > esophNA <- esoph; esophNA$ncases[66] <- NA > with(esophNA, { + interaction.plot(agegp, alcgp, ncases/ncontrols, col= 2:5) + # doesn't show *last* group either + interaction.plot(agegp, alcgp, ncases/ncontrols, col= 2:5, type = "b") + ## alternative take non-NA's {"cheating"} + interaction.plot(agegp, alcgp, ncases/ncontrols, col= 2:5, + fun = function(x) mean(x, na.rm=TRUE), + sub = "function(x) mean(x, na.rm=TRUE)") + }) > rm(esophNA) # to clear up > > > > cleanEx() > nameEx("is.empty") > ### * is.empty > > flush(stderr()); flush(stdout()) > > ### Name: is.empty.model > ### Title: Test if a Model's Formula is Empty > ### Aliases: is.empty.model > ### Keywords: models > > ### ** Examples > > y <- rnorm(20) > is.empty.model(y ~ 0) [1] TRUE > is.empty.model(y ~ -1) [1] TRUE > is.empty.model(lm(y ~ 0)) [1] TRUE > > > > cleanEx() > nameEx("isoreg") > ### * isoreg > > flush(stderr()); flush(stdout()) > > ### Name: isoreg > ### Title: Isotonic / Monotone Regression > ### Aliases: isoreg > ### Keywords: regression smooth > > ### ** Examples > > require(graphics) > > (ir <- isoreg(c(1,0,4,3,3,5,4,2,0))) Isotonic regression from isoreg(x = c(1, 0, 4, 3, 3, 5, 4, 2, 0)), with 2 knots / breaks at obs.nr. 2 9 ; initially ordered 'x' and further components List of 4 $ x : num [1:9] 1 2 3 4 5 6 7 8 9 $ y : num [1:9] 1 0 4 3 3 5 4 2 0 $ yf: num [1:9] 0.5 0.5 3 3 3 3 3 3 3 $ yc: num [1:10] 0 1 1 5 8 11 16 20 22 22 > plot(ir, plot.type = "row") > > (ir3 <- isoreg(y3 <- c(1,0,4,3,3,5,4,2, 3)))# last "3", not "0" Isotonic regression from isoreg(x = y3 <- c(1, 0, 4, 3, 3, 5, 4, 2, 3)), with 3 knots / breaks at obs.nr. 2 5 9 ; initially ordered 'x' and further components List of 4 $ x : num [1:9] 1 2 3 4 5 6 7 8 9 $ y : num [1:9] 1 0 4 3 3 5 4 2 3 $ yf: num [1:9] 0.5 0.5 3.33 3.33 3.33 ... $ yc: num [1:10] 0 1 1 5 8 11 16 20 22 25 > (fi3 <- as.stepfun(ir3)) Step function Call: isoreg(x = y3 <- c(1, 0, 4, 3, 3, 5, 4, 2, 3)) x[1:3] = 2, 5, 9 4 plateau levels = 0.5, 0.5, 3.3333, 3.5 > (ir4 <- isoreg(1:10, y4 <- c(5, 9, 1:2, 5:8, 3, 8))) Isotonic regression from isoreg(x = 1:10, y = y4 <- c(5, 9, 1:2, 5:8, 3, 8)), with 5 knots / breaks at obs.nr. 4 5 6 9 10 ; initially ordered 'x' and further components List of 4 $ x : num [1:10] 1 2 3 4 5 6 7 8 9 10 $ y : num [1:10] 5 9 1 2 5 6 7 8 3 8 $ yf: num [1:10] 4.25 4.25 4.25 4.25 5 6 6 6 6 8 $ yc: num [1:11] 0 5 14 15 17 22 28 35 43 46 ... > cat(sprintf("R^2 = %.2f\n", + 1 - sum(residuals(ir4)^2) / ((10-1)*var(y4)))) R^2 = 0.21 > > ## If you are interested in the knots alone : > with(ir4, cbind(iKnots, yf[iKnots])) iKnots [1,] 4 4.25 [2,] 5 5.00 [3,] 6 6.00 [4,] 9 6.00 [5,] 10 8.00 > > ## Example of unordered x[] with ties: > x <- sample((0:30)/8) > y <- exp(x) > x. <- round(x) # ties! > plot(m <- isoreg(x., y)) > stopifnot(all.equal(with(m, yf[iKnots]), + as.vector(tapply(y, x., mean)))) > > > > cleanEx() > nameEx("kernapply") > ### * kernapply > > flush(stderr()); flush(stdout()) > > ### Name: kernapply > ### Title: Apply Smoothing Kernel > ### Aliases: kernapply kernapply.default kernapply.ts kernapply.tskernel > ### kernapply.vector > ### Keywords: ts > > ### ** Examples > > ## see 'kernel' for examples > > > > cleanEx() > nameEx("kernel") > ### * kernel > > flush(stderr()); flush(stdout()) > > ### Name: kernel > ### Title: Smoothing Kernel Objects > ### Aliases: kernel bandwidth.kernel df.kernel is.tskernel plot.tskernel > ### Keywords: ts > > ### ** Examples > > require(graphics) > > ## Demonstrate a simple trading strategy for the > ## financial time series German stock index DAX. > x <- EuStockMarkets[,1] > k1 <- kernel("daniell", 50) # a long moving average > k2 <- kernel("daniell", 10) # and a short one > plot(k1) > plot(k2) > x1 <- kernapply(x, k1) > x2 <- kernapply(x, k2) > plot(x) > lines(x1, col = "red") # go long if the short crosses the long upwards > lines(x2, col = "green") # and go short otherwise > > ## More interesting kernels > kd <- kernel("daniell", c(3,3)) > kd # note the unusual indexing Daniell(3,3) coef[-6] = 0.02041 coef[-5] = 0.04082 coef[-4] = 0.06122 coef[-3] = 0.08163 coef[-2] = 0.10204 coef[-1] = 0.12245 coef[ 0] = 0.14286 coef[ 1] = 0.12245 coef[ 2] = 0.10204 coef[ 3] = 0.08163 coef[ 4] = 0.06122 coef[ 5] = 0.04082 coef[ 6] = 0.02041 > kd[-2:2] [1] 0.1020408 0.1224490 0.1428571 0.1224490 0.1020408 > plot(kernel("fejer", 100, r=6)) > plot(kernel("modified.daniell", c(7,5,3))) > > # Reproduce example 10.4.3 from Brockwell and Davis (1991) > spectrum(sunspot.year, kernel=kernel("daniell", c(11,7,3)), log="no") > > > > cleanEx() > nameEx("kmeans") > ### * kmeans > > flush(stderr()); flush(stdout()) > > ### Name: kmeans > ### Title: K-Means Clustering > ### Aliases: kmeans print.kmeans fitted.kmeans > ### Keywords: multivariate cluster > > ### ** Examples > > require(graphics) > > # a 2-dimensional example > x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2), + matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)) > colnames(x) <- c("x", "y") > (cl <- kmeans(x, 2)) K-means clustering with 2 clusters of sizes 51, 49 Cluster means: x y 1 0.94443633 1.01712793 2 0.02149367 0.02121248 Clustering vector: [1] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Within cluster sum of squares by cluster: [1] 8.392416 6.525480 (between_SS / total_SS = 75.5 %) Available components: [1] "cluster" "centers" "totss" "withinss" "tot.withinss" [6] "betweenss" "size" > plot(x, col = cl$cluster) > points(cl$centers, col = 1:2, pch = 8, cex=2) > > # sum of squares > ss <- function(x) sum(scale(x, scale = FALSE)^2) > > ## cluster centers "fitted" to each obs.: > fitted.x <- fitted(cl); head(fitted.x) x y 2 0.02149367 0.02121248 2 0.02149367 0.02121248 2 0.02149367 0.02121248 2 0.02149367 0.02121248 2 0.02149367 0.02121248 2 0.02149367 0.02121248 > resid.x <- x - fitted(cl) > > ## Equalities : ---------------------------------- > cbind(cl[c("betweenss", "tot.withinss", "totss")], # the same two columns + c(ss(fitted.x), ss(resid.x), ss(x))) [,1] [,2] betweenss 46.07333 46.07333 tot.withinss 14.9179 14.9179 totss 60.99123 60.99123 > stopifnot(all.equal(cl$ totss, ss(x)), + all.equal(cl$ tot.withinss, ss(resid.x)), + ## these three are the same: + all.equal(cl$ betweenss, ss(fitted.x)), + all.equal(cl$ betweenss, cl$totss - cl$tot.withinss), + ## and hence also + all.equal(ss(x), ss(fitted.x) + ss(resid.x)) + ) > > kmeans(x,1)$withinss # trivial one-cluster, (its W.SS == ss(x)) [1] 60.99123 > > ## random starts do help here with too many clusters > ## (and are often recommended anyway!): > (cl <- kmeans(x, 5, nstart = 25)) K-means clustering with 5 clusters of sizes 24, 15, 24, 25, 12 Cluster means: x y 1 0.1581362 -0.1761590 2 0.8609139 1.3145869 3 0.8043520 0.7805033 4 -0.1096832 0.2106891 5 1.3290081 1.1185534 Clustering vector: [1] 4 1 4 1 4 4 1 1 4 4 3 1 4 4 1 4 1 4 1 4 1 1 4 4 1 4 1 4 4 1 1 1 4 1 4 4 4 [38] 1 1 1 1 4 4 4 4 4 1 1 1 1 2 3 3 3 3 5 5 5 3 5 2 3 5 2 3 2 3 3 5 2 2 5 2 3 [75] 3 5 2 2 2 2 3 2 5 3 5 3 2 3 3 3 3 5 3 2 3 3 5 3 2 3 Within cluster sum of squares by cluster: [1] 1.2816507 0.7104553 1.5056575 2.5330710 1.0314888 (between_SS / total_SS = 88.4 %) Available components: [1] "cluster" "centers" "totss" "withinss" "tot.withinss" [6] "betweenss" "size" > plot(x, col = cl$cluster) > points(cl$centers, col = 1:5, pch = 8) > > > > cleanEx() > nameEx("kruskal.test") > ### * kruskal.test > > flush(stderr()); flush(stdout()) > > ### Name: kruskal.test > ### Title: Kruskal-Wallis Rank Sum Test > ### Aliases: kruskal.test kruskal.test.default kruskal.test.formula > ### Keywords: htest > > ### ** Examples > > ## Hollander & Wolfe (1973), 116. > ## Mucociliary efficiency from the rate of removal of dust in normal > ## subjects, subjects with obstructive airway disease, and subjects > ## with asbestosis. > x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects > y <- c(3.8, 2.7, 4.0, 2.4) # with obstructive airway disease > z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis > kruskal.test(list(x, y, z)) Kruskal-Wallis rank sum test data: list(x, y, z) Kruskal-Wallis chi-squared = 0.7714, df = 2, p-value = 0.68 > ## Equivalently, > x <- c(x, y, z) > g <- factor(rep(1:3, c(5, 4, 5)), + labels = c("Normal subjects", + "Subjects with obstructive airway disease", + "Subjects with asbestosis")) > kruskal.test(x, g) Kruskal-Wallis rank sum test data: x and g Kruskal-Wallis chi-squared = 0.7714, df = 2, p-value = 0.68 > > ## Formula interface. > require(graphics) > boxplot(Ozone ~ Month, data = airquality) > kruskal.test(Ozone ~ Month, data = airquality) Kruskal-Wallis rank sum test data: Ozone by Month Kruskal-Wallis chi-squared = 29.2666, df = 4, p-value = 6.901e-06 > > > > cleanEx() > nameEx("ks.test") > ### * ks.test > > flush(stderr()); flush(stdout()) > > ### Name: ks.test > ### Title: Kolmogorov-Smirnov Tests > ### Aliases: ks.test > ### Keywords: htest > > ### ** Examples > > require(graphics) > > x <- rnorm(50) > y <- runif(30) > # Do x and y come from the same distribution? > ks.test(x, y) Two-sample Kolmogorov-Smirnov test data: x and y D = 0.48, p-value = 0.0002033 alternative hypothesis: two-sided > # Does x come from a shifted gamma distribution with shape 3 and rate 2? > ks.test(x+2, "pgamma", 3, 2) # two-sided, exact One-sample Kolmogorov-Smirnov test data: x + 2 D = 0.4096, p-value = 4.227e-08 alternative hypothesis: two-sided > ks.test(x+2, "pgamma", 3, 2, exact = FALSE) One-sample Kolmogorov-Smirnov test data: x + 2 D = 0.4096, p-value = 1.033e-07 alternative hypothesis: two-sided > ks.test(x+2, "pgamma", 3, 2, alternative = "gr") One-sample Kolmogorov-Smirnov test data: x + 2 D^+ = 0.04, p-value = 0.8302 alternative hypothesis: the CDF of x lies above the null hypothesis > > # test if x is stochastically larger than x2 > x2 <- rnorm(50, -1) > plot(ecdf(x), xlim=range(c(x, x2))) > plot(ecdf(x2), add=TRUE, lty="dashed") > t.test(x, x2, alternative="g") Welch Two Sample t-test data: x and x2 t = 5.6742, df = 96.85, p-value = 7.242e-08 alternative hypothesis: true difference in means is greater than 0 95 percent confidence interval: 0.7069751 Inf sample estimates: mean of x mean of y 0.1004483 -0.8990693 > wilcox.test(x, x2, alternative="g") Wilcoxon rank sum test with continuity correction data: x and x2 W = 1983, p-value = 2.212e-07 alternative hypothesis: true location shift is greater than 0 > ks.test(x, x2, alternative="l") Two-sample Kolmogorov-Smirnov test data: x and x2 D^- = 0.5, p-value = 3.727e-06 alternative hypothesis: the CDF of x lies below that of y > > > > cleanEx() > nameEx("ksmooth") > ### * ksmooth > > flush(stderr()); flush(stdout()) > > ### Name: ksmooth > ### Title: Kernel Regression Smoother > ### Aliases: ksmooth > ### Keywords: smooth > > ### ** Examples > > require(graphics) > > with(cars, { + plot(speed, dist) + lines(ksmooth(speed, dist, "normal", bandwidth=2), col=2) + lines(ksmooth(speed, dist, "normal", bandwidth=5), col=3) + }) > > > > cleanEx() > nameEx("lag") > ### * lag > > flush(stderr()); flush(stdout()) > > ### Name: lag > ### Title: Lag a Time Series > ### Aliases: lag lag.default > ### Keywords: ts > > ### ** Examples > > lag(ldeaths, 12) # starts one year earlier Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1973 3035 2552 2704 2554 2014 1655 1721 1524 1596 2074 2199 2512 1974 2933 2889 2938 2497 1870 1726 1607 1545 1396 1787 2076 2837 1975 2787 3891 3179 2011 1636 1580 1489 1300 1356 1653 2013 2823 1976 3102 2294 2385 2444 1748 1554 1498 1361 1346 1564 1640 2293 1977 2815 3137 2679 1969 1870 1633 1529 1366 1357 1570 1535 2491 1978 3084 2605 2573 2143 1693 1504 1461 1354 1333 1492 1781 1915 > > > > cleanEx() > nameEx("lag.plot") > ### * lag.plot > > flush(stderr()); flush(stdout()) > > ### Name: lag.plot > ### Title: Time Series Lag Plots > ### Aliases: lag.plot > ### Keywords: hplot ts > > ### ** Examples > > require(graphics) > > lag.plot(nhtemp, 8, diag.col = "forest green") > lag.plot(nhtemp, 5, main="Average Temperatures in New Haven") > ## ask defaults to TRUE when we have more than one page: > lag.plot(nhtemp, 6, layout = c(2,1), asp = NA, + main = "New Haven Temperatures", col.main = "blue") > > ## Multivariate (but non-stationary! ...) > lag.plot(freeny.x, lags = 3) > ## Not run: > ##D no lines for long series : > ##D lag.plot(sqrt(sunspots), set = c(1:4, 9:12), pch = ".", col = "gold") > ## End(Not run) > > > > cleanEx() > nameEx("line") > ### * line > > flush(stderr()); flush(stdout()) > > ### Name: line > ### Title: Robust Line Fitting > ### Aliases: line residuals.tukeyline > ### Keywords: robust regression > > ### ** Examples > > require(graphics) > > plot(cars) > (z <- line(cars)) Call: line(cars) Coefficients: [1] -26.053 4.421 > abline(coef(z)) > ## Tukey-Anscombe Plot : > plot(residuals(z) ~ fitted(z), main = deparse(z$call)) > > > > cleanEx() > nameEx("lm") > ### * lm > > flush(stderr()); flush(stdout()) > > ### Name: lm > ### Title: Fitting Linear Models > ### Aliases: lm print.lm > ### Keywords: regression > > ### ** Examples > > require(graphics) > > ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". > ## Page 9: Plant Weight Data. > ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) > trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) > group <- gl(2,10,20, labels=c("Ctl","Trt")) > weight <- c(ctl, trt) > lm.D9 <- lm(weight ~ group) > lm.D90 <- lm(weight ~ group - 1) # omitting intercept > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(lm.D9, las = 1) # Residuals, Fitted, ... > par(opar) > ## Don't show: > ## model frame : > stopifnot(identical(lm(weight ~ group, method = "model.frame"), + model.frame(lm.D9))) > ## End Don't show > ### less simple examples in "See Also" above > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("lm.influence") > ### * lm.influence > > flush(stderr()); flush(stdout()) > > ### Name: lm.influence > ### Title: Regression Diagnostics > ### Aliases: lm.influence influence influence.lm influence.glm > ### Keywords: regression > > ### ** Examples > > ## Analysis of the life-cycle savings data > ## given in Belsley, Kuh and Welsch. > summary(lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, + data = LifeCycleSavings), + corr = TRUE) Call: lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) Residuals: Min 1Q Median 3Q Max -8.2422 -2.6857 -0.2488 2.4280 9.7509 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 28.5660865 7.3545161 3.884 0.000334 *** pop15 -0.4611931 0.1446422 -3.189 0.002603 ** pop75 -1.6914977 1.0835989 -1.561 0.125530 dpi -0.0003369 0.0009311 -0.362 0.719173 ddpi 0.4096949 0.1961971 2.088 0.042471 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.803 on 45 degrees of freedom Multiple R-squared: 0.3385, Adjusted R-squared: 0.2797 F-statistic: 5.756 on 4 and 45 DF, p-value: 0.0007904 Correlation of Coefficients: (Intercept) pop15 pop75 dpi pop15 -0.98 pop75 -0.81 0.77 dpi -0.17 0.18 -0.37 ddpi -0.19 0.10 -0.05 0.26 > utils::str(lmI <- lm.influence(lm.SR)) List of 4 $ hat : Named num [1:50] 0.0677 0.1204 0.0875 0.0895 0.0696 ... ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ... $ coefficients: num [1:50, 1:5] 0.0916 -0.0747 -0.4752 0.0429 0.6604 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ... .. ..$ : chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ... $ sigma : Named num [1:50] 3.84 3.84 3.83 3.84 3.81 ... ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ... $ wt.res : Named num [1:50] 0.864 0.616 2.219 -0.698 3.553 ... ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ... > > ## For more "user level" examples, use example(influence.measures) > > > > cleanEx() > nameEx("lm.summaries") > ### * lm.summaries > > flush(stderr()); flush(stdout()) > > ### Name: lm.summaries > ### Title: Accessing Linear Model Fits > ### Aliases: family.lm formula.lm residuals.lm labels.lm > ### Keywords: regression models > > ### ** Examples > > ## Don't show: > utils::example("lm", echo = FALSE) > ## End Don't show > ##-- Continuing the lm(.) example: > coef(lm.D90)# the bare coefficients groupCtl groupTrt 5.032 4.661 > > ## The 2 basic regression diagnostic plots [plot.lm(.) is preferred] > plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's > abline(h=0, lty=2, col = 'gray') > > qqnorm(residuals(lm.D90)) > > > > cleanEx() > nameEx("lmfit") > ### * lmfit > > flush(stderr()); flush(stdout()) > > ### Name: lm.fit > ### Title: Fitter Functions for Linear Models > ### Aliases: lm.fit lm.wfit > ### Keywords: regression array > > ### ** Examples > > require(utils) > set.seed(129) > n <- 7 ; p <- 2 > X <- matrix(rnorm(n * p), n,p) # no intercept! > y <- rnorm(n) > w <- rnorm(n)^2 > > str(lmw <- lm.wfit(x=X, y=y, w=w)) List of 9 $ coefficients : Named num [1:2] -0.0432 -0.5612 ..- attr(*, "names")= chr [1:2] "x1" "x2" $ residuals : num [1:7] -0.132 -1.308 -0.256 1.468 0.439 ... $ fitted.values: num [1:7] 0.05 -0.5232 0.6151 -0.5766 0.0512 ... $ effects : Named num [1:7] 0.804 1.722 -0.072 2.047 0.392 ... ..- attr(*, "names")= chr [1:7] "x1" "x2" "" "" ... $ weights : num [1:7] 0.3195 0.0123 0.0569 1.8154 0.8359 ... $ rank : int 2 $ assign : NULL $ qr :List of 5 ..$ qr : num [1:7, 1:2] 3.0953 0.0355 0.106 0.5901 -0.5898 ... ..$ qraux: num [1:2] 1.2 1.02 ..$ pivot: int [1:2] 1 2 ..$ tol : num 1e-07 ..$ rank : int 2 ..- attr(*, "class")= chr "qr" $ df.residual : int 5 > > str(lm. <- lm.fit (x=X, y=y)) List of 8 $ coefficients : Named num [1:2] 0.132 -0.553 ..- attr(*, "names")= chr [1:2] "x1" "x2" $ residuals : num [1:7] 0.06433 -1.14333 -0.00708 1.69606 0.09095 ... $ effects : Named num [1:7] 0.791 1.476 -0.415 1.983 0.133 ... ..- attr(*, "names")= chr [1:7] "x1" "x2" "" "" ... $ rank : int 2 $ fitted.values: num [1:7] -0.146 -0.688 0.366 -0.804 0.399 ... $ assign : NULL $ qr :List of 5 ..$ qr : num [1:7, 1:2] 3.321 0.298 0.414 0.408 -0.601 ... ..$ qraux: num [1:2] 1.34 1.32 ..$ pivot: int [1:2] 1 2 ..$ tol : num 1e-07 ..$ rank : int 2 ..- attr(*, "class")= chr "qr" $ df.residual : int 5 > > > > > cleanEx() > nameEx("loess") > ### * loess > > flush(stderr()); flush(stdout()) > > ### Name: loess > ### Title: Local Polynomial Regression Fitting > ### Aliases: loess > ### Keywords: smooth loess > > ### ** Examples > > cars.lo <- loess(dist ~ speed, cars) > predict(cars.lo, data.frame(speed = seq(5, 30, 1)), se = TRUE) $fit 1 2 3 4 5 6 7 8 7.797353 10.002308 12.499786 15.281082 18.446568 21.865315 25.517015 29.350386 9 10 11 12 13 14 15 16 33.230660 37.167935 41.205226 45.055736 48.355889 49.824812 51.986702 56.461318 17 18 19 20 21 22 23 24 61.959729 68.569313 76.316068 85.212121 95.324047 NA NA NA 25 26 NA NA $se.fit 1 2 3 4 5 6 7 8 7.568120 5.945831 4.990827 4.545284 4.308639 4.115049 3.789542 3.716231 9 10 11 12 13 14 15 16 3.776947 4.091747 4.709568 4.245427 4.035929 3.753410 4.004705 4.043190 17 18 19 20 21 22 23 24 4.026105 4.074664 4.570818 5.954217 8.302014 NA NA NA 25 26 NA NA $residual.scale [1] 15.29496 $df [1] 44.6179 > # to allow extrapolation > cars.lo2 <- loess(dist ~ speed, cars, + control = loess.control(surface = "direct")) > predict(cars.lo2, data.frame(speed = seq(5, 30, 1)), se = TRUE) $fit 1 2 3 4 5 6 7 7.741006 9.926596 12.442424 15.281082 18.425712 21.865315 25.713413 8 9 10 11 12 13 14 29.350386 33.230660 37.167935 41.205226 45.781544 48.355889 50.067148 15 16 17 18 19 20 21 51.986702 56.445263 62.025404 68.569313 76.193111 85.053364 95.300523 22 23 24 25 26 106.974661 120.092581 134.665851 150.698545 168.190283 $se.fit 1 2 3 4 5 6 7 8 7.565991 5.959097 5.012013 4.550013 4.321596 4.119331 3.939804 3.720098 9 10 11 12 13 14 15 16 3.780877 4.096004 4.714469 4.398936 4.040129 4.184257 4.008873 4.061865 17 18 19 20 21 22 23 24 4.033998 4.078904 4.584606 5.952480 8.306901 11.601911 15.792480 20.864660 25 26 26.823827 33.683999 $residual.scale [1] 15.31087 $df [1] 44.55085 > > > > cleanEx() > nameEx("logLik") > ### * logLik > > flush(stderr()); flush(stdout()) > > ### Name: logLik > ### Title: Extract Log-Likelihood > ### Aliases: logLik print.logLik str.logLik logLik.lm > ### Keywords: models > > ### ** Examples > > x <- 1:5 > lmx <- lm(x ~ 1) > logLik(lmx) # using print.logLik() method 'log Lik.' -8.827561 (df=2) > utils::str(logLik(lmx)) Class 'logLik' : -8.828 (df=2) > > ## lm method > (fm1 <- lm(rating ~ ., data = attitude)) Call: lm(formula = rating ~ ., data = attitude) Coefficients: (Intercept) complaints privileges learning raises critical 10.78708 0.61319 -0.07305 0.32033 0.08173 0.03838 advance -0.21706 > logLik(fm1) 'log Lik.' -97.24991 (df=8) > logLik(fm1, REML = TRUE) 'log Lik.' -102.6851 (df=8) > > utils::data(Orthodont, package="nlme") > fm1 <- lm(distance ~ Sex * age, Orthodont) > logLik(fm1) 'log Lik.' -239.1209 (df=5) > logLik(fm1, REML = TRUE) 'log Lik.' -241.7796 (df=5) > > > > cleanEx() > nameEx("loglin") > ### * loglin > > flush(stderr()); flush(stdout()) > > ### Name: loglin > ### Title: Fitting Log-Linear Models > ### Aliases: loglin > ### Keywords: category models > > ### ** Examples > > ## Model of joint independence of sex from hair and eye color. > fm <- loglin(HairEyeColor, list(c(1, 2), c(1, 3), c(2, 3))) 5 iterations: deviation 0.04093795 > fm $lrt [1] 6.761258 $pearson [1] 6.868292 $df [1] 9 $margin $margin[[1]] [1] "Hair" "Eye" $margin[[2]] [1] "Hair" "Sex" $margin[[3]] [1] "Eye" "Sex" > 1 - pchisq(fm$lrt, fm$df) [1] 0.66196 > ## Model with no three-factor interactions fits well. > > > > cleanEx() > nameEx("lowess") > ### * lowess > > flush(stderr()); flush(stdout()) > > ### Name: lowess > ### Title: Scatter Plot Smoothing > ### Aliases: lowess > ### Keywords: smooth > > ### ** Examples > > require(graphics) > > plot(cars, main = "lowess(cars)") > lines(lowess(cars), col = 2) > lines(lowess(cars, f=.2), col = 3) > legend(5, 120, c(paste("f = ", c("2/3", ".2"))), lty = 1, col = 2:3) > > > > cleanEx() > nameEx("ls.diag") > ### * ls.diag > > flush(stderr()); flush(stdout()) > > ### Name: ls.diag > ### Title: Compute Diagnostics for 'lsfit' Regression Results > ### Aliases: ls.diag > ### Keywords: regression > > ### ** Examples > > > cleanEx() > nameEx("lsfit") > ### * lsfit > > flush(stderr()); flush(stdout()) > > ### Name: lsfit > ### Title: Find the Least Squares Fit > ### Aliases: lsfit > ### Keywords: regression > > ### ** Examples > > ## Don't show: > utils::example("lm", echo = FALSE) > ## End Don't show > ##-- Using the same data as the lm(.) example: > lsD9 <- lsfit(x = unclass(gl(2,10)), y = weight) > ls.print(lsD9) Residual Standard Error=0.6964 R-Square=0.0731 F-statistic (df=1, 18)=1.4191 p-value=0.249 Estimate Std.Err t-value Pr(>|t|) Intercept 5.403 0.4924 10.9723 0.000 X -0.371 0.3114 -1.1913 0.249 > > > > cleanEx() > nameEx("mad") > ### * mad > > flush(stderr()); flush(stdout()) > > ### Name: mad > ### Title: Median Absolute Deviation > ### Aliases: mad > ### Keywords: univar robust > > ### ** Examples > > mad(c(1:9)) [1] 2.9652 > print(mad(c(1:9), constant=1)) == + mad(c(1:8,100), constant=1) # = 2 ; TRUE [1] 2 [1] TRUE > x <- c(1,2,3, 5,7,8) > sort(abs(x - median(x))) [1] 1 1 2 3 3 4 > c(mad(x, constant=1), + mad(x, constant=1, low = TRUE), + mad(x, constant=1, high = TRUE)) [1] 2.5 2.0 3.0 > > > > cleanEx() > nameEx("mahalanobis") > ### * mahalanobis > > flush(stderr()); flush(stdout()) > > ### Name: mahalanobis > ### Title: Mahalanobis Distance > ### Aliases: mahalanobis > ### Keywords: multivariate > > ### ** Examples > > require(graphics) > > ma <- cbind(1:6, 1:3) > (S <- var(ma)) [,1] [,2] [1,] 3.5 0.8 [2,] 0.8 0.8 > mahalanobis(c(0,0), 1:2, S) [1] 5.37037 > > x <- matrix(rnorm(100*3), ncol = 3) > stopifnot(mahalanobis(x, 0, diag(ncol(x))) == rowSums(x*x)) > ##- Here, D^2 = usual squared Euclidean distances > > Sx <- cov(x) > D2 <- mahalanobis(x, colMeans(x), Sx) > plot(density(D2, bw=.5), + main="Squared Mahalanobis distances, n=100, p=3") ; rug(D2) > qqplot(qchisq(ppoints(100), df=3), D2, + main = expression("Q-Q plot of Mahalanobis" * ~D^2 * + " vs. quantiles of" * ~ chi[3]^2)) > abline(0, 1, col = 'gray') > > > > cleanEx() > nameEx("make.link") > ### * make.link > > flush(stderr()); flush(stdout()) > > ### Name: make.link > ### Title: Create a Link for GLM Families > ### Aliases: make.link > ### Keywords: models > > ### ** Examples > > utils::str(make.link("logit")) List of 5 $ linkfun :function (mu) $ linkinv :function (eta) $ mu.eta :function (eta) $ valideta:function (eta) $ name : chr "logit" - attr(*, "class")= chr "link-glm" > > > > cleanEx() > nameEx("makepredictcall") > ### * makepredictcall > > flush(stderr()); flush(stdout()) > > ### Name: makepredictcall > ### Title: Utility Function for Safe Prediction > ### Aliases: makepredictcall makepredictcall.default SafePrediction > ### Keywords: models > > ### ** Examples > > require(graphics) > > ## using poly: this did not work in R < 1.5.0 > fm <- lm(weight ~ poly(height, 2), data = women) > plot(women, xlab = "Height (in)", ylab = "Weight (lb)") > ht <- seq(57, 73, len = 200) > lines(ht, predict(fm, data.frame(height=ht))) > > ## see also example(cars) > > ## see bs and ns for spline examples. > > > > cleanEx() > nameEx("manova") > ### * manova > > flush(stderr()); flush(stdout()) > > ### Name: manova > ### Title: Multivariate Analysis of Variance > ### Aliases: manova > ### Keywords: models > > ### ** Examples > > ## From Venables and Ripley (2002) p.165. > utils::data(npk, package="MASS") > > ## Set orthogonal contrasts. > op <- options(contrasts=c("contr.helmert", "contr.poly")) > > ## Fake a 2nd response variable > npk2 <- within(npk, foo <- rnorm(24)) > ( npk2.aov <- manova(cbind(yield, foo) ~ block + N*P*K, npk2) ) Call: manova(cbind(yield, foo) ~ block + N * P * K, npk2) Terms: block N P K N:P N:K P:K resp 1 343.2950 189.2817 8.4017 95.2017 21.2817 33.1350 0.4817 resp 2 2.9548 0.0223 1.3807 0.0097 2.7585 0.9724 5.5258 Deg. of Freedom 5 1 1 1 1 1 1 Residuals resp 1 185.2867 resp 2 7.8286 Deg. of Freedom 12 Residual standard error: 3.929447 0.8077039 1 out of 13 effects not estimable Estimated effects are balanced > summary(npk2.aov) Df Pillai approx F num Df den Df Pr(>F) block 5 0.89478 1.9430 10 24 0.08861 . N 1 0.50586 5.6304 2 11 0.02071 * P 1 0.17088 1.1336 2 11 0.35677 K 1 0.34430 2.8879 2 11 0.09815 . N:P 1 0.30158 2.3750 2 11 0.13888 N:K 1 0.21654 1.5201 2 11 0.26127 P:K 1 0.41992 3.9814 2 11 0.05003 . Residuals 12 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ( npk2.aovE <- manova(cbind(yield, foo) ~ N*P*K + Error(block), npk2) ) Call: manova(cbind(yield, foo) ~ N * P * K + Error(block), npk2) Grand Means: yield foo 54.8750000 0.1498669 Stratum 1: block Terms: N:P:K Residuals resp 1 37.00167 306.29333 resp 2 0.06988 2.88496 Deg. of Freedom 1 4 Residual standard error: 8.750619 0.8492579 Estimated effects are balanced Stratum 2: Within Terms: N P K N:P N:K P:K resp 1 189.28167 8.40167 95.20167 21.28167 33.13500 0.48167 resp 2 0.02235 1.38066 0.00966 2.75852 0.97240 5.52584 Deg. of Freedom 1 1 1 1 1 1 Residuals resp 1 185.28667 resp 2 7.82863 Deg. of Freedom 12 Residual standard error: 3.929447 0.8077039 Estimated effects are balanced > summary(npk2.aovE) Error: block Df Pillai approx F num Df den Df Pr(>F) N:P:K 1 0.20004 0.3751 2 3 0.7155 Residuals 4 Error: Within Df Pillai approx F num Df den Df Pr(>F) N 1 0.50586 5.6304 2 11 0.02071 * P 1 0.17088 1.1336 2 11 0.35677 K 1 0.34430 2.8879 2 11 0.09815 . N:P 1 0.30158 2.3750 2 11 0.13888 N:K 1 0.21654 1.5201 2 11 0.26127 P:K 1 0.41992 3.9814 2 11 0.05003 . Residuals 12 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("mantelhaen.test") > ### * mantelhaen.test > > flush(stderr()); flush(stdout()) > > ### Name: mantelhaen.test > ### Title: Cochran-Mantel-Haenszel Chi-Squared Test for Count Data > ### Aliases: mantelhaen.test > ### Keywords: htest > > ### ** Examples > > ## Agresti (1990), pages 231--237, Penicillin and Rabbits > ## Investigation of the effectiveness of immediately injected or 1.5 > ## hours delayed penicillin in protecting rabbits against a lethal > ## injection with beta-hemolytic streptococci. > Rabbits <- + array(c(0, 0, 6, 5, + 3, 0, 3, 6, + 6, 2, 0, 4, + 5, 6, 1, 0, + 2, 5, 0, 0), + dim = c(2, 2, 5), + dimnames = list( + Delay = c("None", "1.5h"), + Response = c("Cured", "Died"), + Penicillin.Level = c("1/8", "1/4", "1/2", "1", "4"))) > Rabbits , , Penicillin.Level = 1/8 Response Delay Cured Died None 0 6 1.5h 0 5 , , Penicillin.Level = 1/4 Response Delay Cured Died None 3 3 1.5h 0 6 , , Penicillin.Level = 1/2 Response Delay Cured Died None 6 0 1.5h 2 4 , , Penicillin.Level = 1 Response Delay Cured Died None 5 1 1.5h 6 0 , , Penicillin.Level = 4 Response Delay Cured Died None 2 0 1.5h 5 0 > ## Classical Mantel-Haenszel test > mantelhaen.test(Rabbits) Mantel-Haenszel chi-squared test with continuity correction data: Rabbits Mantel-Haenszel X-squared = 3.9286, df = 1, p-value = 0.04747 alternative hypothesis: true common odds ratio is not equal to 1 95 percent confidence interval: 1.026713 47.725133 sample estimates: common odds ratio 7 > ## => p = 0.047, some evidence for higher cure rate of immediate > ## injection > ## Exact conditional test > mantelhaen.test(Rabbits, exact = TRUE) Exact conditional test of independence in 2 x 2 x k tables data: Rabbits S = 16, p-value = 0.03994 alternative hypothesis: true common odds ratio is not equal to 1 95 percent confidence interval: 1.077401 529.837399 sample estimates: common odds ratio 10.36102 > ## => p - 0.040 > ## Exact conditional test for one-sided alternative of a higher > ## cure rate for immediate injection > mantelhaen.test(Rabbits, exact = TRUE, alternative = "greater") Exact conditional test of independence in 2 x 2 x k tables data: Rabbits S = 16, p-value = 0.01997 alternative hypothesis: true common odds ratio is greater than 1 95 percent confidence interval: 1.384239 Inf sample estimates: common odds ratio 10.36102 > ## => p = 0.020 > > ## UC Berkeley Student Admissions > mantelhaen.test(UCBAdmissions) Mantel-Haenszel chi-squared test with continuity correction data: UCBAdmissions Mantel-Haenszel X-squared = 1.4269, df = 1, p-value = 0.2323 alternative hypothesis: true common odds ratio is not equal to 1 95 percent confidence interval: 0.7719074 1.0603298 sample estimates: common odds ratio 0.9046968 > ## No evidence for association between admission and gender > ## when adjusted for department. However, > apply(UCBAdmissions, 3, function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1])) A B C D E F 0.3492120 0.8025007 1.1330596 0.9212838 1.2216312 0.8278727 > ## This suggests that the assumption of homogeneous (conditional) > ## odds ratios may be violated. The traditional approach would be > ## using the Woolf test for interaction: > woolf <- function(x) { + x <- x + 1 / 2 + k <- dim(x)[3] + or <- apply(x, 3, function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1])) + w <- apply(x, 3, function(x) 1 / sum(1 / x)) + 1 - pchisq(sum(w * (log(or) - weighted.mean(log(or), w)) ^ 2), k - 1) + } > woolf(UCBAdmissions) [1] 0.0034272 > ## => p = 0.003, indicating that there is significant heterogeneity. > ## (And hence the Mantel-Haenszel test cannot be used.) > > ## Agresti (2002), p. 287f and p. 297. > ## Job Satisfaction example. > Satisfaction <- + as.table(array(c(1, 2, 0, 0, 3, 3, 1, 2, + 11, 17, 8, 4, 2, 3, 5, 2, + 1, 0, 0, 0, 1, 3, 0, 1, + 2, 5, 7, 9, 1, 1, 3, 6), + dim = c(4, 4, 2), + dimnames = + list(Income = + c("<5000", "5000-15000", + "15000-25000", ">25000"), + "Job Satisfaction" = + c("V_D", "L_S", "M_S", "V_S"), + Gender = c("Female", "Male")))) > ## (Satisfaction categories abbreviated for convenience.) > ftable(. ~ Gender + Income, Satisfaction) Job Satisfaction V_D L_S M_S V_S Gender Income Female <5000 1 3 11 2 5000-15000 2 3 17 3 15000-25000 0 1 8 5 >25000 0 2 4 2 Male <5000 1 1 2 1 5000-15000 0 3 5 1 15000-25000 0 0 7 3 >25000 0 1 9 6 > ## Table 7.8 in Agresti (2002), p. 288. > mantelhaen.test(Satisfaction) Cochran-Mantel-Haenszel test data: Satisfaction Cochran-Mantel-Haenszel M^2 = 10.2001, df = 9, p-value = 0.3345 > ## See Table 7.12 in Agresti (2002), p. 297. > > > > cleanEx() > nameEx("mauchly.test") > ### * mauchly.test > > flush(stderr()); flush(stdout()) > > ### Name: mauchly.test > ### Title: Mauchly's Test of Sphericity > ### Aliases: mauchly.test mauchly.test.SSD mauchly.test.mlm > ### Keywords: htest models multivariate > > ### ** Examples > > utils::example(SSD) # Brings in the mlmfit and reacttime objects SSD> # Lifted from Baron+Li: SSD> # "Notes on the use of R for psychology experiments and questionnaires" SSD> # Maxwell and Delaney, p. 497 SSD> reacttime <- matrix(c( SSD+ 420, 420, 480, 480, 600, 780, SSD+ 420, 480, 480, 360, 480, 600, SSD+ 480, 480, 540, 660, 780, 780, SSD+ 420, 540, 540, 480, 780, 900, SSD+ 540, 660, 540, 480, 660, 720, SSD+ 360, 420, 360, 360, 480, 540, SSD+ 480, 480, 600, 540, 720, 840, SSD+ 480, 600, 660, 540, 720, 900, SSD+ 540, 600, 540, 480, 720, 780, SSD+ 480, 420, 540, 540, 660, 780), SSD+ ncol = 6, byrow = TRUE, SSD+ dimnames=list(subj=1:10, SSD+ cond=c("deg0NA", "deg4NA", "deg8NA", SSD+ "deg0NP", "deg4NP", "deg8NP"))) SSD> mlmfit <- lm(reacttime~1) SSD> SSD(mlmfit) $SSD cond cond deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP deg0NA 29160 30600 26640 23760 32400 25560 deg4NA 30600 66600 32400 7200 36000 30600 deg8NA 26640 32400 56160 41040 57600 69840 deg0NP 23760 7200 41040 70560 72000 63360 deg4NP 32400 36000 57600 72000 108000 100800 deg8NP 25560 30600 69840 63360 100800 122760 $call lm(formula = reacttime ~ 1) $df [1] 9 attr(,"class") [1] "SSD" SSD> estVar(mlmfit) cond cond deg0NA deg4NA deg8NA deg0NP deg4NP deg8NP deg0NA 3240 3400 2960 2640 3600 2840 deg4NA 3400 7400 3600 800 4000 3400 deg8NA 2960 3600 6240 4560 6400 7760 deg0NP 2640 800 4560 7840 8000 7040 deg4NP 3600 4000 6400 8000 12000 11200 deg8NP 2840 3400 7760 7040 11200 13640 > > ### traditional test of intrasubj. contrasts > mauchly.test(mlmfit, X=~1) Mauchly's test of sphericity Contrasts orthogonal to ~1 data: SSD matrix from lm(formula = reacttime ~ 1) W = 0.0311, p-value = 0.04765 > > ### tests using intra-subject 3x2 design > idata <- data.frame(deg=gl(3,1,6, labels=c(0,4,8)), + noise=gl(2,3,6, labels=c("A","P"))) > mauchly.test(mlmfit, X = ~ deg + noise, idata = idata) Mauchly's test of sphericity Contrasts orthogonal to ~deg + noise data: SSD matrix from lm(formula = reacttime ~ 1) W = 0.8938, p-value = 0.6381 > mauchly.test(mlmfit, M = ~ deg + noise, X = ~ noise, idata=idata) Mauchly's test of sphericity Contrasts orthogonal to ~noise Contrasts spanned by ~deg + noise data: SSD matrix from lm(formula = reacttime ~ 1) W = 0.9601, p-value = 0.8497 > > > > cleanEx() > nameEx("mcnemar.test") > ### * mcnemar.test > > flush(stderr()); flush(stdout()) > > ### Name: mcnemar.test > ### Title: McNemar's Chi-squared Test for Count Data > ### Aliases: mcnemar.test > ### Keywords: htest > > ### ** Examples > > ## Agresti (1990), p. 350. > ## Presidential Approval Ratings. > ## Approval of the President's performance in office in two surveys, > ## one month apart, for a random sample of 1600 voting-age Americans. > Performance <- + matrix(c(794, 86, 150, 570), + nrow = 2, + dimnames = list("1st Survey" = c("Approve", "Disapprove"), + "2nd Survey" = c("Approve", "Disapprove"))) > Performance 2nd Survey 1st Survey Approve Disapprove Approve 794 150 Disapprove 86 570 > mcnemar.test(Performance) McNemar's Chi-squared test with continuity correction data: Performance McNemar's chi-squared = 16.8178, df = 1, p-value = 4.115e-05 > ## => significant change (in fact, drop) in approval ratings > > > > cleanEx() > nameEx("median") > ### * median > > flush(stderr()); flush(stdout()) > > ### Name: median > ### Title: Median Value > ### Aliases: median median.default > ### Keywords: univar robust > > ### ** Examples > > median(1:4)# = 2.5 [even number] [1] 2.5 > median(c(1:3,100,1000))# = 3 [odd, robust] [1] 3 > > > > cleanEx() > nameEx("medpolish") > ### * medpolish > > flush(stderr()); flush(stdout()) > > ### Name: medpolish > ### Title: Median Polish of a Matrix > ### Aliases: medpolish > ### Keywords: robust > > ### ** Examples > > require(graphics) > > ## Deaths from sport parachuting; from ABC of EDA, p.224: > deaths <- + rbind(c(14,15,14), + c( 7, 4, 7), + c( 8, 2,10), + c(15, 9,10), + c( 0, 2, 0)) > dimnames(deaths) <- list(c("1-24", "25-74", "75-199", "200++", "NA"), + paste(1973:1975)) > deaths 1973 1974 1975 1-24 14 15 14 25-74 7 4 7 75-199 8 2 10 200++ 15 9 10 NA 0 2 0 > (med.d <- medpolish(deaths)) 1 : 19 Final: 19 Median Polish Results (Dataset: "deaths") Overall: 8 Row Effects: 1-24 25-74 75-199 200++ NA 6 -1 0 2 -8 Column Effects: 1973 1974 1975 0 -1 0 Residuals: 1973 1974 1975 1-24 0 2 0 25-74 0 -2 0 75-199 0 -5 2 200++ 5 0 0 NA 0 3 0 > plot(med.d) > ## Check decomposition: > all(deaths == + med.d$overall + outer(med.d$row,med.d$col, "+") + med.d$residuals) [1] TRUE > > > > cleanEx() > nameEx("model.extract") > ### * model.extract > > flush(stderr()); flush(stdout()) > > ### Name: model.extract > ### Title: Extract Components from a Model Frame > ### Aliases: model.extract model.offset model.response model.weights > ### Keywords: manip programming models > > ### ** Examples > > a <- model.frame(cbind(ncases,ncontrols) ~ agegp+tobgp+alcgp, data=esoph) > model.extract(a, "response") ncases ncontrols 1 0 40 2 0 10 3 0 6 4 0 5 5 0 27 6 0 7 7 0 4 8 0 7 9 0 2 10 0 1 11 0 2 12 0 1 13 1 1 14 0 1 15 0 2 16 0 60 17 1 14 18 0 7 19 0 8 20 0 35 21 3 23 22 1 14 23 0 8 24 0 11 25 0 6 26 0 2 27 0 1 28 2 3 29 0 3 30 2 4 31 1 46 32 0 18 33 0 10 34 0 4 35 6 38 36 4 21 37 5 15 38 5 7 39 3 16 40 6 14 41 1 5 42 2 4 43 4 4 44 3 4 45 2 3 46 4 4 47 2 49 48 3 22 49 3 12 50 4 6 51 9 40 52 6 21 53 4 17 54 3 6 55 9 18 56 8 15 57 3 6 58 4 4 59 5 10 60 6 7 61 2 3 62 5 6 63 5 48 64 4 14 65 2 7 66 0 2 67 17 34 68 3 10 69 5 9 70 6 13 71 4 12 72 2 3 73 1 1 74 3 4 75 1 2 76 1 1 77 1 1 78 1 18 79 2 6 80 1 3 81 2 5 82 1 3 83 0 3 84 1 1 85 1 1 86 1 1 87 2 2 88 1 1 > stopifnot(model.extract(a, "response") == model.response(a)) > > a <- model.frame(ncases/(ncases+ncontrols) ~ agegp+tobgp+alcgp, + data = esoph, weights = ncases+ncontrols) > model.response(a) 1 2 3 4 5 6 7 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 8 9 10 11 12 13 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.50000000 0.00000000 15 16 17 18 19 20 21 0.00000000 0.00000000 0.06666667 0.00000000 0.00000000 0.00000000 0.11538462 22 23 24 25 26 27 28 0.06666667 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.40000000 29 30 31 32 33 34 35 0.00000000 0.33333333 0.02127660 0.00000000 0.00000000 0.00000000 0.13636364 36 37 38 39 40 41 42 0.16000000 0.25000000 0.41666667 0.15789474 0.30000000 0.16666667 0.33333333 43 44 45 46 47 48 49 0.50000000 0.42857143 0.40000000 0.50000000 0.03921569 0.12000000 0.20000000 50 51 52 53 54 55 56 0.40000000 0.18367347 0.22222222 0.19047619 0.33333333 0.33333333 0.34782609 57 58 59 60 61 62 63 0.33333333 0.50000000 0.33333333 0.46153846 0.40000000 0.45454545 0.09433962 64 65 66 67 68 69 70 0.22222222 0.22222222 0.00000000 0.33333333 0.23076923 0.35714286 0.31578947 71 72 73 74 75 76 77 0.25000000 0.40000000 0.50000000 0.42857143 0.33333333 0.50000000 0.50000000 78 79 80 81 82 83 84 0.05263158 0.25000000 0.25000000 0.28571429 0.25000000 0.00000000 0.50000000 85 86 87 88 0.50000000 0.50000000 0.50000000 0.50000000 > model.extract(a, "weights") 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 40 10 6 5 27 7 4 7 2 1 2 1 2 1 2 60 15 7 8 35 26 15 8 11 6 2 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 1 5 3 6 47 18 10 4 44 25 20 12 19 20 6 6 8 7 5 8 51 25 15 10 49 27 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 21 9 27 23 9 8 15 13 5 11 53 18 9 2 51 13 14 19 16 5 2 7 3 2 2 19 79 80 81 82 83 84 85 86 87 88 8 4 7 4 3 2 2 2 4 2 > > a <- model.frame(cbind(ncases,ncontrols) ~ agegp, + something = tobgp, data = esoph) > names(a) [1] "cbind(ncases, ncontrols)" "agegp" [3] "(something)" > stopifnot(model.extract(a, "something") == esoph$tobgp) > > > > cleanEx() > nameEx("model.frame") > ### * model.frame > > flush(stderr()); flush(stdout()) > > ### Name: model.frame > ### Title: Extracting the Model Frame from a Formula or Fit > ### Aliases: model.frame model.frame.default model.frame.lm model.frame.glm > ### model.frame.aovlist get_all_vars > ### Keywords: models > > ### ** Examples > > data.class(model.frame(dist ~ speed, data = cars)) [1] "data.frame" > > > > cleanEx() > nameEx("model.matrix") > ### * model.matrix > > flush(stderr()); flush(stdout()) > > ### Name: model.matrix > ### Title: Construct Design Matrices > ### Aliases: model.matrix model.matrix.default model.matrix.lm > ### Keywords: models > > ### ** Examples > > ff <- log(Volume) ~ log(Height) + log(Girth) > utils::str(m <- model.frame(ff, trees)) 'data.frame': 31 obs. of 3 variables: $ log(Volume): num 2.33 2.33 2.32 2.8 2.93 ... $ log(Height): num 4.25 4.17 4.14 4.28 4.39 ... $ log(Girth) : num 2.12 2.15 2.17 2.35 2.37 ... - attr(*, "terms")=Classes 'terms', 'formula' length 3 log(Volume) ~ log(Height) + log(Girth) .. ..- attr(*, "variables")= language list(log(Volume), log(Height), log(Girth)) .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1 .. .. ..- attr(*, "dimnames")=List of 2 .. .. .. ..$ : chr [1:3] "log(Volume)" "log(Height)" "log(Girth)" .. .. .. ..$ : chr [1:2] "log(Height)" "log(Girth)" .. ..- attr(*, "term.labels")= chr [1:2] "log(Height)" "log(Girth)" .. ..- attr(*, "order")= int [1:2] 1 1 .. ..- attr(*, "intercept")= int 1 .. ..- attr(*, "response")= int 1 .. ..- attr(*, ".Environment")= .. ..- attr(*, "predvars")= language list(log(Volume), log(Height), log(Girth)) .. ..- attr(*, "dataClasses")= Named chr [1:3] "numeric" "numeric" "numeric" .. .. ..- attr(*, "names")= chr [1:3] "log(Volume)" "log(Height)" "log(Girth)" > mat <- model.matrix(ff, m) > > dd <- data.frame(a = gl(3,4), b = gl(4,1,12)) # balanced 2-way > options("contrasts") $contrasts unordered ordered "contr.treatment" "contr.poly" > model.matrix(~ a + b, dd) (Intercept) a2 a3 b2 b3 b4 1 1 0 0 0 0 0 2 1 0 0 1 0 0 3 1 0 0 0 1 0 4 1 0 0 0 0 1 5 1 1 0 0 0 0 6 1 1 0 1 0 0 7 1 1 0 0 1 0 8 1 1 0 0 0 1 9 1 0 1 0 0 0 10 1 0 1 1 0 0 11 1 0 1 0 1 0 12 1 0 1 0 0 1 attr(,"assign") [1] 0 1 1 2 2 2 attr(,"contrasts") attr(,"contrasts")$a [1] "contr.treatment" attr(,"contrasts")$b [1] "contr.treatment" > model.matrix(~ a + b, dd, contrasts = list(a="contr.sum")) (Intercept) a1 a2 b2 b3 b4 1 1 1 0 0 0 0 2 1 1 0 1 0 0 3 1 1 0 0 1 0 4 1 1 0 0 0 1 5 1 0 1 0 0 0 6 1 0 1 1 0 0 7 1 0 1 0 1 0 8 1 0 1 0 0 1 9 1 -1 -1 0 0 0 10 1 -1 -1 1 0 0 11 1 -1 -1 0 1 0 12 1 -1 -1 0 0 1 attr(,"assign") [1] 0 1 1 2 2 2 attr(,"contrasts") attr(,"contrasts")$a [1] "contr.sum" attr(,"contrasts")$b [1] "contr.treatment" > model.matrix(~ a + b, dd, contrasts = list(a="contr.sum", b="contr.poly")) (Intercept) a1 a2 b.L b.Q b.C 1 1 1 0 -0.6708204 0.5 -0.2236068 2 1 1 0 -0.2236068 -0.5 0.6708204 3 1 1 0 0.2236068 -0.5 -0.6708204 4 1 1 0 0.6708204 0.5 0.2236068 5 1 0 1 -0.6708204 0.5 -0.2236068 6 1 0 1 -0.2236068 -0.5 0.6708204 7 1 0 1 0.2236068 -0.5 -0.6708204 8 1 0 1 0.6708204 0.5 0.2236068 9 1 -1 -1 -0.6708204 0.5 -0.2236068 10 1 -1 -1 -0.2236068 -0.5 0.6708204 11 1 -1 -1 0.2236068 -0.5 -0.6708204 12 1 -1 -1 0.6708204 0.5 0.2236068 attr(,"assign") [1] 0 1 1 2 2 2 attr(,"contrasts") attr(,"contrasts")$a [1] "contr.sum" attr(,"contrasts")$b [1] "contr.poly" > m.orth <- model.matrix(~a+b, dd, contrasts = list(a="contr.helmert")) > crossprod(m.orth) # m.orth is ALMOST orthogonal (Intercept) a1 a2 b2 b3 b4 (Intercept) 12 0 0 3 3 3 a1 0 8 0 0 0 0 a2 0 0 24 0 0 0 b2 3 0 0 3 0 0 b3 3 0 0 0 3 0 b4 3 0 0 0 0 3 > > > > cleanEx() > nameEx("model.tables") > ### * model.tables > > flush(stderr()); flush(stdout()) > > ### Name: model.tables > ### Title: Compute Tables of Results from an Aov Model Fit > ### Aliases: model.tables model.tables.aov model.tables.aovlist > ### Keywords: models > > ### ** Examples > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("monthplot") > ### * monthplot > > flush(stderr()); flush(stdout()) > > ### Name: monthplot > ### Title: Plot a Seasonal or other Subseries from a Time Series > ### Aliases: monthplot monthplot.default monthplot.ts monthplot.stl > ### monthplot.StructTS > ### Keywords: hplot ts > > ### ** Examples > > require(graphics) > > ## The CO2 data > fit <- stl(log(co2), s.window = 20, t.window = 20) > plot(fit) > op <- par(mfrow = c(2,2)) > monthplot(co2, ylab = "data", cex.axis = 0.8) > monthplot(fit, choice = "seasonal", cex.axis = 0.8) > monthplot(fit, choice = "trend", cex.axis = 0.8) > monthplot(fit, choice = "remainder", type = "h", cex.axis = 0.8) > par(op) > > ## The CO2 data, grouped quarterly > quarter <- (cycle(co2) - 1) %/% 3 > monthplot(co2, phase = quarter) > > ## see also JohnsonJohnson > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("mood.test") > ### * mood.test > > flush(stderr()); flush(stdout()) > > ### Name: mood.test > ### Title: Mood Two-Sample Test of Scale > ### Aliases: mood.test mood.test.default mood.test.formula > ### Keywords: htest > > ### ** Examples > > ## Same data as for the Ansari-Bradley test: > ## Serum iron determination using Hyland control sera > ramsay <- c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99, + 101, 96, 97, 102, 107, 113, 116, 113, 110, 98) > jung.parekh <- c(107, 108, 106, 98, 105, 103, 110, 105, 104, + 100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99) > mood.test(ramsay, jung.parekh) Mood two-sample test of scale data: ramsay and jung.parekh Z = 1.0371, p-value = 0.2997 alternative hypothesis: two.sided > ## Compare this to ansari.test(ramsay, jung.parekh) > > > > cleanEx() > nameEx("na.action") > ### * na.action > > flush(stderr()); flush(stdout()) > > ### Name: na.action > ### Title: NA Action > ### Aliases: na.action na.action.default > ### Keywords: NA methods > > ### ** Examples > > na.action(na.omit(c(1, NA))) [1] 2 attr(,"class") [1] "omit" > > > > cleanEx() > nameEx("na.contiguous") > ### * na.contiguous > > flush(stderr()); flush(stdout()) > > ### Name: na.contiguous > ### Title: Find Longest Contiguous Stretch of non-NAs > ### Aliases: na.contiguous na.contiguous.default > ### Keywords: ts > > ### ** Examples > > na.contiguous(presidents) Qtr1 Qtr2 Qtr3 Qtr4 1952 32 1953 59 74 75 60 1954 71 61 71 57 1955 71 68 79 73 1956 76 71 67 75 1957 79 62 63 57 1958 60 49 48 52 1959 57 62 61 66 1960 71 62 61 57 1961 72 83 71 78 1962 79 71 62 74 1963 76 64 62 57 1964 80 73 69 69 1965 71 64 69 62 1966 63 46 56 44 1967 44 52 38 46 1968 36 49 35 44 1969 59 65 65 56 1970 66 53 61 52 1971 51 48 54 49 1972 49 61 > > > > cleanEx() > nameEx("na.fail") > ### * na.fail > > flush(stderr()); flush(stdout()) > > ### Name: na.fail > ### Title: Handle Missing Values in Objects > ### Aliases: na.fail na.fail.default na.omit na.omit.data.frame > ### na.omit.default na.exclude na.exclude.data.frame na.exclude.default > ### na.pass > ### Keywords: NA > > ### ** Examples > > DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA)) > na.omit(DF) x y 1 1 0 2 2 10 > m <- as.matrix(DF) > na.omit(m) x y [1,] 1 0 [2,] 2 10 attr(,"na.action") [1] 3 attr(,"class") [1] "omit" > stopifnot(all(na.omit(1:3) == 1:3)) # does not affect objects with no NA's > try(na.fail(DF))#> Error: missing values in ... Error in na.fail.default(DF) : missing values in object > > options("na.action") $na.action [1] "na.omit" > > > > cleanEx() > nameEx("nextn") > ### * nextn > > flush(stderr()); flush(stdout()) > > ### Name: nextn > ### Title: Highly Composite Numbers > ### Aliases: nextn > ### Keywords: math > > ### ** Examples > > nextn(1001) # 1024 [1] 1024 > table(sapply(599:630, nextn)) 600 625 640 2 25 5 > > > > cleanEx() > nameEx("nlm") > ### * nlm > > flush(stderr()); flush(stdout()) > > ### Name: nlm > ### Title: Non-Linear Minimization > ### Aliases: nlm > ### Keywords: nonlinear optimize > > ### ** Examples > > f <- function(x) sum((x-1:length(x))^2) > nlm(f, c(10,10)) $minimum [1] 4.303458e-26 $estimate [1] 1 2 $gradient [1] 2.757794e-13 -3.099743e-13 $code [1] 1 $iterations [1] 2 > nlm(f, c(10,10), print.level = 2) iteration = 0 Step: [1] 0 0 Parameter: [1] 10 10 Function Value [1] 145 Gradient: [1] 18.00001 16.00001 iteration = 1 Step: [1] -9 -8 Parameter: [1] 1 2 Function Value [1] 1.721748e-13 Gradient: [1] 1.551336e-06 1.379735e-06 iteration = 2 Parameter: [1] 1 2 Function Value [1] 4.303458e-26 Gradient: [1] 2.757794e-13 -3.099743e-13 Relative gradient close to zero. Current iterate is probably solution. $minimum [1] 4.303458e-26 $estimate [1] 1 2 $gradient [1] 2.757794e-13 -3.099743e-13 $code [1] 1 $iterations [1] 2 > utils::str(nlm(f, c(5), hessian = TRUE)) List of 6 $ minimum : num 2.44e-24 $ estimate : num 1 $ gradient : num 1e-06 $ hessian : num [1, 1] 2 $ code : int 1 $ iterations: int 1 > > f <- function(x, a) sum((x-a)^2) > nlm(f, c(10,10), a=c(3,5)) $minimum [1] 3.371781e-25 $estimate [1] 3 5 $gradient [1] 6.750156e-13 -9.450218e-13 $code [1] 1 $iterations [1] 2 > f <- function(x, a) + { + res <- sum((x-a)^2) + attr(res, "gradient") <- 2*(x-a) + res + } > nlm(f, c(10,10), a=c(3,5)) $minimum [1] 0 $estimate [1] 3 5 $gradient [1] 0 0 $code [1] 1 $iterations [1] 1 > > ## more examples, including the use of derivatives. > ## Not run: demo(nlm) > > > > cleanEx() > nameEx("nlminb") > ### * nlminb > > flush(stderr()); flush(stdout()) > > ### Name: nlminb > ### Title: Optimization using PORT routines > ### Aliases: nlminb > ### Keywords: optimize > > ### ** Examples > > > cleanEx() > nameEx("nls") > ### * nls > > flush(stderr()); flush(stdout()) > > ### Name: nls > ### Title: Nonlinear Least Squares > ### Aliases: nls > ### Keywords: nonlinear regression models > > ### ** Examples > > ## Don't show: > od <- options(digits=5) > ## End Don't show > require(graphics) > > DNase1 <- subset(DNase, Run == 1) > > ## using a selfStart model > fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) > summary(fm1DNase1) Formula: density ~ SSlogis(log(conc), Asym, xmid, scal) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 2.3452 0.0782 30.0 2.2e-13 *** xmid 1.4831 0.0814 18.2 1.2e-10 *** scal 1.0415 0.0323 32.3 8.5e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0192 on 13 degrees of freedom > ## the coefficients only: > coef(fm1DNase1) Asym xmid scal 2.3452 1.4831 1.0415 > ## including their SE, etc: > coef(summary(fm1DNase1)) Estimate Std. Error t value Pr(>|t|) Asym 2.3452 0.078154 30.007 2.1655e-13 xmid 1.4831 0.081353 18.230 1.2185e-10 scal 1.0415 0.032271 32.272 8.5069e-14 > > ## using conditional linearity > fm2DNase1 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)), + data = DNase1, + start = list(xmid = 0, scal = 1), + algorithm = "plinear") > summary(fm2DNase1) Formula: density ~ 1/(1 + exp((xmid - log(conc))/scal)) Parameters: Estimate Std. Error t value Pr(>|t|) xmid 1.4831 0.0814 18.2 1.2e-10 *** scal 1.0415 0.0323 32.3 8.5e-14 *** .lin 2.3452 0.0782 30.0 2.2e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0192 on 13 degrees of freedom > > ## without conditional linearity > fm3DNase1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)), + data = DNase1, + start = list(Asym = 3, xmid = 0, scal = 1)) > summary(fm3DNase1) Formula: density ~ Asym/(1 + exp((xmid - log(conc))/scal)) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 2.3452 0.0782 30.0 2.2e-13 *** xmid 1.4831 0.0814 18.2 1.2e-10 *** scal 1.0415 0.0323 32.3 8.5e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0192 on 13 degrees of freedom > > ## using Port's nl2sol algorithm > fm4DNase1 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)), + data = DNase1, + start = list(Asym = 3, xmid = 0, scal = 1), + algorithm = "port") > summary(fm4DNase1) Formula: density ~ Asym/(1 + exp((xmid - log(conc))/scal)) Parameters: Estimate Std. Error t value Pr(>|t|) Asym 2.3452 0.0782 30.0 2.2e-13 *** xmid 1.4831 0.0814 18.2 1.2e-10 *** scal 1.0415 0.0323 32.3 8.5e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0192 on 13 degrees of freedom Algorithm "port", convergence message: relative convergence (4) > > ## weighted nonlinear regression > Treated <- Puromycin[Puromycin$state == "treated", ] > weighted.MM <- function(resp, conc, Vm, K) + { + ## Purpose: exactly as white book p. 451 -- RHS for nls() + ## Weighted version of Michaelis-Menten model + ## ---------------------------------------------------------- + ## Arguments: 'y', 'x' and the two parameters (see book) + ## ---------------------------------------------------------- + ## Author: Martin Maechler, Date: 23 Mar 2001 + + pred <- (Vm * conc)/(K + conc) + (resp - pred) / sqrt(pred) + } > > Pur.wt <- nls( ~ weighted.MM(rate, conc, Vm, K), data = Treated, + start = list(Vm = 200, K = 0.1)) > summary(Pur.wt) Formula: 0 ~ weighted.MM(rate, conc, Vm, K) Parameters: Estimate Std. Error t value Pr(>|t|) Vm 2.07e+02 9.22e+00 22.42 7.0e-10 *** K 5.46e-02 7.98e-03 6.84 4.5e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.21 on 10 degrees of freedom > > ## Passing arguments using a list that can not be coerced to a data.frame > lisTreat <- with(Treated, + list(conc1 = conc[1], conc.1 = conc[-1], rate = rate)) > > weighted.MM1 <- function(resp, conc1, conc.1, Vm, K) + { + conc <- c(conc1, conc.1) + pred <- (Vm * conc)/(K + conc) + (resp - pred) / sqrt(pred) + } > Pur.wt1 <- nls( ~ weighted.MM1(rate, conc1, conc.1, Vm, K), + data = lisTreat, start = list(Vm = 200, K = 0.1)) > stopifnot(all.equal(coef(Pur.wt), coef(Pur.wt1))) > > ## Chambers and Hastie (1992) Statistical Models in S (p. 537): > ## If the value of the right side [of formula] has an attribute called > ## 'gradient' this should be a matrix with the number of rows equal > ## to the length of the response and one column for each parameter. > > weighted.MM.grad <- function(resp, conc1, conc.1, Vm, K) + { + conc <- c(conc1, conc.1) + + K.conc <- K+conc + dy.dV <- conc/K.conc + dy.dK <- -Vm*dy.dV/K.conc + pred <- Vm*dy.dV + pred.5 <- sqrt(pred) + dev <- (resp - pred) / pred.5 + Ddev <- -0.5*(resp+pred)/(pred.5*pred) + attr(dev, "gradient") <- Ddev * cbind(Vm = dy.dV, K = dy.dK) + dev + } > > Pur.wt.grad <- nls( ~ weighted.MM.grad(rate, conc1, conc.1, Vm, K), + data = lisTreat, start = list(Vm = 200, K = 0.1)) > > rbind(coef(Pur.wt), coef(Pur.wt1), coef(Pur.wt.grad)) Vm K [1,] 206.83 0.054611 [2,] 206.83 0.054611 [3,] 206.83 0.054611 > > ## In this example, there seems no advantage to providing the gradient. > ## In other cases, there might be. > > > ## The two examples below show that you can fit a model to > ## artificial data with noise but not to artificial data > ## without noise. > x <- 1:10 > y <- 2*x + 3 # perfect fit > yeps <- y + rnorm(length(y), sd = 0.01) # added noise > nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321)) Nonlinear regression model model: yeps ~ a + b * x data: parent.frame() a b 3 2 residual sum-of-squares: 0.000524 > ## Not run: > ##D ## terminates in an error, because convergence cannot be confirmed: > ##D nls(y ~ a + b*x, start = list(a = 0.12345, b = 0.54321)) > ## End(Not run) > > ## the nls() internal cheap guess for starting values can be sufficient: > > x <- -(1:100)/10 > y <- 100 + 10 * exp(x / 2) + rnorm(x)/10 > nlmod <- nls(y ~ Const + A * exp(B * x)) Warning in nls(y ~ Const + A * exp(B * x)) : No starting values specified for some parameters. Initializing ‘Const’, ‘A’, ‘B’ to '1.'. Consider specifying 'start' or using a selfStart model > > plot(x,y, main = "nls(*), data, true function and fit, n=100") > curve(100 + 10 * exp(x / 2), col=4, add = TRUE) > lines(x, predict(nlmod), col=2) > > > ## The muscle dataset in MASS is from an experiment on muscle > ## contraction on 21 animals. The observed variables are Strip > ## (identifier of muscle), Conc (Cacl concentration) and Length > ## (resulting length of muscle section). > utils::data(muscle, package = "MASS") > > ## The non linear model considered is > ## Length = alpha + beta*exp(-Conc/theta) + error > ## where theta is constant but alpha and beta may vary with Strip. > > with(muscle, table(Strip)) # 2,3 or 4 obs per strip Strip S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 4 4 4 3 3 3 2 2 2 2 3 2 2 2 2 4 4 3 3 3 S21 3 > > ## We first use the plinear algorithm to fit an overall model, > ## ignoring that alpha and beta might vary with Strip. > > musc.1 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle, + start = list(th=1), algorithm="plinear") > summary(musc.1) Formula: Length ~ cbind(1, exp(-Conc/th)) Parameters: Estimate Std. Error t value Pr(>|t|) th 0.608 0.115 5.31 1.9e-06 *** .lin1 28.963 1.230 23.55 < 2e-16 *** .lin2 -34.227 3.793 -9.02 1.4e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 4.67 on 57 degrees of freedom > > ## Then we use nls' indexing feature for parameters in non-linear > ## models to use the conventional algorithm to fit a model in which > ## alpha and beta vary with Strip. The starting values are provided > ## by the previously fitted model. > ## Note that with indexed parameters, the starting values must be > ## given in a list (with names): > b <- coef(musc.1) > musc.2 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), + muscle, + start = list(a=rep(b[2],21), b=rep(b[3],21), th=b[1])) > summary(musc.2) Formula: Length ~ a[Strip] + b[Strip] * exp(-Conc/th) Parameters: Estimate Std. Error t value Pr(>|t|) a1 23.454 0.796 29.46 5.0e-16 *** a2 28.302 0.793 35.70 < 2e-16 *** a3 30.801 1.716 17.95 1.7e-12 *** a4 25.921 3.016 8.60 1.4e-07 *** a5 23.201 2.891 8.02 3.5e-07 *** a6 20.120 2.435 8.26 2.3e-07 *** a7 33.595 1.682 19.98 3.0e-13 *** a8 39.053 3.753 10.41 8.6e-09 *** a9 32.137 3.318 9.69 2.5e-08 *** a10 40.005 3.336 11.99 1.0e-09 *** a11 36.190 3.109 11.64 1.6e-09 *** a12 36.911 1.839 20.07 2.8e-13 *** a13 30.635 1.700 18.02 1.6e-12 *** a14 34.312 3.495 9.82 2.0e-08 *** a15 38.395 3.375 11.38 2.3e-09 *** a16 31.226 0.886 35.26 < 2e-16 *** a17 31.230 0.821 38.02 < 2e-16 *** a18 19.998 1.011 19.78 3.6e-13 *** a19 37.095 1.071 34.65 < 2e-16 *** a20 32.594 1.121 29.07 6.2e-16 *** a21 30.376 1.057 28.74 7.5e-16 *** b1 -27.300 6.873 -3.97 0.00099 *** b2 -26.270 6.754 -3.89 0.00118 ** b3 -30.901 2.270 -13.61 1.4e-10 *** b4 -32.238 3.810 -8.46 1.7e-07 *** b5 -29.941 3.773 -7.94 4.1e-07 *** b6 -20.622 3.647 -5.65 2.9e-05 *** b7 -19.625 8.085 -2.43 0.02661 * b8 -45.780 4.113 -11.13 3.2e-09 *** b9 -31.345 6.352 -4.93 0.00013 *** b10 -38.599 3.955 -9.76 2.2e-08 *** b11 -33.921 3.839 -8.84 9.2e-08 *** b12 -38.268 8.992 -4.26 0.00053 *** b13 -22.568 8.194 -2.75 0.01355 * b14 -36.167 6.358 -5.69 2.7e-05 *** b15 -32.952 6.354 -5.19 7.4e-05 *** b16 -47.207 9.540 -4.95 0.00012 *** b17 -33.875 7.688 -4.41 0.00039 *** b18 -15.896 6.222 -2.55 0.02051 * b19 -28.969 7.235 -4.00 0.00092 *** b20 -36.917 8.033 -4.60 0.00026 *** b21 -26.508 7.012 -3.78 0.00149 ** th 0.797 0.127 6.30 8.0e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.11 on 17 degrees of freedom > ## Don't show: > options(od) > ## End Don't show > > > > cleanEx() > nameEx("nls.control") > ### * nls.control > > flush(stderr()); flush(stdout()) > > ### Name: nls.control > ### Title: Control the Iterations in nls > ### Aliases: nls.control > ### Keywords: nonlinear regression models > > ### ** Examples > > nls.control(minFactor = 1/2048) $maxiter [1] 50 $tol [1] 1e-05 $minFactor [1] 0.0004882812 $printEval [1] FALSE $warnOnly [1] FALSE > > > > cleanEx() > nameEx("numericDeriv") > ### * numericDeriv > > flush(stderr()); flush(stdout()) > > ### Name: numericDeriv > ### Title: Evaluate Derivatives Numerically > ### Aliases: numericDeriv > ### Keywords: models > > ### ** Examples > > ## Don't show: > od <- options(digits=4) > ## End Don't show > myenv <- new.env() > assign("mean", 0., envir = myenv) > assign("sd", 1., envir = myenv) > assign("x", seq(-3., 3., len = 31), envir = myenv) > numericDeriv(quote(pnorm(x, mean, sd)), c("mean", "sd"), myenv) [1] 0.001350 0.002555 0.004661 0.008198 0.013903 0.022750 0.035930 0.054799 [9] 0.080757 0.115070 0.158655 0.211855 0.274253 0.344578 0.420740 0.500000 [17] 0.579260 0.655422 0.725747 0.788145 0.841345 0.884930 0.919243 0.945201 [25] 0.964070 0.977250 0.986097 0.991802 0.995339 0.997445 0.998650 attr(,"gradient") [,1] [,2] [1,] -0.004432 0.01330 [2,] -0.007915 0.02216 [3,] -0.013583 0.03532 [4,] -0.022395 0.05375 [5,] -0.035475 0.07804 [6,] -0.053991 0.10798 [7,] -0.078950 0.14211 [8,] -0.110921 0.17747 [9,] -0.149727 0.20962 [10,] -0.194186 0.23302 [11,] -0.241971 0.24197 [12,] -0.289692 0.23175 [13,] -0.333225 0.19993 [14,] -0.368270 0.14731 [15,] -0.391043 0.07821 [16,] -0.398942 0.00000 [17,] -0.391043 -0.07821 [18,] -0.368270 -0.14731 [19,] -0.333225 -0.19993 [20,] -0.289692 -0.23175 [21,] -0.241971 -0.24197 [22,] -0.194186 -0.23302 [23,] -0.149727 -0.20962 [24,] -0.110921 -0.17747 [25,] -0.078950 -0.14211 [26,] -0.053991 -0.10798 [27,] -0.035475 -0.07804 [28,] -0.022395 -0.05375 [29,] -0.013583 -0.03532 [30,] -0.007915 -0.02216 [31,] -0.004432 -0.01330 > ## Don't show: > options(od) > ## End Don't show > > > > cleanEx() > nameEx("oneway.test") > ### * oneway.test > > flush(stderr()); flush(stdout()) > > ### Name: oneway.test > ### Title: Test for Equal Means in a One-Way Layout > ### Aliases: oneway.test > ### Keywords: htest > > ### ** Examples > > ## Not assuming equal variances > oneway.test(extra ~ group, data = sleep) One-way analysis of means (not assuming equal variances) data: extra and group F = 3.4626, num df = 1.000, denom df = 17.776, p-value = 0.07939 > ## Assuming equal variances > oneway.test(extra ~ group, data = sleep, var.equal = TRUE) One-way analysis of means data: extra and group F = 3.4626, num df = 1, denom df = 18, p-value = 0.07919 > ## which gives the same result as > anova(lm(extra ~ group, data = sleep)) Analysis of Variance Table Response: extra Df Sum Sq Mean Sq F value Pr(>F) group 1 12.482 12.4820 3.4626 0.07919 . Residuals 18 64.886 3.6048 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > cleanEx() > nameEx("optim") > ### * optim > > flush(stderr()); flush(stdout()) > > ### Name: optim > ### Title: General-purpose Optimization > ### Aliases: optim optimHess > ### Keywords: nonlinear optimize > > ### ** Examples > > > cleanEx() > nameEx("optimize") > ### * optimize > > flush(stderr()); flush(stdout()) > > ### Name: optimize > ### Title: One Dimensional Optimization > ### Aliases: optimize optimise > ### Keywords: optimize > > ### ** Examples > > require(graphics) > > f <- function (x,a) (x-a)^2 > xmin <- optimize(f, c(0, 1), tol = 0.0001, a = 1/3) > xmin $minimum [1] 0.3333333 $objective [1] 0 > > ## See where the function is evaluated: > optimize(function(x) x^2*(print(x)-1), lower=0, upper=10) [1] 3.81966 [1] 6.18034 [1] 2.36068 [1] 2.077939 [1] 1.505823 [1] 0.9306496 [1] 0.9196752 [1] 0.772905 [1] 0.4776816 [1] 0.6491436 [1] 0.656315 [1] 0.6653777 [1] 0.6667786 [1] 0.6666728 [1] 0.6666321 [1] 0.6667135 [1] 0.6666728 $minimum [1] 0.6666728 $objective [1] -0.1481481 > > ## "wrong" solution with unlucky interval and piecewise constant f(): > f <- function(x) ifelse(x > -1, ifelse(x < 4, exp(-1/abs(x - 1)), 10), 10) > fp <- function(x) { print(x); f(x) } > > plot(f, -2,5, ylim = 0:1, col = 2) > optimize(fp, c(-4, 20))# doesn't see the minimum [1] 5.167184 [1] 10.83282 [1] 14.33437 [1] 16.49845 [1] 17.83592 [1] 18.66253 [1] 19.1734 [1] 19.48913 [1] 19.68427 [1] 19.80487 [1] 19.8794 [1] 19.92547 [1] 19.95393 [1] 19.97153 [1] 19.9824 [1] 19.98913 [1] 19.99328 [1] 19.99585 [1] 19.99743 [1] 19.99841 [1] 19.99902 [1] 19.99939 [1] 19.99963 [1] 19.99977 [1] 19.99986 [1] 19.99991 [1] 19.99995 [1] 19.99995 $minimum [1] 19.99995 $objective [1] 10 > optimize(fp, c(-7, 20))# ok [1] 3.313082 [1] 9.686918 [1] -0.6261646 [1] 1.244956 [1] 1.250965 [1] 0.771827 [1] 0.2378417 [1] 1.000451 [1] 0.9906964 [1] 0.9955736 [1] 0.9980122 [1] 0.9992315 [1] 0.9998411 [1] 0.9996083 [1] 0.9994644 [1] 0.9993754 [1] 0.9993204 [1] 0.9992797 [1] 0.9992797 $minimum [1] 0.9992797 $objective [1] 0 > > > > cleanEx() > nameEx("order.dendrogram") > ### * order.dendrogram > > flush(stderr()); flush(stdout()) > > ### Name: order.dendrogram > ### Title: Ordering or Labels of the Leaves in a Dendrogram > ### Aliases: order.dendrogram labels.dendrogram > ### Keywords: manip > > ### ** Examples > > set.seed(123) > x <- rnorm(10) > hc <- hclust(dist(x)) > hc$order [1] 3 6 7 2 4 5 8 9 1 10 > dd <- as.dendrogram(hc) > order.dendrogram(dd) ## the same : [1] 3 6 7 2 4 5 8 9 1 10 > stopifnot(hc$order == order.dendrogram(dd)) > > d2 <- as.dendrogram(hclust(dist(USArrests))) > labels(d2) ## in this case the same as [1] "Florida" "North Carolina" "Delaware" "Alabama" [5] "Louisiana" "Alaska" "Mississippi" "South Carolina" [9] "Maryland" "Arizona" "New Mexico" "California" [13] "Illinois" "New York" "Michigan" "Nevada" [17] "Missouri" "Arkansas" "Tennessee" "Georgia" [21] "Colorado" "Texas" "Rhode Island" "Wyoming" [25] "Oregon" "Oklahoma" "Virginia" "Washington" [29] "Massachusetts" "New Jersey" "Ohio" "Utah" [33] "Connecticut" "Pennsylvania" "Nebraska" "Kentucky" [37] "Montana" "Idaho" "Indiana" "Kansas" [41] "Hawaii" "Minnesota" "Wisconsin" "Iowa" [45] "New Hampshire" "West Virginia" "Maine" "South Dakota" [49] "North Dakota" "Vermont" > stopifnot(identical(labels(d2), + rownames(USArrests)[order.dendrogram(d2)])) > > > > cleanEx() > nameEx("p.adjust") > ### * p.adjust > > flush(stderr()); flush(stdout()) > > ### Name: p.adjust > ### Title: Adjust P-values for Multiple Comparisons > ### Aliases: p.adjust p.adjust.methods > ### Keywords: htest > > ### ** Examples > > require(graphics) > > set.seed(123) > x <- rnorm(50, mean=c(rep(0,25),rep(3,25))) > p <- 2*pnorm( sort(-abs(x))) > > round(p, 3) [1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.002 [13] 0.003 0.004 0.005 0.007 0.007 0.009 0.009 0.011 0.021 0.049 0.061 0.063 [25] 0.074 0.083 0.086 0.119 0.189 0.206 0.221 0.286 0.305 0.466 0.483 0.492 [37] 0.532 0.575 0.578 0.619 0.636 0.645 0.656 0.689 0.719 0.818 0.827 0.897 [49] 0.912 0.944 > round(p.adjust(p), 3) [1] 0.000 0.001 0.001 0.005 0.005 0.006 0.006 0.007 0.009 0.016 0.024 0.063 [13] 0.125 0.131 0.189 0.239 0.240 0.291 0.301 0.350 0.635 1.000 1.000 1.000 [25] 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 [37] 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 [49] 1.000 1.000 > round(p.adjust(p,"BH"), 3) [1] 0.000 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.003 0.007 [13] 0.013 0.013 0.017 0.021 0.021 0.024 0.025 0.028 0.050 0.112 0.130 0.130 [25] 0.148 0.159 0.160 0.213 0.326 0.343 0.356 0.446 0.462 0.684 0.684 0.684 [37] 0.719 0.741 0.741 0.763 0.763 0.763 0.763 0.782 0.799 0.880 0.880 0.930 [49] 0.930 0.944 > > ## or all of them at once (dropping the "fdr" alias): > p.adjust.M <- p.adjust.methods[p.adjust.methods != "fdr"] > p.adj <- sapply(p.adjust.M, function(meth) p.adjust(p, meth)) > p.adj.60 <- sapply(p.adjust.M, function(meth) p.adjust(p, meth, n = 60)) > stopifnot(identical(p.adj[,"none"], p), p.adj <= p.adj.60) > round(p.adj, 3) holm hochberg hommel bonferroni BH BY none [1,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 [2,] 0.001 0.001 0.001 0.001 0.000 0.002 0.000 [3,] 0.001 0.001 0.001 0.001 0.000 0.002 0.000 [4,] 0.005 0.005 0.004 0.005 0.001 0.004 0.000 [5,] 0.005 0.005 0.005 0.005 0.001 0.004 0.000 [6,] 0.006 0.006 0.005 0.006 0.001 0.004 0.000 [7,] 0.006 0.006 0.006 0.007 0.001 0.004 0.000 [8,] 0.007 0.007 0.007 0.008 0.001 0.004 0.000 [9,] 0.009 0.009 0.009 0.011 0.001 0.006 0.000 [10,] 0.016 0.016 0.015 0.019 0.002 0.009 0.000 [11,] 0.024 0.024 0.024 0.031 0.003 0.013 0.001 [12,] 0.063 0.063 0.058 0.081 0.007 0.030 0.002 [13,] 0.125 0.125 0.109 0.165 0.013 0.057 0.003 [14,] 0.131 0.131 0.117 0.177 0.013 0.057 0.004 [15,] 0.189 0.189 0.168 0.262 0.017 0.079 0.005 [16,] 0.239 0.239 0.212 0.342 0.021 0.093 0.007 [17,] 0.240 0.240 0.219 0.353 0.021 0.093 0.007 [18,] 0.291 0.291 0.273 0.440 0.024 0.110 0.009 [19,] 0.301 0.301 0.291 0.470 0.025 0.111 0.009 [20,] 0.350 0.350 0.339 0.565 0.028 0.127 0.011 [21,] 0.635 0.635 0.571 1.000 0.050 0.227 0.021 [22,] 1.000 0.944 0.944 1.000 0.112 0.503 0.049 [23,] 1.000 0.944 0.944 1.000 0.130 0.587 0.061 [24,] 1.000 0.944 0.944 1.000 0.130 0.587 0.063 [25,] 1.000 0.944 0.944 1.000 0.148 0.665 0.074 [26,] 1.000 0.944 0.944 1.000 0.159 0.717 0.083 [27,] 1.000 0.944 0.944 1.000 0.160 0.719 0.086 [28,] 1.000 0.944 0.944 1.000 0.213 0.957 0.119 [29,] 1.000 0.944 0.944 1.000 0.326 1.000 0.189 [30,] 1.000 0.944 0.944 1.000 0.343 1.000 0.206 [31,] 1.000 0.944 0.944 1.000 0.356 1.000 0.221 [32,] 1.000 0.944 0.944 1.000 0.446 1.000 0.286 [33,] 1.000 0.944 0.944 1.000 0.462 1.000 0.305 [34,] 1.000 0.944 0.944 1.000 0.684 1.000 0.466 [35,] 1.000 0.944 0.944 1.000 0.684 1.000 0.483 [36,] 1.000 0.944 0.944 1.000 0.684 1.000 0.492 [37,] 1.000 0.944 0.944 1.000 0.719 1.000 0.532 [38,] 1.000 0.944 0.944 1.000 0.741 1.000 0.575 [39,] 1.000 0.944 0.944 1.000 0.741 1.000 0.578 [40,] 1.000 0.944 0.944 1.000 0.763 1.000 0.619 [41,] 1.000 0.944 0.944 1.000 0.763 1.000 0.636 [42,] 1.000 0.944 0.944 1.000 0.763 1.000 0.645 [43,] 1.000 0.944 0.944 1.000 0.763 1.000 0.656 [44,] 1.000 0.944 0.944 1.000 0.782 1.000 0.689 [45,] 1.000 0.944 0.944 1.000 0.799 1.000 0.719 [46,] 1.000 0.944 0.944 1.000 0.880 1.000 0.818 [47,] 1.000 0.944 0.944 1.000 0.880 1.000 0.827 [48,] 1.000 0.944 0.944 1.000 0.930 1.000 0.897 [49,] 1.000 0.944 0.944 1.000 0.930 1.000 0.912 [50,] 1.000 0.944 0.944 1.000 0.944 1.000 0.944 > ## or a bit nicer: > noquote(apply(p.adj, 2, format.pval, digits = 3)) holm hochberg hommel bonferroni BH BY none [1,] 1.18e-05 1.18e-05 1.18e-05 1.18e-05 1.18e-05 5.3e-05 2.35e-07 [2,] 0.00103 0.00103 0.00101 0.00105 0.000429 0.00193 2.10e-05 [3,] 0.00124 0.00124 0.00124 0.00129 0.000429 0.00193 2.58e-05 [4,] 0.00461 0.00461 0.00422 0.00491 0.000947 0.00426 9.81e-05 [5,] 0.00484 0.00484 0.00453 0.00526 0.000947 0.00426 0.000105 [6,] 0.00559 0.00559 0.00521 0.00621 0.000947 0.00426 0.000124 [7,] 0.00583 0.00583 0.00557 0.00663 0.000947 0.00426 0.000133 [8,] 0.00674 0.00674 0.00659 0.00784 0.000980 0.00441 0.000157 [9,] 0.00947 0.00947 0.00924 0.01127 0.001253 0.00564 0.000225 [10,] 0.01556 0.01556 0.01518 0.01898 0.001898 0.00854 0.000380 [11,] 0.02446 0.02446 0.02446 0.03057 0.002780 0.01251 0.000611 [12,] 0.06294 0.06294 0.05810 0.08070 0.006725 0.03026 0.001614 [13,] 0.12549 0.12549 0.10898 0.16512 0.012637 0.05686 0.003302 [14,] 0.13092 0.13092 0.11677 0.17692 0.012637 0.05686 0.003538 [15,] 0.18853 0.18853 0.16758 0.26185 0.017457 0.07854 0.005237 [16,] 0.23912 0.23912 0.21179 0.34160 0.020762 0.09341 0.006832 [17,] 0.24001 0.24001 0.21884 0.35296 0.020762 0.09341 0.007059 [18,] 0.29057 0.29057 0.27296 0.44026 0.024459 0.11004 0.008805 [19,] 0.30083 0.30083 0.29143 0.47005 0.024740 0.11131 0.009401 [20,] 0.35024 0.35024 0.33894 0.56490 0.028245 0.12708 0.011298 [21,] 0.63451 0.63451 0.57105 1.00000 0.050358 0.22657 0.021150 [22,] 1.00000 0.94379 0.94379 1.00000 0.111880 0.50337 0.049227 [23,] 1.00000 0.94379 0.94379 1.00000 0.130463 0.58698 0.060533 [24,] 1.00000 0.94379 0.94379 1.00000 0.130463 0.58698 0.062622 [25,] 1.00000 0.94379 0.94379 1.00000 0.147903 0.66545 0.073952 [26,] 1.00000 0.94379 0.94379 1.00000 0.159252 0.71651 0.082811 [27,] 1.00000 0.94379 0.94379 1.00000 0.159877 0.71932 0.086333 [28,] 1.00000 0.94379 0.94379 1.00000 0.212617 0.95661 0.119065 [29,] 1.00000 0.94379 0.94379 1.00000 0.325999 1.00000 0.189080 [30,] 1.00000 0.94379 0.94379 1.00000 0.343082 1.00000 0.205849 [31,] 1.00000 0.94379 0.94379 1.00000 0.356325 1.00000 0.220921 [32,] 1.00000 0.94379 0.94379 1.00000 0.446250 1.00000 0.285600 [33,] 1.00000 0.94379 0.94379 1.00000 0.461954 1.00000 0.304889 [34,] 1.00000 0.94379 0.94379 1.00000 0.683577 1.00000 0.466068 [35,] 1.00000 0.94379 0.94379 1.00000 0.683577 1.00000 0.483081 [36,] 1.00000 0.94379 0.94379 1.00000 0.683577 1.00000 0.492175 [37,] 1.00000 0.94379 0.94379 1.00000 0.718845 1.00000 0.531945 [38,] 1.00000 0.94379 0.94379 1.00000 0.741435 1.00000 0.575155 [39,] 1.00000 0.94379 0.94379 1.00000 0.741435 1.00000 0.578319 [40,] 1.00000 0.94379 0.94379 1.00000 0.762606 1.00000 0.618589 [41,] 1.00000 0.94379 0.94379 1.00000 0.762606 1.00000 0.636362 [42,] 1.00000 0.94379 0.94379 1.00000 0.762606 1.00000 0.644859 [43,] 1.00000 0.94379 0.94379 1.00000 0.762606 1.00000 0.655841 [44,] 1.00000 0.94379 0.94379 1.00000 0.782487 1.00000 0.688588 [45,] 1.00000 0.94379 0.94379 1.00000 0.798874 1.00000 0.718986 [46,] 1.00000 0.94379 0.94379 1.00000 0.880265 1.00000 0.817954 [47,] 1.00000 0.94379 0.94379 1.00000 0.880265 1.00000 0.827449 [48,] 1.00000 0.94379 0.94379 1.00000 0.930478 1.00000 0.897130 [49,] 1.00000 0.94379 0.94379 1.00000 0.930478 1.00000 0.911868 [50,] 1.00000 0.94379 0.94379 1.00000 0.943789 1.00000 0.943789 > > > ## and a graphic: > matplot(p, p.adj, ylab="p.adjust(p, meth)", type = "l", asp=1, lty=1:6, + main = "P-value adjustments") > legend(.7,.6, p.adjust.M, col=1:6, lty=1:6) > > ## Can work with NA's: > pN <- p; iN <- c(46,47); pN[iN] <- NA > pN.a <- sapply(p.adjust.M, function(meth) p.adjust(pN, meth)) > ## The smallest 20 P-values all affected by the NA's : > round((pN.a / p.adj)[1:20, ] , 4) holm hochberg hommel bonferroni BH BY none [1,] 0.9600 0.9600 0.9600 0.96 0.96 0.9514 1 [2,] 0.9592 0.9592 0.9583 0.96 0.96 0.9514 1 [3,] 0.9583 0.9583 0.9583 0.96 0.96 0.9514 1 [4,] 0.9574 0.9574 0.9535 0.96 0.96 0.9514 1 [5,] 0.9565 0.9565 0.9535 0.96 0.96 0.9514 1 [6,] 0.9556 0.9556 0.9524 0.96 0.96 0.9514 1 [7,] 0.9545 0.9545 0.9524 0.96 0.96 0.9514 1 [8,] 0.9535 0.9535 0.9524 0.96 0.96 0.9514 1 [9,] 0.9524 0.9524 0.9512 0.96 0.96 0.9514 1 [10,] 0.9512 0.9512 0.9500 0.96 0.96 0.9514 1 [11,] 0.9500 0.9500 0.9500 0.96 0.96 0.9514 1 [12,] 0.9487 0.9487 0.9444 0.96 0.96 0.9514 1 [13,] 0.9474 0.9474 0.9394 0.96 0.96 0.9514 1 [14,] 0.9459 0.9459 0.9394 0.96 0.96 0.9514 1 [15,] 0.9444 0.9444 0.9375 0.96 0.96 0.9514 1 [16,] 0.9429 0.9429 0.9355 0.96 0.96 0.9514 1 [17,] 0.9412 0.9412 0.9355 0.96 0.96 0.9514 1 [18,] 0.9394 0.9394 0.9355 0.96 0.96 0.9514 1 [19,] 0.9375 0.9375 0.9355 0.96 0.96 0.9514 1 [20,] 0.9355 0.9355 0.9333 0.96 0.96 0.9514 1 > > > > cleanEx() > nameEx("pairwise.prop.test") > ### * pairwise.prop.test > > flush(stderr()); flush(stdout()) > > ### Name: pairwise.prop.test > ### Title: Pairwise comparisons for proportions > ### Aliases: pairwise.prop.test > ### Keywords: htest > > ### ** Examples > > smokers <- c( 83, 90, 129, 70 ) > patients <- c( 86, 93, 136, 82 ) > pairwise.prop.test(smokers, patients) Warning in prop.test(x[c(i, j)], n[c(i, j)], ...) : Chi-squared approximation may be incorrect Warning in prop.test(x[c(i, j)], n[c(i, j)], ...) : Chi-squared approximation may be incorrect Warning in prop.test(x[c(i, j)], n[c(i, j)], ...) : Chi-squared approximation may be incorrect Pairwise comparisons using Pairwise comparison of proportions data: smokers out of patients 1 2 3 2 1.000 - - 3 1.000 1.000 - 4 0.119 0.093 0.124 P value adjustment method: holm > > > > cleanEx() > nameEx("pairwise.t.test") > ### * pairwise.t.test > > flush(stderr()); flush(stdout()) > > ### Name: pairwise.t.test > ### Title: Pairwise t tests > ### Aliases: pairwise.t.test > ### Keywords: htest > > ### ** Examples > > attach(airquality) > Month <- factor(Month, labels = month.abb[5:9]) > pairwise.t.test(Ozone, Month) Pairwise comparisons using t tests with pooled SD data: Ozone and Month May Jun Jul Aug Jun 1.00000 - - - Jul 0.00026 0.05113 - - Aug 0.00019 0.04987 1.00000 - Sep 1.00000 1.00000 0.00488 0.00388 P value adjustment method: holm > pairwise.t.test(Ozone, Month, p.adj = "bonf") Pairwise comparisons using t tests with pooled SD data: Ozone and Month May Jun Jul Aug Jun 1.00000 - - - Jul 0.00029 0.10225 - - Aug 0.00019 0.08312 1.00000 - Sep 1.00000 1.00000 0.00697 0.00485 P value adjustment method: bonferroni > pairwise.t.test(Ozone, Month, pool.sd = FALSE) Pairwise comparisons using t tests with non-pooled SD data: Ozone and Month May Jun Jul Aug Jun 1.00000 - - - Jul 0.00026 0.01527 - - Aug 0.00195 0.02135 1.00000 - Sep 0.86321 1.00000 0.00589 0.01721 P value adjustment method: holm > detach() > > > > cleanEx() > nameEx("pairwise.wilcox.test") > ### * pairwise.wilcox.test > > flush(stderr()); flush(stdout()) > > ### Name: pairwise.wilcox.test > ### Title: Pairwise Wilcoxon Rank Sum Tests > ### Aliases: pairwise.wilcox.test > ### Keywords: htest > > ### ** Examples > > attach(airquality) > Month <- factor(Month, labels = month.abb[5:9]) > ## These give warnings because of ties : > pairwise.wilcox.test(Ozone, Month) Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Pairwise comparisons using Wilcoxon rank sum test data: Ozone and Month May Jun Jul Aug Jun 0.5775 - - - Jul 0.0003 0.0848 - - Aug 0.0011 0.1295 1.0000 - Sep 0.4744 1.0000 0.0060 0.0227 P value adjustment method: holm > pairwise.wilcox.test(Ozone, Month, p.adj = "bonf") Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Warning in wilcox.test.default(xi, xj, paired = paired, ...) : cannot compute exact p-value with ties Pairwise comparisons using Wilcoxon rank sum test data: Ozone and Month May Jun Jul Aug Jun 1.0000 - - - Jul 0.0003 0.1414 - - Aug 0.0012 0.2591 1.0000 - Sep 1.0000 1.0000 0.0074 0.0325 P value adjustment method: bonferroni > detach() > > > > cleanEx() > nameEx("plot.acf") > ### * plot.acf > > flush(stderr()); flush(stdout()) > > ### Name: plot.acf > ### Title: Plot Autocovariance and Autocorrelation Functions > ### Aliases: plot.acf > ### Keywords: hplot ts > > ### ** Examples > > require(graphics) > > z4 <- ts(matrix(rnorm(400), 100, 4), start=c(1961, 1), frequency=12) > z7 <- ts(matrix(rnorm(700), 100, 7), start=c(1961, 1), frequency=12) > acf(z4) > acf(z7, max.mfrow = 7)# squeeze on 1 page > acf(z7) # multi-page > > > > cleanEx() > nameEx("plot.isoreg") > ### * plot.isoreg > > flush(stderr()); flush(stdout()) > > ### Name: plot.isoreg > ### Title: Plot Method for isoreg Objects > ### Aliases: plot.isoreg lines.isoreg > ### Keywords: hplot print > > ### ** Examples > > require(graphics) > > utils::example(isoreg) # for the examples there isoreg> require(graphics) isoreg> (ir <- isoreg(c(1,0,4,3,3,5,4,2,0))) Isotonic regression from isoreg(x = c(1, 0, 4, 3, 3, 5, 4, 2, 0)), with 2 knots / breaks at obs.nr. 2 9 ; initially ordered 'x' and further components List of 4 $ x : num [1:9] 1 2 3 4 5 6 7 8 9 $ y : num [1:9] 1 0 4 3 3 5 4 2 0 $ yf: num [1:9] 0.5 0.5 3 3 3 3 3 3 3 $ yc: num [1:10] 0 1 1 5 8 11 16 20 22 22 isoreg> plot(ir, plot.type = "row") isoreg> (ir3 <- isoreg(y3 <- c(1,0,4,3,3,5,4,2, 3)))# last "3", not "0" Isotonic regression from isoreg(x = y3 <- c(1, 0, 4, 3, 3, 5, 4, 2, 3)), with 3 knots / breaks at obs.nr. 2 5 9 ; initially ordered 'x' and further components List of 4 $ x : num [1:9] 1 2 3 4 5 6 7 8 9 $ y : num [1:9] 1 0 4 3 3 5 4 2 3 $ yf: num [1:9] 0.5 0.5 3.33 3.33 3.33 ... $ yc: num [1:10] 0 1 1 5 8 11 16 20 22 25 isoreg> (fi3 <- as.stepfun(ir3)) Step function Call: isoreg(x = y3 <- c(1, 0, 4, 3, 3, 5, 4, 2, 3)) x[1:3] = 2, 5, 9 4 plateau levels = 0.5, 0.5, 3.3333, 3.5 isoreg> (ir4 <- isoreg(1:10, y4 <- c(5, 9, 1:2, 5:8, 3, 8))) Isotonic regression from isoreg(x = 1:10, y = y4 <- c(5, 9, 1:2, 5:8, 3, 8)), with 5 knots / breaks at obs.nr. 4 5 6 9 10 ; initially ordered 'x' and further components List of 4 $ x : num [1:10] 1 2 3 4 5 6 7 8 9 10 $ y : num [1:10] 5 9 1 2 5 6 7 8 3 8 $ yf: num [1:10] 4.25 4.25 4.25 4.25 5 6 6 6 6 8 $ yc: num [1:11] 0 5 14 15 17 22 28 35 43 46 ... isoreg> cat(sprintf("R^2 = %.2f\n", isoreg+ 1 - sum(residuals(ir4)^2) / ((10-1)*var(y4)))) R^2 = 0.21 isoreg> ## If you are interested in the knots alone : isoreg> with(ir4, cbind(iKnots, yf[iKnots])) iKnots [1,] 4 4.25 [2,] 5 5.00 [3,] 6 6.00 [4,] 9 6.00 [5,] 10 8.00 isoreg> ## Example of unordered x[] with ties: isoreg> x <- sample((0:30)/8) isoreg> y <- exp(x) isoreg> x. <- round(x) # ties! isoreg> plot(m <- isoreg(x., y)) isoreg> stopifnot(all.equal(with(m, yf[iKnots]), isoreg+ as.vector(tapply(y, x., mean)))) > > plot(y3, main = "simple plot(.) + lines()") > lines(ir3) > > ## 'same' plot as above, "proving" that only ranks of 'x' are important > plot(isoreg(2^(1:9), c(1,0,4,3,3,5,4,2,0)), plot.type = "row", log = "x") Warning in xy.coords(x, y, xlabel, ylabel, log) : 1 x value <= 0 omitted from logarithmic plot Warning in xy.coords(x, y, xlabel, ylabel, log) : 1 x value <= 0 omitted from logarithmic plot > > plot(ir3, plot.type = "row", ylab = "y3") > plot(isoreg(y3 - 4), plot.t="r", ylab = "y3 - 4") > plot(ir4, plot.type = "ro", ylab = "y4", xlab = "x = 1:n") > > ## experiment a bit with these (C-c C-j): > plot(isoreg(sample(9), y3), plot.type="row") > plot(isoreg(sample(9), y3), plot.type="col.wise") > > plot(ir <- isoreg(sample(10), sample(10, replace = TRUE)), + plot.type = "r") > > > > cleanEx() > nameEx("plot.lm") > ### * plot.lm > > flush(stderr()); flush(stdout()) > > ### Name: plot.lm > ### Title: Plot Diagnostics for an lm Object > ### Aliases: plot.lm plot.mlm > ### Keywords: hplot regression > > ### ** Examples > > require(graphics) > > ## Analysis of the life-cycle savings data > ## given in Belsley, Kuh and Welsch. > lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) > plot(lm.SR) > > ## 4 plots on 1 page; > ## allow room for printing model formula in outer margin: > par(mfrow = c(2, 2), oma = c(0, 0, 2, 0)) > plot(lm.SR) > plot(lm.SR, id.n = NULL) # no id's > plot(lm.SR, id.n = 5, labels.id = NULL)# 5 id numbers > > ## Was default in R <= 2.1.x: > ## Cook's distances instead of Residual-Leverage plot > plot(lm.SR, which = 1:4) > > ## Fit a smooth curve, where applicable: > plot(lm.SR, panel = panel.smooth) > ## Gives a smoother curve > plot(lm.SR, panel = function(x,y) panel.smooth(x, y, span = 1)) > > par(mfrow=c(2,1))# same oma as above > plot(lm.SR, which = 1:2, sub.caption = "Saving Rates, n=50, p=5") > > ## Don't show: > ## An example with *long* formula that needs abbreviation: > for(i in 1:5) assign(paste("long.var.name",i,sep="."), runif(10)) > plot(lm(long.var.name.1 ~ + long.var.name.2 + long.var.name.3 + long.var.name.4 + long.var.name.5)) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("plot.ppr") > ### * plot.ppr > > flush(stderr()); flush(stdout()) > > ### Name: plot.ppr > ### Title: Plot Ridge Functions for Projection Pursuit Regression Fit > ### Aliases: plot.ppr > ### Keywords: hplot > > ### ** Examples > > require(graphics) > > with(rock, { + area1 <- area/10000; peri1 <- peri/10000 + par(mfrow=c(3,2))# maybe: , pty="s") + rock.ppr <- ppr(log(perm) ~ area1 + peri1 + shape, + data = rock, nterms = 2, max.terms = 5) + plot(rock.ppr, main="ppr(log(perm)~ ., nterms=2, max.terms=5)") + plot(update(rock.ppr, bass=5), main = "update(..., bass = 5)") + plot(update(rock.ppr, sm.method="gcv", gcvpen=2), + main = "update(..., sm.method=\"gcv\", gcvpen=2)") + }) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("plot.profile.nls") > ### * plot.profile.nls > > flush(stderr()); flush(stdout()) > > ### Name: plot.profile.nls > ### Title: Plot a profile.nls Object > ### Aliases: plot.profile.nls > ### Keywords: nonlinear regression models > > ### ** Examples > > require(graphics) > > # obtain the fitted object > fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD) > # get the profile for the fitted model > pr1 <- profile(fm1, alpha = 0.05) > opar <- par(mfrow = c(2,2), oma = c(1.1, 0, 1.1, 0), las = 1) > plot(pr1, conf = c(95, 90, 80, 50)/100) > plot(pr1, conf = c(95, 90, 80, 50)/100, absVal = FALSE) > mtext("Confidence intervals based on the profile sum of squares", + side = 3, outer = TRUE) > mtext("BOD data - confidence levels of 50%, 80%, 90% and 95%", + side = 1, outer = TRUE) > par(opar) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("plot.stepfun") > ### * plot.stepfun > > flush(stderr()); flush(stdout()) > > ### Name: plot.stepfun > ### Title: Plot Step Functions > ### Aliases: plot.stepfun lines.stepfun > ### Keywords: hplot > > ### ** Examples > > require(graphics) > > y0 <- c(1,2,4,3) > sfun0 <- stepfun(1:3, y0, f = 0) > sfun.2 <- stepfun(1:3, y0, f = .2) > sfun1 <- stepfun(1:3, y0, right = TRUE) > > tt <- seq(0,3, by=0.1) > op <- par(mfrow=c(2,2)) > plot(sfun0); plot(sfun0, xval=tt, add=TRUE, col.hor="bisque") > plot(sfun.2);plot(sfun.2,xval=tt, add=TRUE, col = "orange")# all colors > plot(sfun1);lines(sfun1, xval=tt, col.hor="coral") > ##-- This is revealing : > plot(sfun0, verticals= FALSE, + main = "stepfun(x, y0, f=f) for f = 0, .2, 1") > for(i in 1:3) + lines(list(sfun0,sfun.2,stepfun(1:3,y0,f = 1))[[i]], col=i) > legend(2.5, 1.9, paste("f =", c(0,0.2,1)), col=1:3, lty=1, y.intersp=1) > par(op) > > # Extend and/or restrict 'viewport': > plot(sfun0, xlim = c(0,5), ylim = c(0, 3.5), + main = "plot(stepfun(*), xlim= . , ylim = .)") > > ##-- this works too (automatic call to ecdf(.)): > plot.stepfun(rt(50, df=3), col.vert = "gray20") > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("plot.ts") > ### * plot.ts > > flush(stderr()); flush(stdout()) > > ### Name: plot.ts > ### Title: Plotting Time-Series Objects > ### Aliases: plot.ts lines.ts > ### Keywords: hplot ts > > ### ** Examples > > require(graphics) > > ## Multivariate > z <- ts(matrix(rt(200 * 8, df = 3), 200, 8), + start = c(1961, 1), frequency = 12) > plot(z, yax.flip = TRUE) > plot(z, axes = FALSE, ann = FALSE, frame.plot = TRUE, + mar.multi = c(0,0,0,0), oma.multi = c(1,1,5,1)) > title("plot(ts(..), axes=FALSE, ann=FALSE, frame.plot=TRUE, mar..., oma...)") > > z <- window(z[,1:3], end = c(1969,12)) > plot(z, type = "b") # multiple > plot(z, plot.type="single", lty=1:3, col=4:2) > > ## A phase plot: > plot(nhtemp, c(nhtemp[-1], NA), cex = .8, col="blue", + main = "Lag plot of New Haven temperatures") > ## a clearer way to do this would be > ## Not run: > ##D plot(nhtemp, lag(nhtemp, 1), cex = .8, col="blue", > ##D main = "Lag plot of New Haven temperatures") > ## End(Not run) > > ## xy.lines and xy.labels are FALSE for large series: > plot(lag(sunspots, 1), sunspots, pch = ".") > > SMI <- EuStockMarkets[, "SMI"] > plot(lag(SMI, 1), SMI, pch = ".") > plot(lag(SMI, 20), SMI, pch = ".", log = "xy", + main = "4 weeks lagged SMI stocks -- log scale", xy.lines= TRUE) > > > > cleanEx() > nameEx("poisson.test") > ### * poisson.test > > flush(stderr()); flush(stdout()) > > ### Name: poisson.test > ### Title: Exact Poisson tests > ### Aliases: poisson.test > ### Keywords: htest > > ### ** Examples > > ### These are paraphrased from data sets in the ISwR package > > ## SMR, Welsh Nickel workers > poisson.test(137, 24.19893) Exact Poisson test data: 137 time base: 24.19893 number of events = 137, time base = 24.199, p-value < 2.2e-16 alternative hypothesis: true event rate is not equal to 1 95 percent confidence interval: 4.753125 6.692709 sample estimates: event rate 5.661407 > > ## eba1977, compare Fredericia to other three cities for ages 55-59 > poisson.test(c(11,6+8+7),c(800, 1083+1050+878)) Comparison of Poisson rates data: c(11, 6 + 8 + 7) time base: c(800, 1083 + 1050 + 878) count1 = 11, expected count1 = 6.717, p-value = 0.07967 alternative hypothesis: true rate ratio is not equal to 1 95 percent confidence interval: 0.8584264 4.2772659 sample estimates: rate ratio 1.971488 > > > > cleanEx() > nameEx("poly") > ### * poly > > flush(stderr()); flush(stdout()) > > ### Name: poly > ### Title: Compute Orthogonal Polynomials > ### Aliases: poly polym predict.poly makepredictcall.poly > ### Keywords: math > > ### ** Examples > > od <- options(digits=3) # avoid too much visual clutter > (z <- poly(1:10, 3)) 1 2 3 [1,] -0.495 0.522 -0.453 [2,] -0.385 0.174 0.151 [3,] -0.275 -0.087 0.378 [4,] -0.165 -0.261 0.335 [5,] -0.055 -0.348 0.130 [6,] 0.055 -0.348 -0.130 [7,] 0.165 -0.261 -0.335 [8,] 0.275 -0.087 -0.378 [9,] 0.385 0.174 -0.151 [10,] 0.495 0.522 0.453 attr(,"degree") [1] 1 2 3 attr(,"coefs") attr(,"coefs")$alpha [1] 5.5 5.5 5.5 attr(,"coefs")$norm2 [1] 1.0 10.0 82.5 528.0 3088.8 attr(,"class") [1] "poly" "matrix" > predict(z, seq(2, 4, 0.5)) 1 2 3 [1,] -0.385 0.1741 0.151 [2,] -0.330 0.0326 0.305 [3,] -0.275 -0.0870 0.378 [4,] -0.220 -0.1850 0.383 [5,] -0.165 -0.2611 0.335 attr(,"degree") [1] 1 2 3 attr(,"coefs") attr(,"coefs")$alpha [1] 5.5 5.5 5.5 attr(,"coefs")$norm2 [1] 1.0 10.0 82.5 528.0 3088.8 attr(,"class") [1] "poly" "matrix" > zapsmall(poly(seq(4, 6, 0.5), 3, coefs = attr(z, "coefs"))) 1 2 3 [1,] -0.165 -0.261 0.335 [2,] -0.110 -0.316 0.246 [3,] -0.055 -0.348 0.130 [4,] 0.000 -0.359 0.000 [5,] 0.055 -0.348 -0.130 attr(,"degree") [1] 1 2 3 attr(,"coefs") attr(,"coefs")$alpha [1] 5.5 5.5 5.5 attr(,"coefs")$norm2 [1] 1.0 10.0 82.5 528.0 3088.8 attr(,"class") [1] "poly" "matrix" > > zapsmall(polym(1:4, c(1, 4:6), degree=3)) # or just poly() 1.0 2.0 3.0 0.1 1.1 2.1 0.2 1.2 0.3 [1,] -0.671 0.5 -0.224 -0.802 0.538 -0.401 0.323 -0.217 -0.053 [2,] -0.224 -0.5 0.671 0.000 0.000 0.000 -0.688 0.154 0.526 [3,] 0.224 -0.5 -0.671 0.267 0.060 -0.134 -0.239 -0.053 -0.788 [4,] 0.671 0.5 0.224 0.535 0.359 0.267 0.604 0.405 0.315 attr(,"degree") [1] 1 2 3 1 2 3 2 3 3 > zapsmall(poly(cbind(1:4, c(1, 4:6)), degree=3)) 1.0 2.0 3.0 0.1 1.1 2.1 0.2 1.2 0.3 [1,] -0.671 0.5 -0.224 -0.802 0.538 -0.401 0.323 -0.217 -0.053 [2,] -0.224 -0.5 0.671 0.000 0.000 0.000 -0.688 0.154 0.526 [3,] 0.224 -0.5 -0.671 0.267 0.060 -0.134 -0.239 -0.053 -0.788 [4,] 0.671 0.5 0.224 0.535 0.359 0.267 0.604 0.405 0.315 attr(,"degree") [1] 1 2 3 1 2 3 2 3 3 > options(od) > > > > cleanEx() > nameEx("power") > ### * power > > flush(stderr()); flush(stdout()) > > ### Name: power > ### Title: Create a Power Link Object > ### Aliases: power > ### Keywords: models > > ### ** Examples > > power() $linkfun function (mu) mu $linkinv function (eta) eta $mu.eta function (eta) rep.int(1, length(eta)) $valideta function (eta) TRUE $name [1] "identity" attr(,"class") [1] "link-glm" > quasi(link=power(1/3))[c("linkfun", "linkinv")] $linkfun function (mu) mu^lambda $linkinv function (eta) pmax(eta^(1/lambda), .Machine$double.eps) > > > > cleanEx() > nameEx("power.anova.test") > ### * power.anova.test > > flush(stderr()); flush(stdout()) > > ### Name: power.anova.test > ### Title: Power Calculations for Balanced One-Way Analysis of Variance > ### Tests > ### Aliases: power.anova.test > ### Keywords: htest > > ### ** Examples > > power.anova.test(groups=4, n=5, between.var=1, within.var=3) Balanced one-way analysis of variance power calculation groups = 4 n = 5 between.var = 1 within.var = 3 sig.level = 0.05 power = 0.3535594 NOTE: n is number in each group > # Power = 0.3535594 > > power.anova.test(groups=4, between.var=1, within.var=3, + power=.80) Balanced one-way analysis of variance power calculation groups = 4 n = 11.92613 between.var = 1 within.var = 3 sig.level = 0.05 power = 0.8 NOTE: n is number in each group > # n = 11.92613 > > ## Assume we have prior knowledge of the group means: > groupmeans <- c(120, 130, 140, 150) > power.anova.test(groups = length(groupmeans), + between.var=var(groupmeans), + within.var=500, power=.90) # n = 15.18834 Balanced one-way analysis of variance power calculation groups = 4 n = 15.18834 between.var = 166.6667 within.var = 500 sig.level = 0.05 power = 0.9 NOTE: n is number in each group > > > > > cleanEx() > nameEx("power.prop.test") > ### * power.prop.test > > flush(stderr()); flush(stdout()) > > ### Name: power.prop.test > ### Title: Power Calculations for Two-Sample Test for Proportions > ### Aliases: power.prop.test > ### Keywords: htest > > ### ** Examples > > power.prop.test(n = 50, p1 = .50, p2 = .75) Two-sample comparison of proportions power calculation n = 50 p1 = 0.5 p2 = 0.75 sig.level = 0.05 power = 0.7401659 alternative = two.sided NOTE: n is number in *each* group > power.prop.test(p1 = .50, p2 = .75, power = .90) Two-sample comparison of proportions power calculation n = 76.70693 p1 = 0.5 p2 = 0.75 sig.level = 0.05 power = 0.9 alternative = two.sided NOTE: n is number in *each* group > power.prop.test(n = 50, p1 = .5, power = .90) Two-sample comparison of proportions power calculation n = 50 p1 = 0.5 p2 = 0.8026141 sig.level = 0.05 power = 0.9 alternative = two.sided NOTE: n is number in *each* group > > > > cleanEx() > nameEx("power.t.test") > ### * power.t.test > > flush(stderr()); flush(stdout()) > > ### Name: power.t.test > ### Title: Power calculations for one and two sample t tests > ### Aliases: power.t.test > ### Keywords: htest > > ### ** Examples > > power.t.test(n = 20, delta = 1) Two-sample t test power calculation n = 20 delta = 1 sd = 1 sig.level = 0.05 power = 0.8689528 alternative = two.sided NOTE: n is number in *each* group > power.t.test(power = .90, delta = 1) Two-sample t test power calculation n = 22.0211 delta = 1 sd = 1 sig.level = 0.05 power = 0.9 alternative = two.sided NOTE: n is number in *each* group > power.t.test(power = .90, delta = 1, alternative = "one.sided") Two-sample t test power calculation n = 17.84713 delta = 1 sd = 1 sig.level = 0.05 power = 0.9 alternative = one.sided NOTE: n is number in *each* group > > > > cleanEx() > nameEx("pp.test") > ### * pp.test > > flush(stderr()); flush(stdout()) > > ### Name: PP.test > ### Title: Phillips-Perron Test for Unit Roots > ### Aliases: PP.test > ### Keywords: ts > > ### ** Examples > > x <- rnorm(1000) > PP.test(x) Phillips-Perron Unit Root Test data: x Dickey-Fuller = -33.0567, Truncation lag parameter = 7, p-value = 0.01 > y <- cumsum(x) # has unit root > PP.test(y) Phillips-Perron Unit Root Test data: y Dickey-Fuller = -2.6899, Truncation lag parameter = 7, p-value = 0.2863 > > > > cleanEx() > nameEx("ppoints") > ### * ppoints > > flush(stderr()); flush(stdout()) > > ### Name: ppoints > ### Title: Ordinates for Probability Plotting > ### Aliases: ppoints > ### Keywords: dplot arith distribution > > ### ** Examples > > ppoints(4) # the same as ppoints(1:4) [1] 0.1470588 0.3823529 0.6176471 0.8529412 > ppoints(10) [1] 0.06097561 0.15853659 0.25609756 0.35365854 0.45121951 0.54878049 [7] 0.64634146 0.74390244 0.84146341 0.93902439 > ppoints(10, a=1/2) [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 > > > > cleanEx() > nameEx("ppr") > ### * ppr > > flush(stderr()); flush(stdout()) > > ### Name: ppr > ### Title: Projection Pursuit Regression > ### Aliases: ppr ppr.default ppr.formula > ### Keywords: regression > > ### ** Examples > > require(graphics) > > # Note: your numerical values may differ > attach(rock) > area1 <- area/10000; peri1 <- peri/10000 > rock.ppr <- ppr(log(perm) ~ area1 + peri1 + shape, + data = rock, nterms = 2, max.terms = 5) > rock.ppr Call: ppr(formula = log(perm) ~ area1 + peri1 + shape, data = rock, nterms = 2, max.terms = 5) Goodness of fit: 2 terms 3 terms 4 terms 5 terms 8.737806 5.289517 4.745799 4.490378 > # Call: > # ppr.formula(formula = log(perm) ~ area1 + peri1 + shape, data = rock, > # nterms = 2, max.terms = 5) > # > # Goodness of fit: > # 2 terms 3 terms 4 terms 5 terms > # 8.737806 5.289517 4.745799 4.490378 > > summary(rock.ppr) Call: ppr(formula = log(perm) ~ area1 + peri1 + shape, data = rock, nterms = 2, max.terms = 5) Goodness of fit: 2 terms 3 terms 4 terms 5 terms 8.737806 5.289517 4.745799 4.490378 Projection direction vectors: term 1 term 2 area1 0.34357179 0.37071027 peri1 -0.93781471 -0.61923542 shape 0.04961846 0.69218595 Coefficients of ridge terms: term 1 term 2 1.6079271 0.5460971 > # ..... (same as above) > # ..... > # > # Projection direction vectors: > # term 1 term 2 > # area1 0.34357179 0.37071027 > # peri1 -0.93781471 -0.61923542 > # shape 0.04961846 0.69218595 > # > # Coefficients of ridge terms: > # term 1 term 2 > # 1.6079271 0.5460971 > > par(mfrow=c(3,2))# maybe: , pty="s") > plot(rock.ppr, main="ppr(log(perm)~ ., nterms=2, max.terms=5)") > plot(update(rock.ppr, bass=5), main = "update(..., bass = 5)") > plot(update(rock.ppr, sm.method="gcv", gcvpen=2), + main = "update(..., sm.method=\"gcv\", gcvpen=2)") > cbind(perm=rock$perm, prediction=round(exp(predict(rock.ppr)), 1)) perm prediction 1 6.3 5.9 2 6.3 6.5 3 6.3 12.1 4 6.3 7.4 5 17.1 16.3 6 17.1 13.2 7 17.1 41.2 8 17.1 10.6 9 119.0 101.9 10 119.0 73.0 11 119.0 46.8 12 119.0 121.7 13 82.4 107.8 14 82.4 79.7 15 82.4 98.3 16 82.4 128.8 17 58.6 66.8 18 58.6 33.2 19 58.6 58.7 20 58.6 111.6 21 142.0 118.9 22 142.0 128.4 23 142.0 91.9 24 142.0 188.5 25 740.0 341.7 26 740.0 577.9 27 740.0 895.7 28 740.0 1041.0 29 890.0 560.8 30 890.0 721.8 31 890.0 937.9 32 890.0 848.5 33 950.0 806.4 34 950.0 1085.4 35 950.0 945.6 36 950.0 848.5 37 100.0 154.1 38 100.0 178.0 39 100.0 321.5 40 100.0 232.7 41 1300.0 1067.0 42 1300.0 697.8 43 1300.0 1236.6 44 1300.0 1301.8 45 580.0 485.1 46 580.0 285.1 47 580.0 644.3 48 580.0 571.5 > detach() > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("prcomp") > ### * prcomp > > flush(stderr()); flush(stdout()) > > ### Name: prcomp > ### Title: Principal Components Analysis > ### Aliases: prcomp prcomp.formula prcomp.default plot.prcomp > ### predict.prcomp print.prcomp summary.prcomp print.summary.prcomp > ### Keywords: multivariate > > ### ** Examples > > > cleanEx() > nameEx("predict.HoltWinters") > ### * predict.HoltWinters > > flush(stderr()); flush(stdout()) > > ### Name: predict.HoltWinters > ### Title: Prediction Function for Fitted Holt-Winters Models > ### Aliases: predict.HoltWinters > ### Keywords: ts > > ### ** Examples > > require(graphics) > > m <- HoltWinters(co2) > p <- predict(m, 50, prediction.interval = TRUE) > plot(m, p) > > > > cleanEx() > nameEx("predict") > ### * predict > > flush(stderr()); flush(stdout()) > > ### Name: predict > ### Title: Model Predictions > ### Aliases: predict > ### Keywords: methods > > ### ** Examples > > ## Don't show: > old <- Sys.setlocale("LC_COLLATE", "C") > ## End Don't show > require(utils) > > ## All the "predict" methods found > ## NB most of the methods in the standard packages are hidden. > for(fn in methods("predict")) + try({ + f <- eval(substitute(getAnywhere(fn)$objs[[1]], list(fn = fn))) + cat(fn, ":\n\t", deparse(args(f)), "\n") + }, silent = TRUE) predict.Arima : function (object, n.ahead = 1, newxreg = NULL, se.fit = TRUE, ...) NULL predict.HoltWinters : function (object, n.ahead = 1, prediction.interval = FALSE, level = 0.95, ...) NULL predict.StructTS : function (object, n.ahead = 1, se.fit = TRUE, ...) NULL predict.ar : function (object, newdata, n.ahead = 1L, se.fit = TRUE, ...) NULL predict.arima0 : function (object, n.ahead = 1, newxreg = NULL, se.fit = TRUE, ...) NULL predict.bSpline : function (object, x, nseg = 50, deriv = 0, ...) NULL predict.bs : function (object, newx, ...) NULL predict.glm : function (object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE, dispersion = NULL, terms = NULL, na.action = na.pass, ...) NULL predict.glmmPQL : function (object, newdata = NULL, type = c("link", "response"), level = Q, na.action = na.pass, ...) NULL predict.lda : function (object, newdata, prior = object$prior, dimen, method = c("plug-in", "predictive", "debiased"), ...) NULL predict.lm : function (object, newdata, se.fit = FALSE, scale = NULL, df = Inf, interval = c("none", "confidence", "prediction"), level = 0.95, type = c("response", "terms"), terms = NULL, na.action = na.pass, pred.var = res.var/weights, weights = 1, ...) NULL predict.loess : function (object, newdata = NULL, se = FALSE, na.action = na.pass, ...) NULL predict.lqs : function (object, newdata, na.action = na.pass, ...) NULL predict.mca : function (object, newdata, type = c("row", "factor"), ...) NULL predict.mlm : function (object, newdata, se.fit = FALSE, na.action = na.pass, ...) NULL predict.nbSpline : function (object, x, nseg = 50, deriv = 0, ...) NULL predict.nls : function (object, newdata, se.fit = FALSE, scale = NULL, df = Inf, interval = c("none", "confidence", "prediction"), level = 0.95, ...) NULL predict.npolySpline : function (object, x, nseg = 50, deriv = 0, ...) NULL predict.ns : function (object, newx, ...) NULL predict.pbSpline : function (object, x, nseg = 50, deriv = 0, ...) NULL predict.polr : function (object, newdata, type = c("class", "probs"), ...) NULL predict.poly : function (object, newdata, ...) NULL predict.polySpline : function (object, x, nseg = 50, deriv = 0, ...) NULL predict.ppolySpline : function (object, x, nseg = 50, deriv = 0, ...) NULL predict.ppr : function (object, newdata, ...) NULL predict.prcomp : function (object, newdata, ...) NULL predict.princomp : function (object, newdata, ...) NULL predict.qda : function (object, newdata, prior = object$prior, method = c("plug-in", "predictive", "debiased", "looCV"), ...) NULL predict.rlm : function (object, newdata = NULL, scale = NULL, ...) NULL predict.smooth.spline : function (object, x, deriv = 0, ...) NULL predict.smooth.spline.fit : function (object, x, deriv = 0, ...) NULL > ## Don't show: > old <- Sys.setlocale("LC_COLLATE", old) > ## End Don't show > > > > cleanEx() > nameEx("predict.arima") > ### * predict.arima > > flush(stderr()); flush(stdout()) > > ### Name: predict.Arima > ### Title: Forecast from ARIMA fits > ### Aliases: predict.Arima > ### Keywords: ts > > ### ** Examples > > od <- options(digits=5) # avoid too much spurious accuracy > predict(arima(lh, order = c(3,0,0)), n.ahead = 12) $pred Time Series: Start = 49 End = 60 Frequency = 1 [1] 2.4602 2.2708 2.1986 2.2607 2.3470 2.4145 2.4389 2.4315 2.4102 2.3917 [11] 2.3827 2.3827 $se Time Series: Start = 49 End = 60 Frequency = 1 [1] 0.42268 0.50293 0.52452 0.52472 0.53055 0.53692 0.53880 0.53884 0.53910 [10] 0.53952 0.53970 0.53971 > > (fit <- arima(USAccDeaths, order = c(0,1,1), + seasonal = list(order=c(0,1,1)))) Call: arima(x = USAccDeaths, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1))) Coefficients: ma1 sma1 -0.430 -0.553 s.e. 0.123 0.178 sigma^2 estimated as 99347: log likelihood = -425.44, aic = 856.88 > predict(fit, n.ahead = 6) $pred Jan Feb Mar Apr May Jun 1979 8336.1 7531.8 8314.6 8616.9 9488.9 9859.8 $se Jan Feb Mar Apr May Jun 1979 315.45 363.01 405.02 443.06 478.09 510.72 > options(od) > > > > cleanEx() > nameEx("predict.glm") > ### * predict.glm > > flush(stderr()); flush(stdout()) > > ### Name: predict.glm > ### Title: Predict Method for GLM Fits > ### Aliases: predict.glm > ### Keywords: models regression > > ### ** Examples > > require(graphics) > > ## example from Venables and Ripley (2002, pp. 190-2.) > ldose <- rep(0:5, 2) > numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) > sex <- factor(rep(c("M", "F"), c(6, 6))) > SF <- cbind(numdead, numalive=20-numdead) > budworm.lg <- glm(SF ~ sex*ldose, family=binomial) > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.9935 0.5527 -5.416 6.09e-08 *** sexM 0.1750 0.7783 0.225 0.822 ldose 0.9060 0.1671 5.422 5.89e-08 *** sexM:ldose 0.3529 0.2700 1.307 0.191 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 124.8756 on 11 degrees of freedom Residual deviance: 4.9937 on 8 degrees of freedom AIC: 43.104 Number of Fisher Scoring iterations: 4 > > plot(c(1,32), c(0,1), type = "n", xlab = "dose", + ylab = "prob", log = "x") > text(2^ldose, numdead/20, as.character(sex)) > ld <- seq(0, 5, 0.1) > lines(2^ld, predict(budworm.lg, data.frame(ldose=ld, + sex=factor(rep("M", length(ld)), levels=levels(sex))), + type = "response")) > lines(2^ld, predict(budworm.lg, data.frame(ldose=ld, + sex=factor(rep("F", length(ld)), levels=levels(sex))), + type = "response")) > > > > cleanEx() > nameEx("predict.lm") > ### * predict.lm > > flush(stderr()); flush(stdout()) > > ### Name: predict.lm > ### Title: Predict method for Linear Model Fits > ### Aliases: predict.lm predict.mlm > ### Keywords: regression > > ### ** Examples > > require(graphics) > > ## Predictions > x <- rnorm(15) > y <- x + rnorm(15) > predict(lm(y ~ x)) 1 2 3 4 5 6 7 -0.6270917 0.2550840 -0.8548779 1.7923222 0.4139268 -0.8383687 0.5858991 8 9 10 11 12 13 14 0.8591183 0.6821126 -0.2774594 1.7013932 0.4796306 -0.6214146 -2.3566523 15 1.2801229 > new <- data.frame(x = seq(-3, 3, 0.5)) > predict(lm(y ~ x), new, se.fit = TRUE) $fit 1 2 3 4 5 6 -3.21182462 -2.66733702 -2.12284942 -1.57836182 -1.03387422 -0.48938662 7 8 9 10 11 12 0.05510098 0.59958858 1.14407618 1.68856379 2.23305139 2.77753899 13 3.32202659 $se.fit 1 2 3 4 5 6 7 8 0.7545246 0.6449274 0.5380108 0.4357521 0.3423511 0.2672616 0.2292452 0.2461261 9 10 11 12 13 0.3090353 0.3966456 0.4960370 0.6013965 0.7100725 $df [1] 13 $residual.scale [1] 0.8832292 > pred.w.plim <- predict(lm(y ~ x), new, interval="prediction") > pred.w.clim <- predict(lm(y ~ x), new, interval="confidence") > matplot(new$x,cbind(pred.w.clim, pred.w.plim[,-1]), + lty=c(1,2,2,3,3), type="l", ylab="predicted y") > > ## Prediction intervals, special cases > ## The first three of these throw warnings > w <- 1 + x^2 > fit <- lm(y ~ x) > wfit <- lm(y ~ x, weights = w) > predict(fit, interval = "prediction") Warning in predict.lm(fit, interval = "prediction") : Predictions on current data refer to _future_ responses fit lwr upr 1 -0.6270917 -2.6311852 1.37700188 2 0.2550840 -1.7160306 2.22619860 3 -0.8548779 -2.8806560 1.17090027 4 1.7923222 -0.3158417 3.90048609 5 0.4139268 -1.5600794 2.38793297 6 -0.8383687 -2.8624006 1.18566313 7 0.5858991 -1.3942769 2.56607519 8 0.8591183 -1.1372816 2.85551813 9 0.6821126 -1.3028836 2.66710884 10 -0.2774594 -2.2586227 1.70370390 11 1.7013932 -0.3922734 3.79505973 12 0.4796306 -1.4963611 2.45562227 13 -0.6214146 -2.6250348 1.38220556 14 -2.3566523 -4.6435457 -0.06975883 15 1.2801229 -0.7562734 3.31651909 > predict(wfit, interval = "prediction") Warning in predict.lm(wfit, interval = "prediction") : Predictions on current data refer to _future_ responses Warning in predict.lm(wfit, interval = "prediction") : Assuming prediction variance inversely proportional to weights used for fitting fit lwr upr 1 -0.6795401 -2.7152943 1.3562142 2 0.2726114 -2.0676598 2.6128825 3 -0.9253947 -2.7873163 0.9365269 4 1.9317861 0.5179227 3.3456495 5 0.4440538 -1.8206181 2.7087257 6 -0.9075760 -2.7817747 0.9666227 7 0.6296673 -1.5219043 2.7812390 8 0.9245587 -1.0206112 2.8697287 9 0.7335126 -1.3468740 2.8138992 10 -0.3021743 -2.5805044 1.9761557 11 1.8336444 0.3867018 3.2805870 12 0.5149694 -1.7094216 2.7393604 13 -0.6734127 -2.7135353 1.3667099 14 -2.5462925 -3.8073357 -1.2852494 15 1.3789581 -0.2749888 3.0329051 > predict(wfit, new, interval = "prediction") Warning in predict.lm(wfit, new, interval = "prediction") : Assuming constant prediction variance even though model fit is weighted fit lwr upr 1 -3.46929871 -6.02669183 -0.9119056 2 -2.88162137 -5.38456191 -0.3786808 3 -2.29394403 -4.75159401 0.1637059 4 -1.70626669 -4.12830217 0.7157688 5 -1.11858935 -3.51511782 1.2779391 6 -0.53091201 -2.91236575 1.8505417 7 0.05676532 -2.32024444 2.4337751 8 0.64444266 -1.73881338 3.0276987 9 1.23212000 -1.16798909 3.6322291 10 1.81979734 -0.60755066 4.2471453 11 2.40747468 -0.05715376 4.8721031 12 2.99515202 0.48364874 5.5066553 13 3.58282936 1.01538228 6.1502764 > predict(wfit, new, interval = "prediction", weights = (new$x)^2) fit lwr upr 1 -3.46929871 -4.7661745 -2.17242292 2 -2.88162137 -4.1752786 -1.58796414 3 -2.29394403 -3.6870669 -0.90082115 4 -1.70626669 -3.3884596 -0.02407382 5 -1.11858935 -3.5151178 1.27793911 6 -0.53091201 -5.2286061 4.16678211 7 0.05676532 -Inf Inf 8 0.64444266 -4.0541654 5.34305071 9 1.23212000 -1.1679891 3.63222909 10 1.81979734 0.1299644 3.50963027 11 2.40747468 1.0020775 3.81287188 12 2.99515202 1.6850045 4.30529950 13 3.58282936 2.2662384 4.89942029 > predict(wfit, new, interval = "prediction", weights = ~x^2) fit lwr upr 1 -3.46929871 -4.7661745 -2.17242292 2 -2.88162137 -4.1752786 -1.58796414 3 -2.29394403 -3.6870669 -0.90082115 4 -1.70626669 -3.3884596 -0.02407382 5 -1.11858935 -3.5151178 1.27793911 6 -0.53091201 -5.2286061 4.16678211 7 0.05676532 -Inf Inf 8 0.64444266 -4.0541654 5.34305071 9 1.23212000 -1.1679891 3.63222909 10 1.81979734 0.1299644 3.50963027 11 2.40747468 1.0020775 3.81287188 12 2.99515202 1.6850045 4.30529950 13 3.58282936 2.2662384 4.89942029 > > > > cleanEx() > nameEx("predict.loess") > ### * predict.loess > > flush(stderr()); flush(stdout()) > > ### Name: predict.loess > ### Title: Predict Loess Curve or Surface > ### Aliases: predict.loess > ### Keywords: smooth > > ### ** Examples > > cars.lo <- loess(dist ~ speed, cars) > predict(cars.lo, data.frame(speed=seq(5, 30, 1)), se=TRUE) $fit 1 2 3 4 5 6 7 8 7.797353 10.002308 12.499786 15.281082 18.446568 21.865315 25.517015 29.350386 9 10 11 12 13 14 15 16 33.230660 37.167935 41.205226 45.055736 48.355889 49.824812 51.986702 56.461318 17 18 19 20 21 22 23 24 61.959729 68.569313 76.316068 85.212121 95.324047 NA NA NA 25 26 NA NA $se.fit 1 2 3 4 5 6 7 8 7.568120 5.945831 4.990827 4.545284 4.308639 4.115049 3.789542 3.716231 9 10 11 12 13 14 15 16 3.776947 4.091747 4.709568 4.245427 4.035929 3.753410 4.004705 4.043190 17 18 19 20 21 22 23 24 4.026105 4.074664 4.570818 5.954217 8.302014 NA NA NA 25 26 NA NA $residual.scale [1] 15.29496 $df [1] 44.6179 > # to get extrapolation > cars.lo2 <- loess(dist ~ speed, cars, + control=loess.control(surface="direct")) > predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE) $fit 1 2 3 4 5 6 7 7.741006 9.926596 12.442424 15.281082 18.425712 21.865315 25.713413 8 9 10 11 12 13 14 29.350386 33.230660 37.167935 41.205226 45.781544 48.355889 50.067148 15 16 17 18 19 20 21 51.986702 56.445263 62.025404 68.569313 76.193111 85.053364 95.300523 22 23 24 25 26 106.974661 120.092581 134.665851 150.698545 168.190283 $se.fit 1 2 3 4 5 6 7 8 7.565991 5.959097 5.012013 4.550013 4.321596 4.119331 3.939804 3.720098 9 10 11 12 13 14 15 16 3.780877 4.096004 4.714469 4.398936 4.040129 4.184257 4.008873 4.061865 17 18 19 20 21 22 23 24 4.033998 4.078904 4.584606 5.952480 8.306901 11.601911 15.792480 20.864660 25 26 26.823827 33.683999 $residual.scale [1] 15.31087 $df [1] 44.55085 > > > > cleanEx() > nameEx("predict.nls") > ### * predict.nls > > flush(stderr()); flush(stdout()) > > ### Name: predict.nls > ### Title: Predicting from Nonlinear Least Squares Fits > ### Aliases: predict.nls > ### Keywords: nonlinear regression models > > ### ** Examples > > ## Don't show: > od <- options(digits=5) > ## End Don't show > require(graphics) > > fm <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD) > predict(fm) # fitted values at observed times [1] 7.8874 12.5250 15.2517 16.8549 17.7975 18.6776 > ## Form data plot and smooth line for the predictions > opar <- par(las = 1) > plot(demand ~ Time, data = BOD, col = 4, + main = "BOD data and fitted first-order curve", + xlim = c(0,7), ylim = c(0, 20) ) > tt <- seq(0, 8, length = 101) > lines(tt, predict(fm, list(Time = tt))) > par(opar) > ## Don't show: > options(od) > ## End Don't show > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("predict.smooth.spline") > ### * predict.smooth.spline > > flush(stderr()); flush(stdout()) > > ### Name: predict.smooth.spline > ### Title: Predict from Smoothing Spline Fit > ### Aliases: predict.smooth.spline > ### Keywords: smooth > > ### ** Examples > > require(graphics) > > attach(cars) > cars.spl <- smooth.spline(speed, dist, df=6.4) > ## Don't show: > print.default(cars.spl) $x [1] 4 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 25 $y [1] 5.833836 12.575603 15.037250 17.795007 20.935898 24.228479 28.488199 [8] 33.969567 38.578804 40.986090 43.494687 47.156344 50.862366 52.692043 [15] 54.754531 66.036824 75.010419 85.290273 95.014670 $w [1] 2 2 1 1 3 2 4 4 4 3 2 3 4 3 5 1 1 4 1 $yin [1] 6.00000 13.00000 16.00000 10.00000 26.00000 22.50000 21.50000 35.00000 [9] 50.50000 33.33333 36.00000 40.66667 64.50000 50.00000 50.40000 66.00000 [17] 54.00000 93.75000 85.00000 $data $data$x [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15 [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25 $data$y [1] 2 10 4 22 16 10 18 26 34 17 28 14 20 24 28 26 34 34 46 [20] 26 36 60 80 20 26 54 32 40 32 40 50 42 56 76 84 36 46 68 [39] 32 48 52 56 64 66 54 70 92 93 120 85 $data$w [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [39] 1 1 1 1 1 1 1 1 1 1 1 1 $lev [1] 0.8934338 0.4379689 0.1842314 0.1529048 0.3641292 0.2022506 0.3399561 [8] 0.3123173 0.3248036 0.2745481 0.1965797 0.2777060 0.3368286 0.2546068 [15] 0.5210306 0.1743541 0.1531692 0.6249904 0.3750748 $cv.crit [1] 257.2678 $pen.crit [1] 3015.936 $crit [1] 3 $df [1] 6.400884 $spar [1] 0.4873957 $lambda [1] 0.0008526606 $iparms icrit ispar iter 3 0 13 $fit $knot [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 0.1904762 0.2380952 [8] 0.2857143 0.3333333 0.3809524 0.4285714 0.4761905 0.5238095 0.5714286 [15] 0.6190476 0.6666667 0.7142857 0.7619048 0.8571429 0.9047619 0.9523810 [22] 1.0000000 1.0000000 1.0000000 1.0000000 $nk [1] 21 $min [1] 4 $range [1] 21 $coef [1] 5.833836 8.018090 10.929996 14.991332 17.703639 20.964154 24.055137 [8] 28.186176 34.129352 39.113818 40.888197 43.249931 47.080201 51.367328 [15] 52.624683 54.840036 62.675393 74.718525 85.502238 91.843521 95.014670 attr(,"class") [1] "smooth.spline.fit" $call smooth.spline(x = speed, y = dist, df = 6.4) attr(,"class") [1] "smooth.spline" > ## End Don't show > > ## "Proof" that the derivatives are okay, by comparing with approximation > diff.quot <- function(x,y) { + ## Difference quotient (central differences where available) + n <- length(x); i1 <- 1:2; i2 <- (n-1):n + c(diff(y[i1]) / diff(x[i1]), (y[-i1] - y[-i2]) / (x[-i1] - x[-i2]), + diff(y[i2]) / diff(x[i2])) + } > > xx <- unique(sort(c(seq(0,30, by = .2), kn <- unique(speed)))) > i.kn <- match(kn, xx)# indices of knots within xx > op <- par(mfrow = c(2,2)) > plot(speed, dist, xlim = range(xx), main = "Smooth.spline & derivatives") > lines(pp <- predict(cars.spl, xx), col = "red") > points(kn, pp$y[i.kn], pch = 3, col="dark red") > mtext("s(x)", col = "red") > for(d in 1:3){ + n <- length(pp$x) + plot(pp$x, diff.quot(pp$x,pp$y), type = 'l', xlab="x", ylab="", + col = "blue", col.main = "red", + main= paste0("s",paste(rep("'",d), collapse=""),"(x)")) + mtext("Difference quotient approx.(last)", col = "blue") + lines(pp <- predict(cars.spl, xx, deriv = d), col = "red") + ## Don't show: + print(pp) + ## End Don't show + points(kn, pp$y[i.kn], pch = 3, col="dark red") + abline(h=0, lty = 3, col = "gray") + } $x [1] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 [16] 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 [31] 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 [46] 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 [61] 12.0 12.2 12.4 12.6 12.8 13.0 13.2 13.4 13.6 13.8 14.0 14.2 14.4 14.6 14.8 [76] 15.0 15.2 15.4 15.6 15.8 16.0 16.2 16.4 16.6 16.8 17.0 17.2 17.4 17.6 17.8 [91] 18.0 18.2 18.4 18.6 18.8 19.0 19.2 19.4 19.6 19.8 20.0 20.2 20.4 20.6 20.8 [106] 21.0 21.2 21.4 21.6 21.8 22.0 22.2 22.4 22.6 22.8 23.0 23.2 23.4 23.6 23.8 [121] 24.0 24.2 24.4 24.6 24.8 25.0 25.2 25.4 25.6 25.8 26.0 26.2 26.4 26.6 26.8 [136] 27.0 27.2 27.4 27.6 27.8 28.0 28.2 28.4 28.6 28.8 29.0 29.2 29.4 29.6 29.8 [151] 30.0 $y [1] 2.184254 2.184254 2.184254 2.184254 2.184254 2.184254 2.184254 [8] 2.184254 2.184254 2.184254 2.184254 2.184254 2.184254 2.184254 [15] 2.184254 2.184254 2.184254 2.184254 2.184254 2.184254 2.184254 [22] 2.185055 2.187545 2.191723 2.197591 2.205146 2.214391 2.225324 [29] 2.237946 2.252257 2.268256 2.285944 2.305321 2.326387 2.349141 [36] 2.373584 2.401852 2.436084 2.476278 2.522435 2.574554 2.635109 [43] 2.706573 2.788944 2.882223 2.986411 3.081697 3.148275 3.186142 [50] 3.195300 3.175749 3.166034 3.204703 3.291756 3.427192 3.611011 [57] 3.834464 4.088800 4.374019 4.690122 5.037108 5.344118 5.540295 [64] 5.625638 5.600147 5.463821 5.227052 4.900227 4.483347 3.976412 [71] 3.379422 2.813354 2.399183 2.136910 2.026534 2.068056 2.203151 [78] 2.373493 2.579082 2.819918 3.096002 3.369475 3.602482 3.795021 [85] 3.947094 4.058699 4.080338 3.962512 3.705220 3.308463 2.772241 [92] 2.234965 1.835047 1.572485 1.447281 1.459435 1.588383 1.813566 [99] 2.134982 2.552632 3.066516 3.621455 4.162272 4.688967 5.201539 [106] 5.699989 6.184317 6.654523 7.110606 7.552567 7.980405 8.394132 [113] 8.793758 9.179284 9.550708 9.908031 10.197841 10.366724 10.414682 [120] 10.341713 10.147818 9.918957 9.741090 9.614215 9.538335 9.513448 [127] 9.513448 9.513448 9.513448 9.513448 9.513448 9.513448 9.513448 [134] 9.513448 9.513448 9.513448 9.513448 9.513448 9.513448 9.513448 [141] 9.513448 9.513448 9.513448 9.513448 9.513448 9.513448 9.513448 [148] 9.513448 9.513448 9.513448 9.513448 $x [1] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 [16] 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 [31] 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 [46] 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 [61] 12.0 12.2 12.4 12.6 12.8 13.0 13.2 13.4 13.6 13.8 14.0 14.2 14.4 14.6 14.8 [76] 15.0 15.2 15.4 15.6 15.8 16.0 16.2 16.4 16.6 16.8 17.0 17.2 17.4 17.6 17.8 [91] 18.0 18.2 18.4 18.6 18.8 19.0 19.2 19.4 19.6 19.8 20.0 20.2 20.4 20.6 20.8 [106] 21.0 21.2 21.4 21.6 21.8 22.0 22.2 22.4 22.6 22.8 23.0 23.2 23.4 23.6 23.8 [121] 24.0 24.2 24.4 24.6 24.8 25.0 25.2 25.4 25.6 25.8 26.0 26.2 26.4 26.6 26.8 [136] 27.0 27.2 27.4 27.6 27.8 28.0 28.2 28.4 28.6 28.8 29.0 29.2 29.4 29.6 29.8 [151] 30.0 $y [1] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [6] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [11] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [16] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [21] -0.0002165147 0.0082269950 0.0166705047 0.0251140144 0.0335575241 [26] 0.0420010338 0.0504445435 0.0588880532 0.0673315629 0.0757750726 [31] 0.0842185822 0.0926620919 0.1011056016 0.1095491113 0.1179926210 [36] 0.1264361307 0.1562499762 0.1860638217 0.2158776671 0.2456915126 [41] 0.2755053581 0.3300457726 0.3845861870 0.4391266015 0.4936670160 [46] 0.5482074305 0.4046596591 0.2611118877 0.1175641163 -0.0259836551 [51] -0.1695314264 0.0723860374 0.3143035013 0.5562209652 0.7981384291 [56] 1.0400558930 1.1944722898 1.3488886866 1.5033050835 1.6577214803 [61] 1.8121378771 1.2579681380 0.7037983989 0.1496286598 -0.4045410793 [66] -0.9587108185 -1.4089861032 -1.8592613879 -2.3095366727 -2.7598119574 [71] -3.2100872422 -2.4505987906 -1.6911103389 -0.9316218873 -0.1721334357 [76] 0.5873550159 0.7635912416 0.9398274674 1.1160636931 1.2922999189 [81] 1.4685361447 1.2662005031 1.0638648615 0.8615292198 0.6591935782 [86] 0.4568579366 -0.2404682740 -0.9377944846 -1.6351206952 -2.3324469058 [91] -3.0297731165 -2.3429865119 -1.6561999074 -0.9694133029 -0.2826266984 [96] 0.4041599061 0.8853283585 1.3664968109 1.8476652633 2.3288337157 [101] 2.8100021681 2.7393907024 2.6687792367 2.5981677710 2.5275563053 [106] 2.4569448396 2.3863333739 2.3157219082 2.2451104425 2.1744989768 [111] 2.1038875112 2.0333827893 1.9628780674 1.8923733455 1.8218686237 [116] 1.7513639018 1.1467332516 0.5421026014 -0.0625280487 -0.6671586989 [121] -1.2717893491 -1.0168219086 -0.7618544680 -0.5068870274 -0.2519195869 [126] 0.0030478537 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [131] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [136] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [141] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [146] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 [151] 0.0000000000 $x [1] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 [16] 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 [31] 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 [46] 9.0 9.2 9.4 9.6 9.8 10.0 10.2 10.4 10.6 10.8 11.0 11.2 11.4 11.6 11.8 [61] 12.0 12.2 12.4 12.6 12.8 13.0 13.2 13.4 13.6 13.8 14.0 14.2 14.4 14.6 14.8 [76] 15.0 15.2 15.4 15.6 15.8 16.0 16.2 16.4 16.6 16.8 17.0 17.2 17.4 17.6 17.8 [91] 18.0 18.2 18.4 18.6 18.8 19.0 19.2 19.4 19.6 19.8 20.0 20.2 20.4 20.6 20.8 [106] 21.0 21.2 21.4 21.6 21.8 22.0 22.2 22.4 22.6 22.8 23.0 23.2 23.4 23.6 23.8 [121] 24.0 24.2 24.4 24.6 24.8 25.0 25.2 25.4 25.6 25.8 26.0 26.2 26.4 26.6 26.8 [136] 27.0 27.2 27.4 27.6 27.8 28.0 28.2 28.4 28.6 28.8 29.0 29.2 29.4 29.6 29.8 [151] 30.0 $y [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [19] 0.00000000 0.00000000 0.04221755 0.04221755 0.04221755 0.04221755 [25] 0.04221755 0.04221755 0.04221755 0.04221755 0.04221755 0.04221755 [31] 0.04221755 0.04221755 0.04221755 0.04221755 0.04221755 0.14906923 [37] 0.14906923 0.14906923 0.14906923 0.14906923 0.27270207 0.27270207 [43] 0.27270207 0.27270207 0.27270207 -0.71773886 -0.71773886 -0.71773886 [49] -0.71773886 -0.71773886 1.20958732 1.20958732 1.20958732 1.20958732 [55] 1.20958732 0.77208198 0.77208198 0.77208198 0.77208198 0.77208198 [61] -2.77084870 -2.77084870 -2.77084870 -2.77084870 -2.77084870 -2.25137642 [67] -2.25137642 -2.25137642 -2.25137642 -2.25137642 3.79744226 3.79744226 [73] 3.79744226 3.79744226 3.79744226 0.88118113 0.88118113 0.88118113 [79] 0.88118113 0.88118113 -1.01167821 -1.01167821 -1.01167821 -1.01167821 [85] -1.01167821 -3.48663105 -3.48663105 -3.48663105 -3.48663105 -3.48663105 [91] 3.43393302 3.43393302 3.43393302 3.43393302 3.43393302 2.40584226 [97] 2.40584226 2.40584226 2.40584226 2.40584226 -0.35305733 -0.35305733 [103] -0.35305733 -0.35305733 -0.35305733 -0.35305733 -0.35305733 -0.35305733 [109] -0.35305733 -0.35305733 -0.35252361 -0.35252361 -0.35252361 -0.35252361 [115] -0.35252361 -3.02315325 -3.02315325 -3.02315325 -3.02315325 -3.02315325 [121] 1.27483720 1.27483720 1.27483720 1.27483720 1.27483720 1.27483720 [127] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [133] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [139] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [145] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [151] 0.00000000 > detach(); par(op) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("princomp") > ### * princomp > > flush(stderr()); flush(stdout()) > > ### Name: princomp > ### Title: Principal Components Analysis > ### Aliases: princomp princomp.formula princomp.default plot.princomp > ### print.princomp predict.princomp > ### Keywords: multivariate > > ### ** Examples > > require(graphics) > > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <- princomp(USArrests)) # inappropriate Call: princomp(x = USArrests) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 82.890847 14.069560 6.424204 2.457837 4 variables and 50 observations. > princomp(USArrests, cor = TRUE) # =^= prcomp(USArrests, scale=TRUE) Call: princomp(x = USArrests, cor = TRUE) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 1.5748783 0.9948694 0.5971291 0.4164494 4 variables and 50 observations. > ## Similar, but different: > ## The standard deviations differ by a factor of sqrt(49/50) > > summary(pc.cr <- princomp(USArrests, cor = TRUE)) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604 0.2474413 0.0891408 0.04335752 Cumulative Proportion 0.6200604 0.8675017 0.9566425 1.00000000 > loadings(pc.cr) ## note that blank entries are small but not zero Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Murder -0.536 0.418 -0.341 0.649 Assault -0.583 0.188 -0.268 -0.743 UrbanPop -0.278 -0.873 -0.378 0.134 Rape -0.543 -0.167 0.818 Comp.1 Comp.2 Comp.3 Comp.4 SS loadings 1.00 1.00 1.00 1.00 Proportion Var 0.25 0.25 0.25 0.25 Cumulative Var 0.25 0.50 0.75 1.00 > plot(pc.cr) # shows a screeplot. > biplot(pc.cr) > > ## Formula interface > princomp(~ ., data = USArrests, cor = TRUE) Call: princomp(formula = ~., data = USArrests, cor = TRUE) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 1.5748783 0.9948694 0.5971291 0.4164494 4 variables and 50 observations. > > ## NA-handling > USArrests[1, 2] <- NA > pc.cr <- princomp(~ Murder + Assault + UrbanPop, + data = USArrests, na.action=na.exclude, cor = TRUE) > > ## (Simple) Robust PCA: > ## Classical: > (pc.cl <- princomp(stackloss)) Call: princomp(x = stackloss) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 13.596589 4.676077 2.617533 1.366320 4 variables and 21 observations. > ## Robust: > (pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss))) Call: princomp(x = stackloss, covmat = MASS::cov.rob(stackloss)) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 7.8322873 4.0077676 1.9114016 0.7624211 4 variables and 21 observations. > > > > cleanEx() > nameEx("print.ts") > ### * print.ts > > flush(stderr()); flush(stdout()) > > ### Name: print.ts > ### Title: Printing Time-Series Objects > ### Aliases: print.ts > ### Keywords: ts > > ### ** Examples > > print(ts(1:10, frequency = 7, start = c(12, 2)), calendar = TRUE) p1 p2 p3 p4 p5 p6 p7 12 1 2 3 4 5 6 13 7 8 9 10 > > > > cleanEx() > nameEx("printCoefmat") > ### * printCoefmat > > flush(stderr()); flush(stdout()) > > ### Name: printCoefmat > ### Title: Print Coefficient Matrices > ### Aliases: printCoefmat > ### Keywords: print > > ### ** Examples > > cmat <- cbind(rnorm(3, 10), sqrt(rchisq(3, 12))) > cmat <- cbind(cmat, cmat[,1]/cmat[,2]) > cmat <- cbind(cmat, 2*pnorm(-cmat[,3])) > colnames(cmat) <- c("Estimate", "Std.Err", "Z value", "Pr(>z)") > printCoefmat(cmat[,1:3]) Estimate Std.Err Z value [1,] 9.3735 4.4447 2.1089 [2,] 10.1836 3.5496 2.8689 [3,] 9.1644 2.7365 3.3490 > printCoefmat(cmat) Estimate Std.Err Z value Pr(>z) [1,] 9.3735 4.4447 2.1089 0.0349492 * [2,] 10.1836 3.5496 2.8689 0.0041185 ** [3,] 9.1644 2.7365 3.3490 0.0008111 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > options(show.coef.Pvalues = FALSE) > printCoefmat(cmat, digits=2) Estimate Std.Err Z value [1,] 9.4 4.4 2.1 [2,] 10.2 3.5 2.9 [3,] 9.2 2.7 3.3 > printCoefmat(cmat, digits=2, P.values = TRUE) Estimate Std.Err Z value Pr(>z) [1,] 9.4 4.4 2.1 0.035 * [2,] 10.2 3.5 2.9 0.004 ** [3,] 9.2 2.7 3.3 8e-04 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > options(show.coef.Pvalues = TRUE)# revert > > > > cleanEx() > nameEx("profile.nls") > ### * profile.nls > > flush(stderr()); flush(stdout()) > > ### Name: profile.nls > ### Title: Method for Profiling nls Objects > ### Aliases: profile.nls > ### Keywords: nonlinear regression models > > ### ** Examples > > ## Don't show: > od <- options(digits = 4) > ## End Don't show > # obtain the fitted object > fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD) > # get the profile for the fitted model: default level is too extreme > pr1 <- profile(fm1, alpha = 0.05) > # profiled values for the two parameters > pr1$A tau par.vals.A par.vals.lrc 1 -3.0873 13.59751 0.14033 2 -2.4957 14.46884 0.01607 3 -1.8906 15.41836 -0.11787 4 -1.2634 16.49088 -0.26877 5 -0.6080 17.75662 -0.44553 6 0.0000 19.14258 -0.63282 7 0.4998 20.52853 -0.80750 8 0.9465 22.06851 -0.98233 9 1.3780 23.98253 -1.17103 10 1.7908 26.44552 -1.37431 11 2.1841 29.75928 -1.59599 12 2.5559 34.43804 -1.84209 13 2.9028 41.42542 -2.12176 > pr1$lrc tau par.vals.A par.vals.lrc 1 -2.9550 42.395076 -2.184153 2 -2.5201 33.446683 -1.836926 3 -2.0570 28.124205 -1.547326 4 -1.5710 24.656989 -1.293871 5 -1.0653 22.245847 -1.063030 6 -0.5412 20.482887 -0.845157 7 0.0000 19.142578 -0.632822 8 0.5539 18.095015 -0.420487 9 1.1022 17.273414 -0.207623 10 1.6298 16.621533 0.007939 11 2.1335 16.086803 0.234850 12 2.6052 15.638807 0.485009 13 3.0276 15.265409 0.779458 > # see also example(plot.profile.nls) > ## Don't show: > options(od) > ## End Don't show > > > > cleanEx() > nameEx("proj") > ### * proj > > flush(stderr()); flush(stdout()) > > ### Name: proj > ### Title: Projections of Models > ### Aliases: proj proj.default proj.lm proj.aov proj.aovlist > ### Keywords: models > > ### ** Examples > > N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) > P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) > K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) > yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, + 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) > > npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), + K=factor(K), yield=yield) > npk.aov <- aov(yield ~ block + N*P*K, npk) > proj(npk.aov) (Intercept) block N P K N:P N:K 1 54.875 -0.850 -2.808333 -0.5916667 -1.991667 0.9416667 1.175 2 54.875 -0.850 2.808333 -0.5916667 1.991667 -0.9416667 1.175 3 54.875 -0.850 -2.808333 0.5916667 1.991667 -0.9416667 -1.175 4 54.875 -0.850 2.808333 0.5916667 -1.991667 0.9416667 -1.175 5 54.875 2.575 2.808333 0.5916667 1.991667 0.9416667 1.175 6 54.875 2.575 2.808333 -0.5916667 -1.991667 -0.9416667 -1.175 7 54.875 2.575 -2.808333 0.5916667 -1.991667 -0.9416667 1.175 8 54.875 2.575 -2.808333 -0.5916667 1.991667 0.9416667 -1.175 9 54.875 5.900 -2.808333 -0.5916667 1.991667 0.9416667 -1.175 10 54.875 5.900 2.808333 -0.5916667 -1.991667 -0.9416667 -1.175 11 54.875 5.900 2.808333 0.5916667 1.991667 0.9416667 1.175 12 54.875 5.900 -2.808333 0.5916667 -1.991667 -0.9416667 1.175 13 54.875 -4.750 2.808333 0.5916667 1.991667 0.9416667 1.175 14 54.875 -4.750 2.808333 -0.5916667 -1.991667 -0.9416667 -1.175 15 54.875 -4.750 -2.808333 0.5916667 -1.991667 -0.9416667 1.175 16 54.875 -4.750 -2.808333 -0.5916667 1.991667 0.9416667 -1.175 17 54.875 -4.350 2.808333 -0.5916667 1.991667 -0.9416667 1.175 18 54.875 -4.350 -2.808333 0.5916667 1.991667 -0.9416667 -1.175 19 54.875 -4.350 2.808333 0.5916667 -1.991667 0.9416667 -1.175 20 54.875 -4.350 -2.808333 -0.5916667 -1.991667 0.9416667 1.175 21 54.875 1.475 2.808333 0.5916667 -1.991667 0.9416667 -1.175 22 54.875 1.475 2.808333 -0.5916667 1.991667 -0.9416667 1.175 23 54.875 1.475 -2.808333 -0.5916667 -1.991667 0.9416667 1.175 24 54.875 1.475 -2.808333 0.5916667 1.991667 -0.9416667 -1.175 P:K Residuals 1 0.1416667 -1.39166667 2 -0.1416667 4.47500000 3 0.1416667 -5.02500000 4 -0.1416667 1.94166667 5 0.1416667 -5.30000000 6 0.1416667 2.80000000 7 -0.1416667 2.16666667 8 -0.1416667 0.33333333 9 -0.1416667 3.80833333 10 0.1416667 -3.22500000 11 0.1416667 1.07500000 12 -0.1416667 -1.65833333 13 0.1416667 4.22500000 14 0.1416667 0.42500000 15 -0.1416667 -0.50833333 16 -0.1416667 -4.14166667 17 -0.1416667 -2.82500000 18 0.1416667 3.17500000 19 -0.1416667 -1.75833333 20 0.1416667 1.40833333 21 -0.1416667 -0.18333333 22 -0.1416667 -1.65000000 23 0.1416667 -0.01666667 24 0.1416667 1.85000000 attr(,"df") (Intercept) block N P K N:P 1 5 1 1 1 1 N:K P:K Residuals 1 1 12 attr(,"formula") yield ~ block + N * P * K attr(,"onedf") [1] FALSE attr(,"factors") attr(,"factors")$`(Intercept)` [1] "(Intercept)" attr(,"factors")$block [1] "block" attr(,"factors")$N [1] "N" attr(,"factors")$P [1] "P" attr(,"factors")$K [1] "K" attr(,"factors")$`N:P` [1] "N" "P" attr(,"factors")$`N:K` [1] "N" "K" attr(,"factors")$`P:K` [1] "P" "K" attr(,"factors")$Residuals [1] "block" "N" "P" "K" "Within" attr(,"call") aov(formula = yield ~ block + N * P * K, data = npk) attr(,"t.factor") block N P K N:P N:K P:K N:P:K yield 0 0 0 0 0 0 0 0 block 1 0 0 0 0 0 0 0 N 0 1 0 0 1 1 0 1 P 0 0 1 0 1 0 1 1 K 0 0 0 1 0 1 1 1 attr(,"class") [1] "aovproj" > > ## as a test, not particularly sensible > options(contrasts=c("contr.helmert", "contr.treatment")) > npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) > proj(npk.aovE) (Intercept) : (Intercept) 1 54.875 2 54.875 3 54.875 4 54.875 5 54.875 6 54.875 7 54.875 8 54.875 9 54.875 10 54.875 11 54.875 12 54.875 13 54.875 14 54.875 15 54.875 16 54.875 17 54.875 18 54.875 19 54.875 20 54.875 21 54.875 22 54.875 23 54.875 24 54.875 attr(,"df") attr(,"df")$df (Intercept) 1 attr(,"onedf") attr(,"onedf")$onedf [1] FALSE attr(,"factors") attr(,"factors")$`(Intercept)` [1] "(Intercept)" block : N:P:K Residuals 1 -1.241667 0.3916667 2 -1.241667 0.3916667 3 -1.241667 0.3916667 4 -1.241667 0.3916667 5 1.241667 1.3333333 6 1.241667 1.3333333 7 1.241667 1.3333333 8 1.241667 1.3333333 9 1.241667 4.6583333 10 1.241667 4.6583333 11 1.241667 4.6583333 12 1.241667 4.6583333 13 1.241667 -5.9916667 14 1.241667 -5.9916667 15 1.241667 -5.9916667 16 1.241667 -5.9916667 17 -1.241667 -3.1083333 18 -1.241667 -3.1083333 19 -1.241667 -3.1083333 20 -1.241667 -3.1083333 21 -1.241667 2.7166667 22 -1.241667 2.7166667 23 -1.241667 2.7166667 24 -1.241667 2.7166667 attr(,"df") attr(,"df")$df N:P:K Residuals 1 4 attr(,"onedf") attr(,"onedf")$onedf [1] FALSE attr(,"factors") attr(,"factors")$`N:P:K` [1] "N" "P" "K" attr(,"factors")$Residuals [1] "block" Within : N P K N:P N:K P:K Residuals 1 -2.808333 -0.5916667 -1.991667 0.9416667 1.175 0.1416667 -1.39166667 2 2.808333 -0.5916667 1.991667 -0.9416667 1.175 -0.1416667 4.47500000 3 -2.808333 0.5916667 1.991667 -0.9416667 -1.175 0.1416667 -5.02500000 4 2.808333 0.5916667 -1.991667 0.9416667 -1.175 -0.1416667 1.94166667 5 2.808333 0.5916667 1.991667 0.9416667 1.175 0.1416667 -5.30000000 6 2.808333 -0.5916667 -1.991667 -0.9416667 -1.175 0.1416667 2.80000000 7 -2.808333 0.5916667 -1.991667 -0.9416667 1.175 -0.1416667 2.16666667 8 -2.808333 -0.5916667 1.991667 0.9416667 -1.175 -0.1416667 0.33333333 9 -2.808333 -0.5916667 1.991667 0.9416667 -1.175 -0.1416667 3.80833333 10 2.808333 -0.5916667 -1.991667 -0.9416667 -1.175 0.1416667 -3.22500000 11 2.808333 0.5916667 1.991667 0.9416667 1.175 0.1416667 1.07500000 12 -2.808333 0.5916667 -1.991667 -0.9416667 1.175 -0.1416667 -1.65833333 13 2.808333 0.5916667 1.991667 0.9416667 1.175 0.1416667 4.22500000 14 2.808333 -0.5916667 -1.991667 -0.9416667 -1.175 0.1416667 0.42500000 15 -2.808333 0.5916667 -1.991667 -0.9416667 1.175 -0.1416667 -0.50833333 16 -2.808333 -0.5916667 1.991667 0.9416667 -1.175 -0.1416667 -4.14166667 17 2.808333 -0.5916667 1.991667 -0.9416667 1.175 -0.1416667 -2.82500000 18 -2.808333 0.5916667 1.991667 -0.9416667 -1.175 0.1416667 3.17500000 19 2.808333 0.5916667 -1.991667 0.9416667 -1.175 -0.1416667 -1.75833333 20 -2.808333 -0.5916667 -1.991667 0.9416667 1.175 0.1416667 1.40833333 21 2.808333 0.5916667 -1.991667 0.9416667 -1.175 -0.1416667 -0.18333333 22 2.808333 -0.5916667 1.991667 -0.9416667 1.175 -0.1416667 -1.65000000 23 -2.808333 -0.5916667 -1.991667 0.9416667 1.175 0.1416667 -0.01666667 24 -2.808333 0.5916667 1.991667 -0.9416667 -1.175 0.1416667 1.85000000 attr(,"df") attr(,"df")$df N P K N:P N:K P:K Residuals 1 1 1 1 1 1 12 attr(,"onedf") attr(,"onedf")$onedf [1] FALSE attr(,"factors") attr(,"factors")$N [1] "N" attr(,"factors")$P [1] "P" attr(,"factors")$K [1] "K" attr(,"factors")$`N:P` [1] "N" "P" attr(,"factors")$`N:K` [1] "N" "K" attr(,"factors")$`P:K` [1] "P" "K" attr(,"factors")$Residuals [1] "block" "Within" > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("prop.test") > ### * prop.test > > flush(stderr()); flush(stdout()) > > ### Name: prop.test > ### Title: Test of Equal or Given Proportions > ### Aliases: prop.test > ### Keywords: htest > > ### ** Examples > > heads <- rbinom(1, size=100, prob = .5) > prop.test(heads, 100) # continuity correction TRUE by default 1-sample proportions test with continuity correction data: heads out of 100, null probability 0.5 X-squared = 0.09, df = 1, p-value = 0.7642 alternative hypothesis: true p is not equal to 0.5 95 percent confidence interval: 0.4183183 0.6201278 sample estimates: p 0.52 > prop.test(heads, 100, correct = FALSE) 1-sample proportions test without continuity correction data: heads out of 100, null probability 0.5 X-squared = 0.16, df = 1, p-value = 0.6892 alternative hypothesis: true p is not equal to 0.5 95 percent confidence interval: 0.4231658 0.6153545 sample estimates: p 0.52 > > ## Data from Fleiss (1981), p. 139. > ## H0: The null hypothesis is that the four populations from which > ## the patients were drawn have the same true proportion of smokers. > ## A: The alternative is that this proportion is different in at > ## least one of the populations. > > smokers <- c( 83, 90, 129, 70 ) > patients <- c( 86, 93, 136, 82 ) > prop.test(smokers, patients) 4-sample test for equality of proportions without continuity correction data: smokers out of patients X-squared = 12.6004, df = 3, p-value = 0.005585 alternative hypothesis: two.sided sample estimates: prop 1 prop 2 prop 3 prop 4 0.9651163 0.9677419 0.9485294 0.8536585 > > > > cleanEx() > nameEx("prop.trend.test") > ### * prop.trend.test > > flush(stderr()); flush(stdout()) > > ### Name: prop.trend.test > ### Title: Test for trend in proportions > ### Aliases: prop.trend.test > ### Keywords: htest > > ### ** Examples > > smokers <- c( 83, 90, 129, 70 ) > patients <- c( 86, 93, 136, 82 ) > prop.test(smokers, patients) 4-sample test for equality of proportions without continuity correction data: smokers out of patients X-squared = 12.6004, df = 3, p-value = 0.005585 alternative hypothesis: two.sided sample estimates: prop 1 prop 2 prop 3 prop 4 0.9651163 0.9677419 0.9485294 0.8536585 > prop.trend.test(smokers, patients) Chi-squared Test for Trend in Proportions data: smokers out of patients , using scores: 1 2 3 4 X-squared = 8.2249, df = 1, p-value = 0.004132 > prop.trend.test(smokers, patients,c(0,0,0,1)) Chi-squared Test for Trend in Proportions data: smokers out of patients , using scores: 0 0 0 1 X-squared = 12.1731, df = 1, p-value = 0.0004848 > > > > cleanEx() > nameEx("qqnorm") > ### * qqnorm > > flush(stderr()); flush(stdout()) > > ### Name: qqnorm > ### Title: Quantile-Quantile Plots > ### Aliases: qqnorm qqnorm.default qqplot qqline > ### Keywords: hplot distribution > > ### ** Examples > > require(graphics) > > y <- rt(200, df = 5) > qqnorm(y); qqline(y, col = 2) > qqplot(y, rt(300, df = 5)) > > qqnorm(precip, ylab = "Precipitation [in/yr] for 70 US cities") > > ## "QQ-Chisquare" : -------------------------- > y <- rchisq(500, df=3) > ## Q-Q plot for Chi^2 data against true theoretical distribution: > qqplot(qchisq(ppoints(500), df=3), y, + main = expression("Q-Q plot for" ~~ {chi^2}[nu==3])) > qqline(y, distribution = function(p) qchisq(p, df=3), + prob = c(0.1, 0.6), col=2) > mtext("qqline(*, dist = qchisq(., df=3), prob = c(0.1, 0.6))") > > > > cleanEx() > nameEx("quade.test") > ### * quade.test > > flush(stderr()); flush(stdout()) > > ### Name: quade.test > ### Title: Quade Test > ### Aliases: quade.test quade.test.default quade.test.formula > ### Keywords: htest > > ### ** Examples > > ## Conover (1999, p. 375f): > ## Numbers of five brands of a new hand lotion sold in seven stores > ## during one week. > y <- matrix(c( 5, 4, 7, 10, 12, + 1, 3, 1, 0, 2, + 16, 12, 22, 22, 35, + 5, 4, 3, 5, 4, + 10, 9, 7, 13, 10, + 19, 18, 28, 37, 58, + 10, 7, 6, 8, 7), + nrow = 7, byrow = TRUE, + dimnames = + list(Store = as.character(1:7), + Brand = LETTERS[1:5])) > y Brand Store A B C D E 1 5 4 7 10 12 2 1 3 1 0 2 3 16 12 22 22 35 4 5 4 3 5 4 5 10 9 7 13 10 6 19 18 28 37 58 7 10 7 6 8 7 > quade.test(y) Quade test data: y Quade F = 3.8293, num df = 4, denom df = 24, p-value = 0.01519 > > > > cleanEx() > nameEx("quantile") > ### * quantile > > flush(stderr()); flush(stdout()) > > ### Name: quantile > ### Title: Sample Quantiles > ### Aliases: quantile quantile.default > ### Keywords: univar > > ### ** Examples > > quantile(x <- rnorm(1001)) # Extremes & Quartiles by default 0% 25% 50% 75% 100% -3.00804860 -0.69731820 -0.03472603 0.68924373 3.81027668 > quantile(x, probs = c(0.1, 0.5, 1, 2, 5, 10, 50, NA)/100) 0.1% 0.5% 1% 2% 5% 10% -2.99694930 -2.59232767 -2.42431731 -2.24515257 -1.72663060 -1.33880074 50% -0.03472603 NA > > ### Compare different types > p <- c(0.1, 0.5, 1, 2, 5, 10, 50)/100 > res <- matrix(as.numeric(NA), 9, 7) > for(type in 1:9) res[type, ] <- y <- quantile(x, p, type = type) > dimnames(res) <- list(1:9, names(y)) > round(res, 3) 0.1% 0.5% 1% 2% 5% 10% 50% 1 -2.997 -2.592 -2.424 -2.245 -1.727 -1.339 -0.035 2 -2.997 -2.592 -2.424 -2.245 -1.727 -1.339 -0.035 3 -3.008 -2.596 -2.433 -2.265 -1.733 -1.344 -0.036 4 -3.008 -2.596 -2.433 -2.264 -1.733 -1.344 -0.035 5 -3.002 -2.594 -2.428 -2.255 -1.730 -1.341 -0.035 6 -3.008 -2.596 -2.432 -2.264 -1.733 -1.343 -0.035 7 -2.997 -2.592 -2.424 -2.245 -1.727 -1.339 -0.035 8 -3.004 -2.595 -2.430 -2.258 -1.731 -1.342 -0.035 9 -3.004 -2.595 -2.429 -2.257 -1.730 -1.341 -0.035 > > > > cleanEx() > nameEx("r2dtable") > ### * r2dtable > > flush(stderr()); flush(stdout()) > > ### Name: r2dtable > ### Title: Random 2-way Tables with Given Marginals > ### Aliases: r2dtable > ### Keywords: distribution > > ### ** Examples > > ## Fisher's Tea Drinker data. > TeaTasting <- + matrix(c(3, 1, 1, 3), + nrow = 2, + dimnames = list(Guess = c("Milk", "Tea"), + Truth = c("Milk", "Tea"))) > ## Simulate permutation test for independence based on the maximum > ## Pearson residuals (rather than their sum). > rowTotals <- rowSums(TeaTasting) > colTotals <- colSums(TeaTasting) > nOfCases <- sum(rowTotals) > expected <- outer(rowTotals, colTotals, "*") / nOfCases > maxSqResid <- function(x) max((x - expected) ^ 2 / expected) > simMaxSqResid <- + sapply(r2dtable(1000, rowTotals, colTotals), maxSqResid) > sum(simMaxSqResid >= maxSqResid(TeaTasting)) / 1000 [1] 0.465 > ## Fisher's exact test gives p = 0.4857 ... > > > > cleanEx() > nameEx("rWishart") > ### * rWishart > > flush(stderr()); flush(stdout()) > > ### Name: rWishart > ### Title: Random Wishart Distributed Matrices > ### Aliases: rWishart > ### Keywords: multivariate > > ### ** Examples > > ## Artificial > S <- toeplitz((10:1)/10) > set.seed(11) > R <- rWishart(1000, 20, S) > dim(R)# 10 10 1000 [1] 10 10 1000 > mR <- apply(R, 1:2, mean)# ~= E[ Wish(S, 20) ] = 20 * S > stopifnot(all.equal(mR, 20*S, tol = .009)) > > ## See Details, the variance is > Va <- 20*(S^2 + tcrossprod(diag(S))) > vR <- apply(R, 1:2, var) > stopifnot(all.equal(vR, Va, tol = 1/16)) > > > > cleanEx() > nameEx("read.ftable") > ### * read.ftable > > flush(stderr()); flush(stdout()) > > ### Name: read.ftable > ### Title: Manipulate Flat Contingency Tables > ### Aliases: read.ftable write.ftable format.ftable > ### Keywords: category > > ### ** Examples > > ## Agresti (1990), page 157, Table 5.8. > ## Not in ftable standard format, but o.k. > file <- tempfile() > cat(" Intercourse\n", + "Race Gender Yes No\n", + "White Male 43 134\n", + " Female 26 149\n", + "Black Male 29 23\n", + " Female 22 36\n", + file = file) > ft <- read.ftable(file) > ft Intercourse Yes No Race Gender White Male 43 134 Female 26 149 Black Male 29 23 Female 22 36 > unlink(file) > > ## Agresti (1990), page 297, Table 8.16. > ## Almost o.k., but misses the name of the row variable. > file <- tempfile() > cat(" \"Tonsil Size\"\n", + " \"Not Enl.\" \"Enl.\" \"Greatly Enl.\"\n", + "Noncarriers 497 560 269\n", + "Carriers 19 29 24\n", + file = file) > ft <- read.ftable(file, skip = 2, + row.var.names = "Status", + col.vars = list("Tonsil Size" = + c("Not Enl.", "Enl.", "Greatly Enl."))) > ft Tonsil Size Not Enl. Enl. Greatly Enl. Status Noncarriers 497 560 269 Carriers 19 29 24 > unlink(file) > > ft22 <- ftable(Titanic, row.vars = 2:1, col.vars = 4:3) > write.ftable(ft22, quote = FALSE) Survived No Yes Age Child Adult Child Adult Sex Class Male 1st 0 118 5 57 2nd 0 154 11 14 3rd 35 387 13 75 Crew 0 670 0 192 Female 1st 0 4 1 140 2nd 0 13 13 80 3rd 17 89 14 76 Crew 0 3 0 20 > ## Don't show: > stopifnot(dim(format(ft)) == 4:5, + dim(format(ftable(UCBAdmissions))) == c(6,9), + dim(format(ft22)) == c(11,7)) > ## End Don't show > > > > cleanEx() > nameEx("rect.hclust") > ### * rect.hclust > > flush(stderr()); flush(stdout()) > > ### Name: rect.hclust > ### Title: Draw Rectangles Around Hierarchical Clusters > ### Aliases: rect.hclust > ### Keywords: aplot cluster > > ### ** Examples > > require(graphics) > > hca <- hclust(dist(USArrests)) > plot(hca) > rect.hclust(hca, k=3, border="red") > x <- rect.hclust(hca, h=50, which=c(2,7), border=3:4) > x [[1]] Alabama Alaska Delaware Louisiana Mississippi 1 2 8 18 24 South Carolina 40 [[2]] Connecticut Idaho Indiana Kansas Kentucky Montana 7 12 14 16 17 26 Nebraska Ohio Pennsylvania Utah 27 35 38 44 > > > > cleanEx() > nameEx("relevel") > ### * relevel > > flush(stderr()); flush(stdout()) > > ### Name: relevel > ### Title: Reorder Levels of Factor > ### Aliases: relevel relevel.default relevel.factor relevel.ordered > ### Keywords: utilities models > > ### ** Examples > > warpbreaks$tension <- relevel(warpbreaks$tension, ref="M") > summary(lm(breaks ~ wool + tension, data=warpbreaks)) Call: lm(formula = breaks ~ wool + tension, data = warpbreaks) Residuals: Min 1Q Median 3Q Max -19.500 -8.083 -2.139 6.472 30.722 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 29.278 3.162 9.260 2e-12 *** woolB -5.778 3.162 -1.827 0.0736 . tensionL 10.000 3.872 2.582 0.0128 * tensionH -4.722 3.872 -1.219 0.2284 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.62 on 50 degrees of freedom Multiple R-squared: 0.2691, Adjusted R-squared: 0.2253 F-statistic: 6.138 on 3 and 50 DF, p-value: 0.00123 > > > > cleanEx() > nameEx("reorder.dendrogram") > ### * reorder.dendrogram > > flush(stderr()); flush(stdout()) > > ### Name: reorder.dendrogram > ### Title: Reorder a Dendrogram > ### Aliases: reorder.dendrogram > ### Keywords: manip > > ### ** Examples > > require(graphics) > > set.seed(123) > x <- rnorm(10) > hc <- hclust(dist(x)) > dd <- as.dendrogram(hc) > dd.reorder <- reorder(dd, 10:1) > plot(dd, main = "random dendrogram 'dd'") > > op <- par(mfcol = 1:2) > plot(dd.reorder, main = "reorder(dd, 10:1)") > plot(reorder(dd,10:1, agglo.FUN= mean), + main = "reorder(dd, 10:1, mean)") > par(op) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("reorder.factor") > ### * reorder.factor > > flush(stderr()); flush(stdout()) > > ### Name: reorder.default > ### Title: Reorder Levels of a Factor > ### Aliases: reorder reorder.default > ### Keywords: utilities > > ### ** Examples > > require(graphics) > > bymedian <- with(InsectSprays, reorder(spray, count, median)) > boxplot(count ~ bymedian, data = InsectSprays, + xlab = "Type of spray", ylab = "Insect count", + main = "InsectSprays data", varwidth = TRUE, + col = "lightgray") > > > > cleanEx() > nameEx("replications") > ### * replications > > flush(stderr()); flush(stdout()) > > ### Name: replications > ### Title: Number of Replications of Terms > ### Aliases: replications > ### Keywords: models > > ### ** Examples > > ## From Venables and Ripley (2002) p.165. > N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) > P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) > K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) > yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, + 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) > > npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P), + K=factor(K), yield=yield) > replications(~ . - yield, npk) block N P K 4 12 12 12 > > > > cleanEx() > nameEx("reshape") > ### * reshape > > flush(stderr()); flush(stdout()) > > ### Name: reshape > ### Title: Reshape Grouped Data > ### Aliases: reshape > ### Keywords: manip > > ### ** Examples > > summary(Indometh) Subject time conc 1:11 Min. :0.250 Min. :0.0500 4:11 1st Qu.:0.750 1st Qu.:0.1100 2:11 Median :2.000 Median :0.3400 5:11 Mean :2.886 Mean :0.5918 6:11 3rd Qu.:5.000 3rd Qu.:0.8325 3:11 Max. :8.000 Max. :2.7200 > wide <- reshape(Indometh, v.names = "conc", idvar = "Subject", + timevar = "time", direction = "wide") > wide Subject conc.0.25 conc.0.5 conc.0.75 conc.1 conc.1.25 conc.2 conc.3 conc.4 1 1 1.50 0.94 0.78 0.48 0.37 0.19 0.12 0.11 12 2 2.03 1.63 0.71 0.70 0.64 0.36 0.32 0.20 23 3 2.72 1.49 1.16 0.80 0.80 0.39 0.22 0.12 34 4 1.85 1.39 1.02 0.89 0.59 0.40 0.16 0.11 45 5 2.05 1.04 0.81 0.39 0.30 0.23 0.13 0.11 56 6 2.31 1.44 1.03 0.84 0.64 0.42 0.24 0.17 conc.5 conc.6 conc.8 1 0.08 0.07 0.05 12 0.25 0.12 0.08 23 0.11 0.08 0.08 34 0.10 0.07 0.07 45 0.08 0.10 0.06 56 0.13 0.10 0.09 > > reshape(wide, direction = "long") Subject time conc 1.0.25 1 0.25 1.50 2.0.25 2 0.25 2.03 3.0.25 3 0.25 2.72 4.0.25 4 0.25 1.85 5.0.25 5 0.25 2.05 6.0.25 6 0.25 2.31 1.0.5 1 0.50 0.94 2.0.5 2 0.50 1.63 3.0.5 3 0.50 1.49 4.0.5 4 0.50 1.39 5.0.5 5 0.50 1.04 6.0.5 6 0.50 1.44 1.0.75 1 0.75 0.78 2.0.75 2 0.75 0.71 3.0.75 3 0.75 1.16 4.0.75 4 0.75 1.02 5.0.75 5 0.75 0.81 6.0.75 6 0.75 1.03 1.1 1 1.00 0.48 2.1 2 1.00 0.70 3.1 3 1.00 0.80 4.1 4 1.00 0.89 5.1 5 1.00 0.39 6.1 6 1.00 0.84 1.1.25 1 1.25 0.37 2.1.25 2 1.25 0.64 3.1.25 3 1.25 0.80 4.1.25 4 1.25 0.59 5.1.25 5 1.25 0.30 6.1.25 6 1.25 0.64 1.2 1 2.00 0.19 2.2 2 2.00 0.36 3.2 3 2.00 0.39 4.2 4 2.00 0.40 5.2 5 2.00 0.23 6.2 6 2.00 0.42 1.3 1 3.00 0.12 2.3 2 3.00 0.32 3.3 3 3.00 0.22 4.3 4 3.00 0.16 5.3 5 3.00 0.13 6.3 6 3.00 0.24 1.4 1 4.00 0.11 2.4 2 4.00 0.20 3.4 3 4.00 0.12 4.4 4 4.00 0.11 5.4 5 4.00 0.11 6.4 6 4.00 0.17 1.5 1 5.00 0.08 2.5 2 5.00 0.25 3.5 3 5.00 0.11 4.5 4 5.00 0.10 5.5 5 5.00 0.08 6.5 6 5.00 0.13 1.6 1 6.00 0.07 2.6 2 6.00 0.12 3.6 3 6.00 0.08 4.6 4 6.00 0.07 5.6 5 6.00 0.10 6.6 6 6.00 0.10 1.8 1 8.00 0.05 2.8 2 8.00 0.08 3.8 3 8.00 0.08 4.8 4 8.00 0.07 5.8 5 8.00 0.06 6.8 6 8.00 0.09 > reshape(wide, idvar = "Subject", varying = list(2:12), + v.names = "conc", direction = "long") Subject time conc 1.1 1 1 1.50 2.1 2 1 2.03 3.1 3 1 2.72 4.1 4 1 1.85 5.1 5 1 2.05 6.1 6 1 2.31 1.2 1 2 0.94 2.2 2 2 1.63 3.2 3 2 1.49 4.2 4 2 1.39 5.2 5 2 1.04 6.2 6 2 1.44 1.3 1 3 0.78 2.3 2 3 0.71 3.3 3 3 1.16 4.3 4 3 1.02 5.3 5 3 0.81 6.3 6 3 1.03 1.4 1 4 0.48 2.4 2 4 0.70 3.4 3 4 0.80 4.4 4 4 0.89 5.4 5 4 0.39 6.4 6 4 0.84 1.5 1 5 0.37 2.5 2 5 0.64 3.5 3 5 0.80 4.5 4 5 0.59 5.5 5 5 0.30 6.5 6 5 0.64 1.6 1 6 0.19 2.6 2 6 0.36 3.6 3 6 0.39 4.6 4 6 0.40 5.6 5 6 0.23 6.6 6 6 0.42 1.7 1 7 0.12 2.7 2 7 0.32 3.7 3 7 0.22 4.7 4 7 0.16 5.7 5 7 0.13 6.7 6 7 0.24 1.8 1 8 0.11 2.8 2 8 0.20 3.8 3 8 0.12 4.8 4 8 0.11 5.8 5 8 0.11 6.8 6 8 0.17 1.9 1 9 0.08 2.9 2 9 0.25 3.9 3 9 0.11 4.9 4 9 0.10 5.9 5 9 0.08 6.9 6 9 0.13 1.10 1 10 0.07 2.10 2 10 0.12 3.10 3 10 0.08 4.10 4 10 0.07 5.10 5 10 0.10 6.10 6 10 0.10 1.11 1 11 0.05 2.11 2 11 0.08 3.11 3 11 0.08 4.11 4 11 0.07 5.11 5 11 0.06 6.11 6 11 0.09 > > ## times need not be numeric > df <- data.frame(id = rep(1:4, rep(2,4)), + visit = I(rep(c("Before","After"), 4)), + x = rnorm(4), y = runif(4)) > df id visit x y 1 1 Before -0.6264538 0.62911404 2 1 After 0.1836433 0.06178627 3 2 Before -0.8356286 0.20597457 4 2 After 1.5952808 0.17655675 5 3 Before -0.6264538 0.62911404 6 3 After 0.1836433 0.06178627 7 4 Before -0.8356286 0.20597457 8 4 After 1.5952808 0.17655675 > reshape(df, timevar = "visit", idvar = "id", direction = "wide") id x.Before y.Before x.After y.After 1 1 -0.6264538 0.6291140 0.1836433 0.06178627 3 2 -0.8356286 0.2059746 1.5952808 0.17655675 5 3 -0.6264538 0.6291140 0.1836433 0.06178627 7 4 -0.8356286 0.2059746 1.5952808 0.17655675 > ## warns that y is really varying > reshape(df, timevar = "visit", idvar = "id", direction = "wide", v.names = "x") Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying = varying, : some constant variables (y) are really varying id y x.Before x.After 1 1 0.6291140 -0.6264538 0.1836433 3 2 0.2059746 -0.8356286 1.5952808 5 3 0.6291140 -0.6264538 0.1836433 7 4 0.2059746 -0.8356286 1.5952808 > > > ## unbalanced 'long' data leads to NA fill in 'wide' form > df2 <- df[1:7, ] > df2 id visit x y 1 1 Before -0.6264538 0.62911404 2 1 After 0.1836433 0.06178627 3 2 Before -0.8356286 0.20597457 4 2 After 1.5952808 0.17655675 5 3 Before -0.6264538 0.62911404 6 3 After 0.1836433 0.06178627 7 4 Before -0.8356286 0.20597457 > reshape(df2, timevar = "visit", idvar = "id", direction = "wide") id x.Before y.Before x.After y.After 1 1 -0.6264538 0.6291140 0.1836433 0.06178627 3 2 -0.8356286 0.2059746 1.5952808 0.17655675 5 3 -0.6264538 0.6291140 0.1836433 0.06178627 7 4 -0.8356286 0.2059746 NA NA > > ## Alternative regular expressions for guessing names > df3 <- data.frame(id = 1:4, age = c(40,50,60,50), dose1 = c(1,2,1,2), + dose2 = c(2,1,2,1), dose4 = c(3,3,3,3)) > reshape(df3, direction = "long", varying = 3:5, sep = "") id age time dose 1.1 1 40 1 1 2.1 2 50 1 2 3.1 3 60 1 1 4.1 4 50 1 2 1.2 1 40 2 2 2.2 2 50 2 1 3.2 3 60 2 2 4.2 4 50 2 1 1.4 1 40 4 3 2.4 2 50 4 3 3.4 3 60 4 3 4.4 4 50 4 3 > > > ## an example that isn't longitudinal data > state.x77 <- as.data.frame(state.x77) > long <- reshape(state.x77, idvar = "state", ids = row.names(state.x77), + times = names(state.x77), timevar = "Characteristic", + varying = list(names(state.x77)), direction = "long") > > reshape(long, direction = "wide") state Population Income Illiteracy Life Exp Alabama.Population Alabama 3615 3624 2.1 69.05 Alaska.Population Alaska 365 6315 1.5 69.31 Arizona.Population Arizona 2212 4530 1.8 70.55 Arkansas.Population Arkansas 2110 3378 1.9 70.66 California.Population California 21198 5114 1.1 71.71 Colorado.Population Colorado 2541 4884 0.7 72.06 Connecticut.Population Connecticut 3100 5348 1.1 72.48 Delaware.Population Delaware 579 4809 0.9 70.06 Florida.Population Florida 8277 4815 1.3 70.66 Georgia.Population Georgia 4931 4091 2.0 68.54 Hawaii.Population Hawaii 868 4963 1.9 73.60 Idaho.Population Idaho 813 4119 0.6 71.87 Illinois.Population Illinois 11197 5107 0.9 70.14 Indiana.Population Indiana 5313 4458 0.7 70.88 Iowa.Population Iowa 2861 4628 0.5 72.56 Kansas.Population Kansas 2280 4669 0.6 72.58 Kentucky.Population Kentucky 3387 3712 1.6 70.10 Louisiana.Population Louisiana 3806 3545 2.8 68.76 Maine.Population Maine 1058 3694 0.7 70.39 Maryland.Population Maryland 4122 5299 0.9 70.22 Massachusetts.Population Massachusetts 5814 4755 1.1 71.83 Michigan.Population Michigan 9111 4751 0.9 70.63 Minnesota.Population Minnesota 3921 4675 0.6 72.96 Mississippi.Population Mississippi 2341 3098 2.4 68.09 Missouri.Population Missouri 4767 4254 0.8 70.69 Montana.Population Montana 746 4347 0.6 70.56 Nebraska.Population Nebraska 1544 4508 0.6 72.60 Nevada.Population Nevada 590 5149 0.5 69.03 New Hampshire.Population New Hampshire 812 4281 0.7 71.23 New Jersey.Population New Jersey 7333 5237 1.1 70.93 New Mexico.Population New Mexico 1144 3601 2.2 70.32 New York.Population New York 18076 4903 1.4 70.55 North Carolina.Population North Carolina 5441 3875 1.8 69.21 North Dakota.Population North Dakota 637 5087 0.8 72.78 Ohio.Population Ohio 10735 4561 0.8 70.82 Oklahoma.Population Oklahoma 2715 3983 1.1 71.42 Oregon.Population Oregon 2284 4660 0.6 72.13 Pennsylvania.Population Pennsylvania 11860 4449 1.0 70.43 Rhode Island.Population Rhode Island 931 4558 1.3 71.90 South Carolina.Population South Carolina 2816 3635 2.3 67.96 South Dakota.Population South Dakota 681 4167 0.5 72.08 Tennessee.Population Tennessee 4173 3821 1.7 70.11 Texas.Population Texas 12237 4188 2.2 70.90 Utah.Population Utah 1203 4022 0.6 72.90 Vermont.Population Vermont 472 3907 0.6 71.64 Virginia.Population Virginia 4981 4701 1.4 70.08 Washington.Population Washington 3559 4864 0.6 71.72 West Virginia.Population West Virginia 1799 3617 1.4 69.48 Wisconsin.Population Wisconsin 4589 4468 0.7 72.48 Wyoming.Population Wyoming 376 4566 0.6 70.29 Murder HS Grad Frost Area Alabama.Population 15.1 41.3 20 50708 Alaska.Population 11.3 66.7 152 566432 Arizona.Population 7.8 58.1 15 113417 Arkansas.Population 10.1 39.9 65 51945 California.Population 10.3 62.6 20 156361 Colorado.Population 6.8 63.9 166 103766 Connecticut.Population 3.1 56.0 139 4862 Delaware.Population 6.2 54.6 103 1982 Florida.Population 10.7 52.6 11 54090 Georgia.Population 13.9 40.6 60 58073 Hawaii.Population 6.2 61.9 0 6425 Idaho.Population 5.3 59.5 126 82677 Illinois.Population 10.3 52.6 127 55748 Indiana.Population 7.1 52.9 122 36097 Iowa.Population 2.3 59.0 140 55941 Kansas.Population 4.5 59.9 114 81787 Kentucky.Population 10.6 38.5 95 39650 Louisiana.Population 13.2 42.2 12 44930 Maine.Population 2.7 54.7 161 30920 Maryland.Population 8.5 52.3 101 9891 Massachusetts.Population 3.3 58.5 103 7826 Michigan.Population 11.1 52.8 125 56817 Minnesota.Population 2.3 57.6 160 79289 Mississippi.Population 12.5 41.0 50 47296 Missouri.Population 9.3 48.8 108 68995 Montana.Population 5.0 59.2 155 145587 Nebraska.Population 2.9 59.3 139 76483 Nevada.Population 11.5 65.2 188 109889 New Hampshire.Population 3.3 57.6 174 9027 New Jersey.Population 5.2 52.5 115 7521 New Mexico.Population 9.7 55.2 120 121412 New York.Population 10.9 52.7 82 47831 North Carolina.Population 11.1 38.5 80 48798 North Dakota.Population 1.4 50.3 186 69273 Ohio.Population 7.4 53.2 124 40975 Oklahoma.Population 6.4 51.6 82 68782 Oregon.Population 4.2 60.0 44 96184 Pennsylvania.Population 6.1 50.2 126 44966 Rhode Island.Population 2.4 46.4 127 1049 South Carolina.Population 11.6 37.8 65 30225 South Dakota.Population 1.7 53.3 172 75955 Tennessee.Population 11.0 41.8 70 41328 Texas.Population 12.2 47.4 35 262134 Utah.Population 4.5 67.3 137 82096 Vermont.Population 5.5 57.1 168 9267 Virginia.Population 9.5 47.8 85 39780 Washington.Population 4.3 63.5 32 66570 West Virginia.Population 6.7 41.6 100 24070 Wisconsin.Population 3.0 54.5 149 54464 Wyoming.Population 6.9 62.9 173 97203 > > reshape(long, direction = "wide", new.row.names = unique(long$state)) state Population Income Illiteracy Life Exp Murder Alabama Alabama 3615 3624 2.1 69.05 15.1 Alaska Alaska 365 6315 1.5 69.31 11.3 Arizona Arizona 2212 4530 1.8 70.55 7.8 Arkansas Arkansas 2110 3378 1.9 70.66 10.1 California California 21198 5114 1.1 71.71 10.3 Colorado Colorado 2541 4884 0.7 72.06 6.8 Connecticut Connecticut 3100 5348 1.1 72.48 3.1 Delaware Delaware 579 4809 0.9 70.06 6.2 Florida Florida 8277 4815 1.3 70.66 10.7 Georgia Georgia 4931 4091 2.0 68.54 13.9 Hawaii Hawaii 868 4963 1.9 73.60 6.2 Idaho Idaho 813 4119 0.6 71.87 5.3 Illinois Illinois 11197 5107 0.9 70.14 10.3 Indiana Indiana 5313 4458 0.7 70.88 7.1 Iowa Iowa 2861 4628 0.5 72.56 2.3 Kansas Kansas 2280 4669 0.6 72.58 4.5 Kentucky Kentucky 3387 3712 1.6 70.10 10.6 Louisiana Louisiana 3806 3545 2.8 68.76 13.2 Maine Maine 1058 3694 0.7 70.39 2.7 Maryland Maryland 4122 5299 0.9 70.22 8.5 Massachusetts Massachusetts 5814 4755 1.1 71.83 3.3 Michigan Michigan 9111 4751 0.9 70.63 11.1 Minnesota Minnesota 3921 4675 0.6 72.96 2.3 Mississippi Mississippi 2341 3098 2.4 68.09 12.5 Missouri Missouri 4767 4254 0.8 70.69 9.3 Montana Montana 746 4347 0.6 70.56 5.0 Nebraska Nebraska 1544 4508 0.6 72.60 2.9 Nevada Nevada 590 5149 0.5 69.03 11.5 New Hampshire New Hampshire 812 4281 0.7 71.23 3.3 New Jersey New Jersey 7333 5237 1.1 70.93 5.2 New Mexico New Mexico 1144 3601 2.2 70.32 9.7 New York New York 18076 4903 1.4 70.55 10.9 North Carolina North Carolina 5441 3875 1.8 69.21 11.1 North Dakota North Dakota 637 5087 0.8 72.78 1.4 Ohio Ohio 10735 4561 0.8 70.82 7.4 Oklahoma Oklahoma 2715 3983 1.1 71.42 6.4 Oregon Oregon 2284 4660 0.6 72.13 4.2 Pennsylvania Pennsylvania 11860 4449 1.0 70.43 6.1 Rhode Island Rhode Island 931 4558 1.3 71.90 2.4 South Carolina South Carolina 2816 3635 2.3 67.96 11.6 South Dakota South Dakota 681 4167 0.5 72.08 1.7 Tennessee Tennessee 4173 3821 1.7 70.11 11.0 Texas Texas 12237 4188 2.2 70.90 12.2 Utah Utah 1203 4022 0.6 72.90 4.5 Vermont Vermont 472 3907 0.6 71.64 5.5 Virginia Virginia 4981 4701 1.4 70.08 9.5 Washington Washington 3559 4864 0.6 71.72 4.3 West Virginia West Virginia 1799 3617 1.4 69.48 6.7 Wisconsin Wisconsin 4589 4468 0.7 72.48 3.0 Wyoming Wyoming 376 4566 0.6 70.29 6.9 HS Grad Frost Area Alabama 41.3 20 50708 Alaska 66.7 152 566432 Arizona 58.1 15 113417 Arkansas 39.9 65 51945 California 62.6 20 156361 Colorado 63.9 166 103766 Connecticut 56.0 139 4862 Delaware 54.6 103 1982 Florida 52.6 11 54090 Georgia 40.6 60 58073 Hawaii 61.9 0 6425 Idaho 59.5 126 82677 Illinois 52.6 127 55748 Indiana 52.9 122 36097 Iowa 59.0 140 55941 Kansas 59.9 114 81787 Kentucky 38.5 95 39650 Louisiana 42.2 12 44930 Maine 54.7 161 30920 Maryland 52.3 101 9891 Massachusetts 58.5 103 7826 Michigan 52.8 125 56817 Minnesota 57.6 160 79289 Mississippi 41.0 50 47296 Missouri 48.8 108 68995 Montana 59.2 155 145587 Nebraska 59.3 139 76483 Nevada 65.2 188 109889 New Hampshire 57.6 174 9027 New Jersey 52.5 115 7521 New Mexico 55.2 120 121412 New York 52.7 82 47831 North Carolina 38.5 80 48798 North Dakota 50.3 186 69273 Ohio 53.2 124 40975 Oklahoma 51.6 82 68782 Oregon 60.0 44 96184 Pennsylvania 50.2 126 44966 Rhode Island 46.4 127 1049 South Carolina 37.8 65 30225 South Dakota 53.3 172 75955 Tennessee 41.8 70 41328 Texas 47.4 35 262134 Utah 67.3 137 82096 Vermont 57.1 168 9267 Virginia 47.8 85 39780 Washington 63.5 32 66570 West Virginia 41.6 100 24070 Wisconsin 54.5 149 54464 Wyoming 62.9 173 97203 > > ## multiple id variables > df3 <- data.frame(school = rep(1:3, each = 4), class = rep(9:10, 6), + time = rep(c(1,1,2,2), 3), score = rnorm(12)) > wide <- reshape(df3, idvar = c("school","class"), direction = "wide") > wide school class score.1 score.2 1 1 9 0.48742905 0.57578135 2 1 10 0.73832471 -0.30538839 5 2 9 1.51178117 -0.62124058 6 2 10 0.38984324 -2.21469989 9 3 9 1.12493092 -0.01619026 10 3 10 -0.04493361 0.94383621 > ## transform back > reshape(wide) school class time score.1 1.9.1 1 9 1 0.48742905 1.10.1 1 10 1 0.73832471 2.9.1 2 9 1 1.51178117 2.10.1 2 10 1 0.38984324 3.9.1 3 9 1 1.12493092 3.10.1 3 10 1 -0.04493361 1.9.2 1 9 2 0.57578135 1.10.2 1 10 2 -0.30538839 2.9.2 2 9 2 -0.62124058 2.10.2 2 10 2 -2.21469989 3.9.2 3 9 2 -0.01619026 3.10.2 3 10 2 0.94383621 > > > > > cleanEx() > nameEx("runmed") > ### * runmed > > flush(stderr()); flush(stdout()) > > ### Name: runmed > ### Title: Running Medians - Robust Scatter Plot Smoothing > ### Aliases: runmed > ### Keywords: smooth robust > > ### ** Examples > > require(graphics) > > utils::example(nhtemp) nhtemp> require(stats); require(graphics) nhtemp> plot(nhtemp, main = "nhtemp data", nhtemp+ ylab = "Mean annual temperature in New Haven, CT (deg. F)") > myNHT <- as.vector(nhtemp) > myNHT[20] <- 2 * nhtemp[20] > plot(myNHT, type="b", ylim = c(48,60), main = "Running Medians Example") > lines(runmed(myNHT, 7), col = "red") > > ## special: multiple y values for one x > plot(cars, main = "'cars' data and runmed(dist, 3)") > lines(cars, col = "light gray", type = "c") > with(cars, lines(speed, runmed(dist, k = 3), col = 2)) > > ## nice quadratic with a few outliers > y <- ys <- (-20:20)^2 > y [c(1,10,21,41)] <- c(150, 30, 400, 450) > all(y == runmed(y, 1)) # 1-neighbourhood <==> interpolation [1] TRUE > plot(y) ## lines(y, lwd=.1, col="light gray") > lines(lowess(seq(y),y, f = .3), col = "brown") > lines(runmed(y, 7), lwd=2, col = "blue") > lines(runmed(y,11), lwd=2, col = "red") > > ## Lowess is not robust > y <- ys ; y[21] <- 6666 ; x <- seq(y) > col <- c("black", "brown","blue") > plot(y, col=col[1]) > lines(lowess(x,y, f = .3), col = col[2]) > lines(runmed(y, 7), lwd=2, col = col[3]) > legend(length(y),max(y), c("data", "lowess(y, f = 0.3)", "runmed(y, 7)"), + xjust = 1, col = col, lty = c(0, 1,1), pch = c(1,NA,NA)) > > > > cleanEx() > nameEx("scatter.smooth") > ### * scatter.smooth > > flush(stderr()); flush(stdout()) > > ### Name: scatter.smooth > ### Title: Scatter Plot with Smooth Curve Fitted by Loess > ### Aliases: scatter.smooth loess.smooth > ### Keywords: smooth > > ### ** Examples > > require(graphics) > > with(cars, scatter.smooth(speed, dist)) > > > > cleanEx() > nameEx("screeplot") > ### * screeplot > > flush(stderr()); flush(stdout()) > > ### Name: screeplot > ### Title: Screeplots > ### Aliases: screeplot screeplot.default > ### Keywords: multivariate > > ### ** Examples > > require(graphics) > > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <- princomp(USArrests, cor = TRUE)) # inappropriate Call: princomp(x = USArrests, cor = TRUE) Standard deviations: Comp.1 Comp.2 Comp.3 Comp.4 1.5748783 0.9948694 0.5971291 0.4164494 4 variables and 50 observations. > screeplot(pc.cr) > > fit <- princomp(covmat=Harman74.cor) > screeplot(fit) > screeplot(fit, npcs=24, type="lines") > > > > cleanEx() > nameEx("sd") > ### * sd > > flush(stderr()); flush(stdout()) > > ### Name: sd > ### Title: Standard Deviation > ### Aliases: sd > ### Keywords: univar > > ### ** Examples > > sd(1:2) ^ 2 [1] 0.5 > > > > cleanEx() > nameEx("se.contrast") > ### * se.contrast > > flush(stderr()); flush(stdout()) > > ### Name: se.contrast > ### Title: Standard Errors for Contrasts in Model Terms > ### Aliases: se.contrast se.contrast.aov se.contrast.aovlist > ### Keywords: models > > ### ** Examples > > ## From Venables and Ripley (2002) p.165. > N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0) > P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0) > K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0) > yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5, + 55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0) > > npk <- data.frame(block = gl(6,4), N = factor(N), P = factor(P), + K = factor(K), yield = yield) > ## Set suitable contrasts. > options(contrasts=c("contr.helmert", "contr.poly")) > npk.aov1 <- aov(yield ~ block + N + K, data=npk) > se.contrast(npk.aov1, list(N == "0", N == "1"), data=npk) [1] 1.609175 > # or via a matrix > cont <- matrix(c(-1,1), 2, 1, dimnames=list(NULL, "N")) > se.contrast(npk.aov1, cont[N, , drop=FALSE]/12, data=npk) N 1.609175 > > ## test a multi-stratum model > npk.aov2 <- aov(yield ~ N + K + Error(block/(N + K)), data=npk) > se.contrast(npk.aov2, list(N == "0", N == "1")) [1] 1.812166 > > > ## an example looking at an interaction contrast > ## Dataset from R.E. Kirk (1995) > ## 'Experimental Design: procedures for the behavioral sciences' > score <- c(12, 8,10, 6, 8, 4,10,12, 8, 6,10,14, 9, 7, 9, 5,11,12, + 7,13, 9, 9, 5,11, 8, 7, 3, 8,12,10,13,14,19, 9,16,14) > A <- gl(2, 18, labels=c("a1", "a2")) > B <- rep(gl(3, 6, labels=c("b1", "b2", "b3")), 2) > fit <- aov(score ~ A*B) > cont <- c(1, -1)[A] * c(1, -1, 0)[B] > sum(cont) # 0 [1] 0 > sum(cont*score) # value of the contrast [1] -18 > se.contrast(fit, as.matrix(cont)) Contrast 1 14.24547 > (t.stat <- sum(cont*score)/se.contrast(fit, as.matrix(cont))) Contrast 1 -1.26356 > summary(fit, split=list(B=1:2), expand.split = TRUE) Df Sum Sq Mean Sq F value Pr(>F) A 1 18.78 18.78 2.221 0.14661 B 2 62.00 31.00 3.666 0.03763 * B: C1 1 1.50 1.50 0.177 0.67662 B: C2 1 60.50 60.50 7.155 0.01199 * A:B 2 81.56 40.78 4.823 0.01527 * A:B: C1 1 13.50 13.50 1.597 0.21612 A:B: C2 1 68.06 68.06 8.049 0.00809 ** Residuals 30 253.67 8.46 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## t.stat^2 is the F value on the A:B: C1 line (with Helmert contrasts) > ## Now look at all three interaction contrasts > cont <- c(1, -1)[A] * cbind(c(1, -1, 0), c(1, 0, -1), c(0, 1, -1))[B,] > se.contrast(fit, cont) # same, due to balance. Contrast 1 Contrast 2 Contrast 3 14.24547 14.24547 14.24547 > rm(A,B,score) > > > ## multi-stratum example where efficiencies play a role > utils::example(eff.aovlist) eff.vl> ## An example from Yates (1932), eff.vl> ## a 2^3 design in 2 blocks replicated 4 times eff.vl> eff.vl> Block <- gl(8, 4) eff.vl> A <- factor(c(0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1, eff.vl+ 0,1,0,1,0,1,0,1,0,1,0,1)) eff.vl> B <- factor(c(0,0,1,1,0,0,1,1,0,1,0,1,1,0,1,0,0,0,1,1, eff.vl+ 0,0,1,1,0,0,1,1,0,0,1,1)) eff.vl> C <- factor(c(0,1,1,0,1,0,0,1,0,0,1,1,0,0,1,1,0,1,0,1, eff.vl+ 1,0,1,0,0,0,1,1,1,1,0,0)) eff.vl> Yield <- c(101, 373, 398, 291, 312, 106, 265, 450, 106, 306, 324, 449, eff.vl+ 272, 89, 407, 338, 87, 324, 279, 471, 323, 128, 423, 334, eff.vl+ 131, 103, 445, 437, 324, 361, 302, 272) eff.vl> aovdat <- data.frame(Block, A, B, C, Yield) eff.vl> old <- getOption("contrasts") eff.vl> options(contrasts=c("contr.helmert", "contr.poly")) eff.vl> (fit <- aov(Yield ~ A*B*C + Error(Block), data = aovdat)) Call: aov(formula = Yield ~ A * B * C + Error(Block), data = aovdat) Grand Mean: 291.5938 Stratum 1: Block Terms: A:B A:C B:C A:B:C Residuals Sum of Squares 780.1250 276.1250 2556.1250 112.5000 774.0938 Deg. of Freedom 1 1 1 1 3 Residual standard error: 16.06335 Estimated effects are balanced Stratum 2: Within Terms: A B C A:B A:C B:C Sum of Squares 3465.28 161170.03 278817.78 28.17 1802.67 11528.17 Deg. of Freedom 1 1 1 1 1 1 A:B:C Residuals Sum of Squares 45.37 5423.28 Deg. of Freedom 1 17 Residual standard error: 17.86103 Estimated effects are balanced eff.vl> eff.aovlist(fit) A B C A:B A:C B:C A:B:C Block 0 0 0 0.25 0.25 0.25 0.25 Within 1 1 1 0.75 0.75 0.75 0.75 eff.vl> options(contrasts = old) > fit <- aov(Yield ~ A + B * C + Error(Block), data = aovdat) > cont1 <- c(-1, 1)[A]/32 # Helmert contrasts > cont2 <- c(-1, 1)[B] * c(-1, 1)[C]/32 > cont <- cbind(A=cont1, BC=cont2) > colSums(cont*Yield) # values of the contrasts A BC 10.40625 -20.90625 > se.contrast(fit, as.matrix(cont)) A BC 3.377196 3.899650 > ## Not run: > ##D # comparison with lme > ##D library(nlme) > ##D fit2 <- lme(Yield ~ A + B*C, random = ~1 | Block, data = aovdat) > ##D summary(fit2)$tTable # same estimates, similar (but smaller) se's. > ## End(Not run) > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("selfStart") > ### * selfStart > > flush(stderr()); flush(stdout()) > > ### Name: selfStart > ### Title: Construct Self-starting Nonlinear Models > ### Aliases: selfStart selfStart.default selfStart.formula > ### Keywords: models > > ### ** Examples > > ## self-starting logistic model > > SSlogis <- selfStart(~ Asym/(1 + exp((xmid - x)/scal)), + function(mCall, data, LHS) + { + xy <- sortedXyData(mCall[["x"]], LHS, data) + if(nrow(xy) < 4) { + stop("Too few distinct x values to fit a logistic") + } + z <- xy[["y"]] + if (min(z) <= 0) { z <- z + 0.05 * max(z) } # avoid zeroes + z <- z/(1.05 * max(z)) # scale to within unit height + xy[["z"]] <- log(z/(1 - z)) # logit transformation + aux <- coef(lm(x ~ z, xy)) + parameters(xy) <- list(xmid = aux[1], scal = aux[2]) + pars <- as.vector(coef(nls(y ~ 1/(1 + exp((xmid - x)/scal)), + data = xy, algorithm = "plinear"))) + value <- c(pars[3], pars[1], pars[2]) + names(value) <- mCall[c("Asym", "xmid", "scal")] + value + }, c("Asym", "xmid", "scal")) > > # 'first.order.log.model' is a function object defining a first order > # compartment model > # 'first.order.log.initial' is a function object which calculates initial > # values for the parameters in 'first.order.log.model' > > # self-starting first order compartment model > ## Not run: > ##D SSfol <- selfStart(first.order.log.model, first.order.log.initial) > ## End(Not run) > > ## Explore the self-starting models already available in R's "stats": > pos.st <- which("package:stats" == search()) > mSS <- apropos("^SS..", where=TRUE, ignore.case=FALSE) > (mSS <- unname(mSS[names(mSS) == pos.st])) [1] "SSasymp" "SSasympOff" "SSasympOrig" "SSbiexp" "SSfol" [6] "SSfpl" "SSgompertz" "SSlogis" "SSmicmen" "SSweibull" > fSS <- sapply(mSS, get, pos = pos.st, mode = "function") > all(sapply(fSS, inherits, "selfStart"))# -> TRUE [1] TRUE > > ## Show the argument list of each self-starting function: > str(fSS, give.attr=FALSE) List of 10 $ SSasymp :function (input, Asym, R0, lrc) $ SSasympOff :function (input, Asym, lrc, c0) $ SSasympOrig:function (input, Asym, lrc) $ SSbiexp :function (input, A1, lrc1, A2, lrc2) $ SSfol :function (Dose, input, lKe, lKa, lCl) $ SSfpl :function (input, A, B, xmid, scal) $ SSgompertz :function (x, Asym, b2, b3) $ SSlogis :function (input, Asym, xmid, scal) $ SSmicmen :function (input, Vm, K) $ SSweibull :function (x, Asym, Drop, lrc, pwr) > > > > cleanEx() > nameEx("setNames") > ### * setNames > > flush(stderr()); flush(stdout()) > > ### Name: setNames > ### Title: Set the Names in an Object > ### Aliases: setNames > ### Keywords: list > > ### ** Examples > > setNames( 1:3, c("foo", "bar", "baz") ) foo bar baz 1 2 3 > # this is just a short form of > tmp <- 1:3 > names(tmp) <- c("foo", "bar", "baz") > tmp foo bar baz 1 2 3 > > > > cleanEx() > nameEx("shapiro.test") > ### * shapiro.test > > flush(stderr()); flush(stdout()) > > ### Name: shapiro.test > ### Title: Shapiro-Wilk Normality Test > ### Aliases: shapiro.test > ### Keywords: htest > > ### ** Examples > > shapiro.test(rnorm(100, mean = 5, sd = 3)) Shapiro-Wilk normality test data: rnorm(100, mean = 5, sd = 3) W = 0.9956, p-value = 0.9876 > shapiro.test(runif(100, min = 2, max = 4)) Shapiro-Wilk normality test data: runif(100, min = 2, max = 4) W = 0.9309, p-value = 5.616e-05 > > > > cleanEx() > nameEx("simulate") > ### * simulate > > flush(stderr()); flush(stdout()) > > ### Name: simulate > ### Title: Simulate Responses > ### Aliases: simulate > ### Keywords: models datagen > > ### ** Examples > > x <- 1:5 > mod1 <- lm(c(1:3, 7, 6) ~ x) > S1 <- simulate(mod1, nsim = 4) > ## repeat the simulation: > .Random.seed <- attr(S1, "seed") > identical(S1, simulate(mod1, nsim = 4)) [1] TRUE > > S2 <- simulate(mod1, nsim = 200, seed = 101) > rowMeans(S2) # should be about the same as 1 2 3 4 5 0.6885691 2.2329771 3.7790352 5.2775859 6.8131451 > fitted(mod1) 1 2 3 4 5 0.8 2.3 3.8 5.3 6.8 > > ## repeat identically: > (sseed <- attr(S2, "seed")) # seed; RNGkind as attribute [1] 101 attr(,"kind") attr(,"kind")[[1]] [1] "Mersenne-Twister" attr(,"kind")[[2]] [1] "Inversion" > stopifnot(identical(S2, simulate(mod1, nsim = 200, seed = sseed))) > > ## To be sure about the proper RNGkind, e.g., after > RNGversion("2.7.0") > ## first set the RNG kind, then simulate > do.call(RNGkind, attr(sseed, "kind")) > identical(S2, simulate(mod1, nsim = 200, seed = sseed)) [1] TRUE > > ## Binomial GLM examples > yb1 <- matrix(c(4, 4, 5, 7, 8, 6, 6, 5, 3, 2), ncol = 2) > modb1 <- glm(yb1 ~ x, family = binomial) > S3 <- simulate(modb1, nsim = 4) > # each column of S3 is a two-column matrix. > > x2 <- sort(runif(100)) > yb2 <- rbinom(100, prob = plogis(2*(x2-1)), size = 1) > yb2 <- factor(1 + yb2, labels = c("failure", "success")) > modb2 <- glm(yb2 ~ x2, family = binomial) > S4 <- simulate(modb2, nsim = 4) > # each column of S4 is a factor > > > > cleanEx() > nameEx("smooth") > ### * smooth > > flush(stderr()); flush(stdout()) > > ### Name: smooth > ### Title: Tukey's (Running Median) Smoothing > ### Aliases: smooth > ### Keywords: robust smooth > > ### ** Examples > > require(graphics) > > ## see also demo(smooth) ! > > x1 <- c(4, 1, 3, 6, 6, 4, 1, 6, 2, 4, 2) # very artificial > (x3R <- smooth(x1, "3R")) # 2 iterations of "3" 3R Tukey smoother resulting from smooth(x = x1, kind = "3R") used 2 iterations [1] 3 3 3 6 6 4 4 4 2 2 2 > smooth(x3R, kind = "S") S Tukey smoother resulting from smooth(x = x3R, kind = "S") changed [1] 3 3 3 3 4 4 4 4 2 2 2 > > sm.3RS <- function(x, ...) + smooth(smooth(x, "3R", ...), "S", ...) > > y <- c(1,1, 19:1) > plot(y, main = "misbehaviour of \"3RSR\"", col.main = 3) > lines(sm.3RS(y)) > lines(smooth(y)) > lines(smooth(y, "3RSR"), col = 3, lwd = 2)# the horror > > x <- c(8:10,10, 0,0, 9,9) > plot(x, main = "breakdown of 3R and S and hence 3RSS") > matlines(cbind(smooth(x,"3R"),smooth(x,"S"), smooth(x,"3RSS"),smooth(x))) > > presidents[is.na(presidents)] <- 0 # silly > summary(sm3 <- smooth(presidents, "3R")) 3R Tukey smoother resulting from smooth(x = presidents, kind = "3R") ; n = 120 used 4 iterations Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0 44.0 57.0 54.2 71.0 82.0 > summary(sm2 <- smooth(presidents,"3RSS")) 3RSS Tukey smoother resulting from smooth(x = presidents, kind = "3RSS") ; n = 120 used 5 iterations Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 44.00 57.00 55.45 69.00 82.00 > summary(sm <- smooth(presidents)) 3RS3R Tukey smoother resulting from smooth(x = presidents) ; n = 120 used 7 iterations Min. 1st Qu. Median Mean 3rd Qu. Max. 24.00 44.00 57.00 55.88 69.00 82.00 > > all.equal(c(sm2),c(smooth(smooth(sm3, "S"), "S"))) # 3RSS === 3R S S [1] TRUE > all.equal(c(sm), c(smooth(smooth(sm3, "S"), "3R")))# 3RS3R === 3R S 3R [1] TRUE > > plot(presidents, main = "smooth(presidents0, *) : 3R and default 3RS3R") > lines(sm3,col = 3, lwd = 1.5) > lines(sm, col = 2, lwd = 1.25) > > > > cleanEx() > nameEx("smooth.spline") > ### * smooth.spline > > flush(stderr()); flush(stdout()) > > ### Name: smooth.spline > ### Title: Fit a Smoothing Spline > ### Aliases: smooth.spline > ### Keywords: smooth > > ### ** Examples > > require(graphics) > > attach(cars) > plot(speed, dist, main = "data(cars) & smoothing splines") > cars.spl <- smooth.spline(speed, dist) > (cars.spl) Call: smooth.spline(x = speed, y = dist) Smoothing Parameter spar= 0.7801305 lambda= 0.1112206 (11 iterations) Equivalent Degrees of Freedom (Df): 2.635278 Penalized Criterion: 4187.776 GCV: 244.1044 > ## This example has duplicate points, so avoid cv=TRUE > ## Don't show: > stopifnot(cars.spl $ w == table(speed)) # weights = multiplicities > utils::str(cars.spl, digits=5, vec.len=6) List of 15 $ x : num [1:19] 4 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... $ y : num [1:19] 1.6578 11.6829 15.0644 18.4823 21.9471 25.4656 29.0505 32.7076 ... $ w : num [1:19] 2 2 1 1 3 2 4 4 4 3 2 3 4 3 5 ... $ yin : num [1:19] 6 13 16 10 26 22.5 21.5 35 ... $ data :List of 3 ..$ x: num [1:50] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 ... ..$ y: num [1:50] 2 10 4 22 16 10 18 26 34 17 28 14 20 24 28 ... ..$ w: num [1:50] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... $ lev : num [1:19] 0.399342 0.179105 0.069771 0.055561 0.136721 0.077539 0.137252 0.126354 ... $ cv.crit : num 244.1 $ pen.crit: num 4187.8 $ crit : num 244.1 $ df : num 2.6353 $ spar : num 0.78013 $ lambda : num 0.11122 $ iparms : Named int [1:3] 1 0 11 ..- attr(*, "names")= chr [1:3] "icrit" "ispar" "iter" $ fit :List of 5 ..$ knot : num [1:25] 0 0 0 0 0.14286 0.19048 0.2381 0.28571 ... ..$ nk : int 21 ..$ min : num 4 ..$ range: num 21 ..$ coef : num [1:21] 1.6578 4.9869 9.4256 15.0584 18.4743 21.9384 25.4544 29.0377 ... ..- attr(*, "class")= chr "smooth.spline.fit" $ call : language smooth.spline(x = speed, y = dist) - attr(*, "class")= chr "smooth.spline" > cars.spl$fit $knot [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.1428571 0.1904762 0.2380952 [8] 0.2857143 0.3333333 0.3809524 0.4285714 0.4761905 0.5238095 0.5714286 [15] 0.6190476 0.6666667 0.7142857 0.7619048 0.8571429 0.9047619 0.9523810 [22] 1.0000000 1.0000000 1.0000000 1.0000000 $nk [1] 21 $min [1] 4 $range [1] 21 $coef [1] 1.657809 4.986889 9.425578 15.058364 18.474307 21.938439 25.454405 [8] 29.037679 32.697985 36.415692 40.180057 44.035086 48.004725 52.097325 [15] 56.299736 62.096055 68.215320 74.524063 79.313459 82.506943 84.103688 attr(,"class") [1] "smooth.spline.fit" > ## End Don't show > lines(cars.spl, col = "blue") > lines(smooth.spline(speed, dist, df=10), lty=2, col = "red") > legend(5,120,c(paste("default [C.V.] => df =",round(cars.spl$df,1)), + "s( * , df = 10)"), col = c("blue","red"), lty = 1:2, + bg='bisque') > detach() > > > ## Residual (Tukey Anscombe) plot: > plot(residuals(cars.spl) ~ fitted(cars.spl)) > abline(h = 0, col="gray") > > ## consistency check: > stopifnot(all.equal(cars$dist, + fitted(cars.spl) + residuals(cars.spl))) > > ##-- artificial example > y18 <- c(1:3,5,4,7:3,2*(2:5),rep(10,4)) > xx <- seq(1,length(y18), len=201) > (s2 <- smooth.spline(y18)) # GCV Call: smooth.spline(x = y18) Smoothing Parameter spar= 0.3928105 lambda= 9.672776e-05 (13 iterations) Equivalent Degrees of Freedom (Df): 8.494168 Penalized Criterion: 3.59204 GCV: 0.7155391 > (s02 <- smooth.spline(y18, spar = 0.2)) Call: smooth.spline(x = y18, spar = 0.2) Smoothing Parameter spar= 0.2 lambda= 3.911187e-06 Equivalent Degrees of Freedom (Df): 15.259 Penalized Criterion: 0.4973656 GCV: 1.191602 > (s02. <- smooth.spline(y18, spar = 0.2, cv=NA)) Call: smooth.spline(x = y18, spar = 0.2, cv = NA) Smoothing Parameter spar= 0.2 lambda= 3.911187e-06 Equivalent Degrees of Freedom (Df): NA Penalized Criterion: 0.4973656 > plot(y18, main=deparse(s2$call), col.main=2) > lines(s2, col = "gray"); lines(predict(s2, xx), col = 2) > lines(predict(s02, xx), col = 3); mtext(deparse(s02$call), col = 3) > > ## Don't show: > stopifnot(all.equal(predict(s02, xx), + predict(s02., xx), tol= 1e-15)) > ## End Don't show > > > cleanEx() > nameEx("smoothEnds") > ### * smoothEnds > > flush(stderr()); flush(stdout()) > > ### Name: smoothEnds > ### Title: End Points Smoothing (for Running Medians) > ### Aliases: smoothEnds > ### Keywords: smooth robust > > ### ** Examples > > require(graphics) > > y <- ys <- (-20:20)^2 > y [c(1,10,21,41)] <- c(100, 30, 400, 470) > s7k <- runmed(y,7, endrule = "keep") > s7. <- runmed(y,7, endrule = "const") > s7m <- runmed(y,7) > col3 <- c("midnightblue","blue","steelblue") > plot(y, main = "Running Medians -- runmed(*, k=7, end.rule = X)") > lines(ys, col = "light gray") > matlines(cbind(s7k,s7.,s7m), lwd= 1.5, lty = 1, col = col3) > legend(1,470, paste("endrule",c("keep","constant","median"),sep=" = "), + col = col3, lwd = 1.5, lty = 1) > > stopifnot(identical(s7m, smoothEnds(s7k, 7))) > > > > cleanEx() > nameEx("sortedXyData") > ### * sortedXyData > > flush(stderr()); flush(stdout()) > > ### Name: sortedXyData > ### Title: Create a 'sortedXyData' Object > ### Aliases: sortedXyData sortedXyData.default > ### Keywords: manip > > ### ** Examples > > DNase.2 <- DNase[ DNase$Run == "2", ] > sortedXyData( expression(log(conc)), expression(density), DNase.2 ) x y 1 -3.0194489 0.0475 2 -1.6331544 0.1300 3 -0.9400073 0.2160 4 -0.2468601 0.3920 5 0.4462871 0.6765 6 1.1394343 1.0970 7 1.8325815 1.5400 8 2.5257286 1.9230 > > > > cleanEx() > nameEx("spec.ar") > ### * spec.ar > > flush(stderr()); flush(stdout()) > > ### Name: spec.ar > ### Title: Estimate Spectral Density of a Time Series from AR Fit > ### Aliases: spec.ar > ### Keywords: ts > > ### ** Examples > > require(graphics) > > spec.ar(lh) > > spec.ar(ldeaths) > spec.ar(ldeaths, method="burg") > > spec.ar(log(lynx)) > spec.ar(log(lynx), method="burg", add=TRUE, col="purple") > spec.ar(log(lynx), method="mle", add=TRUE, col="forest green") > spec.ar(log(lynx), method="ols", add=TRUE, col="blue") > > > > cleanEx() > nameEx("spec.pgram") > ### * spec.pgram > > flush(stderr()); flush(stdout()) > > ### Name: spec.pgram > ### Title: Estimate Spectral Density of a Time Series by a Smoothed > ### Periodogram > ### Aliases: spec.pgram > ### Keywords: ts > > ### ** Examples > > require(graphics) > > ## Examples from Venables & Ripley > spectrum(ldeaths) > spectrum(ldeaths, spans = c(3,5)) > spectrum(ldeaths, spans = c(5,7)) > spectrum(mdeaths, spans = c(3,3)) > spectrum(fdeaths, spans = c(3,3)) > > ## bivariate example > mfdeaths.spc <- spec.pgram(ts.union(mdeaths, fdeaths), spans = c(3,3)) > # plots marginal spectra: now plot coherency and phase > plot(mfdeaths.spc, plot.type = "coherency") > plot(mfdeaths.spc, plot.type = "phase") > > ## now impose a lack of alignment > mfdeaths.spc <- spec.pgram(ts.intersect(mdeaths, lag(fdeaths, 4)), + spans = c(3,3), plot = FALSE) > plot(mfdeaths.spc, plot.type = "coherency") > plot(mfdeaths.spc, plot.type = "phase") > > stocks.spc <- spectrum(EuStockMarkets, kernel("daniell", c(30,50)), + plot = FALSE) > plot(stocks.spc, plot.type = "marginal") # the default type > plot(stocks.spc, plot.type = "coherency") > plot(stocks.spc, plot.type = "phase") > > sales.spc <- spectrum(ts.union(BJsales, BJsales.lead), + kernel("modified.daniell", c(5,7))) > plot(sales.spc, plot.type = "coherency") > plot(sales.spc, plot.type = "phase") > > > > cleanEx() > nameEx("spectrum") > ### * spectrum > > flush(stderr()); flush(stdout()) > > ### Name: spectrum > ### Title: Spectral Density Estimation > ### Aliases: spectrum spec > ### Keywords: ts > > ### ** Examples > > require(graphics) > > ## Examples from Venables & Ripley > ## spec.pgram > par(mfrow=c(2,2)) > spectrum(lh) > spectrum(lh, spans=3) > spectrum(lh, spans=c(3,3)) > spectrum(lh, spans=c(3,5)) > > spectrum(ldeaths) > spectrum(ldeaths, spans=c(3,3)) > spectrum(ldeaths, spans=c(3,5)) > spectrum(ldeaths, spans=c(5,7)) > spectrum(ldeaths, spans=c(5,7), log="dB", ci=0.8) > > # for multivariate examples see the help for spec.pgram > > ## spec.ar > spectrum(lh, method="ar") > spectrum(ldeaths, method="ar") > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("splinefun") > ### * splinefun > > flush(stderr()); flush(stdout()) > > ### Name: splinefun > ### Title: Interpolating Splines > ### Aliases: spline splinefun splinefunH > ### Keywords: math dplot > > ### ** Examples > > require(graphics) > > op <- par(mfrow = c(2,1), mgp = c(2,.8,0), mar = .1+c(3,3,3,1)) > n <- 9 > x <- 1:n > y <- rnorm(n) > plot(x, y, main = paste("spline[fun](.) through", n, "points")) > lines(spline(x, y)) > lines(spline(x, y, n = 201), col = 2) > > y <- (x-6)^2 > plot(x, y, main = "spline(.) -- 3 methods") > lines(spline(x, y, n = 201), col = 2) > lines(spline(x, y, n = 201, method = "natural"), col = 3) > lines(spline(x, y, n = 201, method = "periodic"), col = 4) Warning in spline(x, y, n = 201, method = "periodic") : spline: first and last y values differ - using y[1] for both > legend(6,25, c("fmm","natural","periodic"), col=2:4, lty=1) > > y <- sin((x-0.5)*pi) > f <- splinefun(x, y) > ls(envir = environment(f)) [1] "z" > splinecoef <- get("z", envir = environment(f)) > curve(f(x), 1, 10, col = "green", lwd = 1.5) > points(splinecoef, col = "purple", cex = 2) > curve(f(x, deriv=1), 1, 10, col = 2, lwd = 1.5) > curve(f(x, deriv=2), 1, 10, col = 2, lwd = 1.5, n = 401) > curve(f(x, deriv=3), 1, 10, col = 2, lwd = 1.5, n = 401) > par(op) > > ## Manual spline evaluation --- demo the coefficients : > .x <- splinecoef$x > u <- seq(3,6, by = 0.25) > (ii <- findInterval(u, .x)) [1] 3 3 3 3 4 4 4 4 5 5 5 5 6 > dx <- u - .x[ii] > f.u <- with(splinecoef, + y[ii] + dx*(b[ii] + dx*(c[ii] + dx* d[ii]))) > stopifnot(all.equal(f(u), f.u)) > > ## An example with ties (non-unique x values): > set.seed(1); x <- round(rnorm(30), 1); y <- sin(pi * x) + rnorm(30)/10 > plot(x,y, main="spline(x,y) when x has ties") > lines(spline(x,y, n= 201), col = 2) > ## visualizes the non-unique ones: > tx <- table(x); mx <- as.numeric(names(tx[tx > 1])) > ry <- matrix(unlist(tapply(y, match(x,mx), range, simplify=FALSE)), + ncol=2, byrow=TRUE) > segments(mx, ry[,1], mx, ry[,2], col = "blue", lwd = 2) > > ## An example of monotone interpolation > n <- 20 > set.seed(11) > x. <- sort(runif(n)) ; y. <- cumsum(abs(rnorm(n))) > plot(x.,y.) > curve(splinefun(x.,y.)(x), add = TRUE, col = 2, n = 1001) > curve(splinefun(x.,y., method = "monoH.FC")(x), add = TRUE, col = 3, n = 1001) > curve(splinefun(x.,y., method = "hyman")(x), add = TRUE, col = 4, n = 1001) > legend("topleft", + paste0("splinefun( \"", c("fmm", "monoH.FC", "hyman"), "\" )"), + col = 2:4, lty = 1) > > ## and one from Fritsch and Carlson (1980), Dougherty et al (1989) > x. <- c(7.09, 8.09, 8.19, 8.7, 9.2, 10, 12, 15, 20) > f <- c(0, 2.76429e-5, 4.37498e-2, 0.169183, 0.469428, 0.943740, + 0.998636, 0.999919, 0.999994) > plot(x., f, ylim = c(-0.2, 1.2)) > curve(splinefun(x., f)(x), add = TRUE, col = 2, n = 1001) > curve(splinefun(x., f, method = "hyman")(x), add = TRUE, col = 4, n = 1001) > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("stat.anova") > ### * stat.anova > > flush(stderr()); flush(stdout()) > > ### Name: stat.anova > ### Title: GLM Anova Statistics > ### Aliases: stat.anova > ### Keywords: regression models > > ### ** Examples > > ##-- Continued from '?glm': > ## Don't show: > utils::example("glm", echo = FALSE) treatment outcome counts 1 1 1 18 2 1 2 17 3 1 3 15 4 2 1 20 5 2 2 10 6 2 3 20 7 3 1 25 8 3 2 13 9 3 3 12 > ## End Don't show > print(ag <- anova(glm.D93)) Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev NULL 8 10.5814 outcome 2 5.4523 6 5.1291 treatment 2 0.0000 4 5.1291 > stat.anova(ag$table, test = "Cp", + scale = sum(resid(glm.D93, "pearson")^2)/4, + df.scale = 4, n = 9) table Cp > > > > cleanEx() > nameEx("step") > ### * step > > flush(stderr()); flush(stdout()) > > ### Name: step > ### Title: Choose a model by AIC in a Stepwise Algorithm > ### Aliases: step > ### Keywords: models > > ### ** Examples > > > cleanEx() > nameEx("stepfun") > ### * stepfun > > flush(stderr()); flush(stdout()) > > ### Name: stepfun > ### Title: Step Function Class > ### Aliases: stepfun is.stepfun as.stepfun print.stepfun summary.stepfun > ### knots > ### Keywords: dplot > > ### ** Examples > > y0 <- c(1.,2.,4.,3.) > sfun0 <- stepfun(1:3, y0, f = 0) > sfun.2 <- stepfun(1:3, y0, f = .2) > sfun1 <- stepfun(1:3, y0, f = 1) > sfun1c <- stepfun(1:3, y0, right=TRUE)# hence f=1 > sfun0 Step function Call: stepfun(1:3, y0, f = 0) x[1:3] = 1, 2, 3 4 plateau levels = 1, 2, 4, 3 > summary(sfun0) Step function with continuity 'f'= 0 , 3 knots at [1] 1 2 3 and 4 plateau levels (y) at [1] 1 2 4 3 > summary(sfun.2) Step function with continuity 'f'= 0.2 , 3 knots at [1] 1 2 3 and 4 plateau levels (y) at [1] 1 2 4 3 > > ## look at the internal structure: > unclass(sfun0) function (v) .C(C_R_approxfun, as.double(x), as.double(y), n, xout = as.double(v), as.integer(length(v)), as.integer(method), as.double(yleft), as.double(yright), as.double(f), NAOK = TRUE)$xout attr(,"call") stepfun(1:3, y0, f = 0) > ls(envir = environment(sfun0)) [1] "f" "method" "n" "x" "y" "yleft" "yright" > > x0 <- seq(0.5,3.5, by = 0.25) > rbind(x=x0, f.f0 = sfun0(x0), f.f02= sfun.2(x0), + f.f1 = sfun1(x0), f.f1c = sfun1c(x0)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] x 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.5 f.f0 1.0 1.00 2 2.00 2.0 2.00 4 4.00 4.0 4.00 3 3.00 3.0 f.f02 1.0 1.00 2 2.40 2.4 2.40 4 3.80 3.8 3.80 3 3.00 3.0 f.f1 1.0 1.00 2 4.00 4.0 4.00 4 3.00 3.0 3.00 3 3.00 3.0 f.f1c 1.0 1.00 1 2.00 2.0 2.00 2 4.00 4.0 4.00 4 3.00 3.0 > ## Identities : > stopifnot(identical(y0[-1], sfun0 (1:3)),# right = FALSE + identical(y0[-4], sfun1c(1:3)))# right = TRUE > > > > cleanEx() > nameEx("stl") > ### * stl > > flush(stderr()); flush(stdout()) > > ### Name: stl > ### Title: Seasonal Decomposition of Time Series by Loess > ### Aliases: stl > ### Keywords: ts > > ### ** Examples > > require(graphics) > > plot(stl(nottem, "per")) > plot(stl(nottem, s.window = 4, t.window = 50, t.jump = 1)) > > plot(stllc <- stl(log(co2), s.window=21)) > summary(stllc) Call: stl(x = log(co2), s.window = 21) Time.series components: seasonal trend remainder Min. :-0.009939103 Min. :5.753541 Min. :-0.0022554051 1st Qu.:-0.004536535 1st Qu.:5.778556 1st Qu.:-0.0004586796 Median : 0.000877761 Median :5.815125 Median :-0.0000088674 Mean :-0.000001304 Mean :5.819267 Mean :-0.0000019655 3rd Qu.: 0.004997747 3rd Qu.:5.859806 3rd Qu.: 0.0004023465 Max. : 0.009114691 Max. :5.898750 Max. : 0.0019396260 IQR: STL.seasonal STL.trend STL.remainder data 0.009534 0.081250 0.000861 0.079370 % 12.0 102.4 1.1 100.0 Weights: all == 1 Other components: List of 5 $ win : Named num [1:3] 21 21 13 $ deg : Named int [1:3] 0 1 1 $ jump : Named num [1:3] 3 3 2 $ inner: int 2 $ outer: int 0 > ## linear trend, strict period. > plot(stl(log(co2), s.window="per", t.window=1000)) > > ## Two STL plotted side by side : > stmd <- stl(mdeaths, s.window = "per") # non-robust > summary(stmR <- stl(mdeaths, s.window = "per", robust = TRUE)) Call: stl(x = mdeaths, s.window = "per", robust = TRUE) Time.series components: seasonal trend remainder Min. :-446.8302 Min. :1318.650 Min. :-314.5835 1st Qu.:-301.5726 1st Qu.:1432.208 1st Qu.: -32.5392 Median : -79.0561 Median :1448.891 Median : 5.7943 Mean : 0.0000 Mean :1472.880 Mean : 23.0646 3rd Qu.: 304.5673 3rd Qu.:1548.974 3rd Qu.: 47.5134 Max. : 544.7904 Max. :1615.535 Max. : 872.1992 IQR: STL.seasonal STL.trend STL.remainder data 606.14 116.77 80.05 707.75 % 85.6 16.5 11.3 100.0 Weights: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000000 0.5594126 0.9451991 0.7312186 0.9853375 1.0000000 Other components: List of 5 $ win : Named num [1:3] 721 19 13 $ deg : Named int [1:3] 0 1 1 $ jump : Named num [1:3] 73 2 2 $ inner: int 1 $ outer: int 15 > op <- par(mar = c(0, 4, 0, 3), oma = c(5, 0, 4, 0), mfcol = c(4, 2)) > plot(stmd, set.pars=NULL, labels = NULL, + main = "stl(mdeaths, s.w = \"per\", robust = FALSE / TRUE )") > plot(stmR, set.pars=NULL) > # mark the 'outliers' : > (iO <- which(stmR $ weights < 1e-8)) # 10 were considered outliers [1] 24 26 27 28 36 37 50 52 59 61 > sts <- stmR$time.series > points(time(sts)[iO], 0.8* sts[,"remainder"][iO], pch = 4, col = "red") > par(op)# reset > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("summary.aov") > ### * summary.aov > > flush(stderr()); flush(stdout()) > > ### Name: summary.aov > ### Title: Summarize an Analysis of Variance Model > ### Aliases: summary.aov summary.aovlist print.summary.aov > ### print.summary.aovlist > ### Keywords: models regression > > ### ** Examples > > ## For a simple example see example(aov) > > # Cochran and Cox (1957, p.164) > # 3x3 factorial with ordered factors, each is average of 12. > CC <- data.frame( + y = c(449, 413, 326, 409, 358, 291, 341, 278, 312)/12, + P = ordered(gl(3, 3)), N = ordered(gl(3, 1, 9)) + ) > CC.aov <- aov(y ~ N * P, data = CC , weights = rep(12, 9)) > summary(CC.aov) Df Sum Sq Mean Sq N 2 1016.7 508.3 P 2 917.4 458.7 N:P 4 399.3 99.8 > > # Split both main effects into linear and quadratic parts. > summary(CC.aov, split = list(N = list(L = 1, Q = 2), + P = list(L = 1, Q = 2))) Df Sum Sq Mean Sq N 2 1016.7 508.3 N: L 1 1012.5 1012.5 N: Q 1 4.2 4.2 P 2 917.4 458.7 P: L 1 917.3 917.3 P: Q 1 0.0 0.0 N:P 4 399.3 99.8 N:P: L.L 1 184.1 184.1 N:P: Q.L 1 152.1 152.1 N:P: L.Q 1 49.0 49.0 N:P: Q.Q 1 14.1 14.1 > > # Split only the interaction > summary(CC.aov, split = list("N:P" = list(L.L = 1, Q = 2:4))) Df Sum Sq Mean Sq N 2 1016.7 508.3 P 2 917.4 458.7 N:P 4 399.3 99.8 N:P: L.L 1 184.1 184.1 N:P: Q 3 215.2 71.7 > > # split on just one var > summary(CC.aov, split = list(P = list(lin = 1, quad = 2))) Df Sum Sq Mean Sq N 2 1016.7 508.3 P 2 917.4 458.7 P: lin 1 917.3 917.3 P: quad 1 0.0 0.0 N:P 4 399.3 99.8 N:P: lin 2 336.2 168.1 N:P: quad 2 63.1 31.5 > summary(CC.aov, split = list(P = list(lin = 1, quad = 2)), + expand.split=FALSE) Df Sum Sq Mean Sq N 2 1016.7 508.3 P 2 917.4 458.7 P: lin 1 917.3 917.3 P: quad 1 0.0 0.0 N:P 4 399.3 99.8 > > > cleanEx() > nameEx("summary.glm") > ### * summary.glm > > flush(stderr()); flush(stdout()) > > ### Name: summary.glm > ### Title: Summarizing Generalized Linear Model Fits > ### Aliases: summary.glm print.summary.glm > ### Keywords: models regression > > ### ** Examples > > ## For examples see example(glm) > > > > cleanEx() > nameEx("summary.lm") > ### * summary.lm > > flush(stderr()); flush(stdout()) > > ### Name: summary.lm > ### Title: Summarizing Linear Model Fits > ### Aliases: summary.lm summary.mlm print.summary.lm > ### Keywords: regression models > > ### ** Examples > > ## Don't show: > utils::example("lm", echo = FALSE) > ## End Don't show > ##-- Continuing the lm(.) example: > coef(lm.D90)# the bare coefficients groupCtl groupTrt 5.032 4.661 > sld90 <- summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept > sld90 Call: lm(formula = weight ~ group - 1) Residuals: Min 1Q Median 3Q Max -1.0710 -0.4938 0.0685 0.2462 1.3690 Coefficients: Estimate Std. Error t value Pr(>|t|) groupCtl 5.0320 0.2202 22.85 9.55e-15 *** groupTrt 4.6610 0.2202 21.16 3.62e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6964 on 18 degrees of freedom Multiple R-squared: 0.9818, Adjusted R-squared: 0.9798 F-statistic: 485.1 on 2 and 18 DF, p-value: < 2.2e-16 > coef(sld90)# much more Estimate Std. Error t value Pr(>|t|) groupCtl 5.032 0.2202177 22.85012 9.547128e-15 groupTrt 4.661 0.2202177 21.16542 3.615345e-14 > > > > cleanEx() > nameEx("summary.manova") > ### * summary.manova > > flush(stderr()); flush(stdout()) > > ### Name: summary.manova > ### Title: Summary Method for Multivariate Analysis of Variance > ### Aliases: summary.manova print.summary.manova > ### Keywords: models > > ### ** Examples > > > cleanEx() > nameEx("summary.princomp") > ### * summary.princomp > > flush(stderr()); flush(stdout()) > > ### Name: summary.princomp > ### Title: Summary method for Principal Components Analysis > ### Aliases: summary.princomp print.summary.princomp > ### Keywords: multivariate > > ### ** Examples > > summary(pc.cr <- princomp(USArrests, cor=TRUE)) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604 0.2474413 0.0891408 0.04335752 Cumulative Proportion 0.6200604 0.8675017 0.9566425 1.00000000 > print(summary(princomp(USArrests, cor=TRUE), + loadings = TRUE, cutoff = 0.2), digits = 2) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604 0.2474413 0.0891408 0.04335752 Cumulative Proportion 0.6200604 0.8675017 0.9566425 1.00000000 Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Murder -0.54 0.42 -0.34 0.65 Assault -0.58 -0.27 -0.74 UrbanPop -0.28 -0.87 -0.38 Rape -0.54 0.82 > > > > cleanEx() > nameEx("supsmu") > ### * supsmu > > flush(stderr()); flush(stdout()) > > ### Name: supsmu > ### Title: Friedman's SuperSmoother > ### Aliases: supsmu > ### Keywords: smooth > > ### ** Examples > > require(graphics) > > with(cars, { + plot(speed, dist) + lines(supsmu(speed, dist)) + lines(supsmu(speed, dist, bass = 7), lty = 2) + }) > > > > cleanEx() > nameEx("symnum") > ### * symnum > > flush(stderr()); flush(stdout()) > > ### Name: symnum > ### Title: Symbolic Number Coding > ### Aliases: symnum > ### Keywords: utilities character > > ### ** Examples > > ii <- 0:8; names(ii) <- ii > symnum(ii, cut= 2*(0:4), sym = c(".", "-", "+", "$")) 0 1 2 3 4 5 6 7 8 . . . - - + + $ $ attr(,"legend") [1] 0 ‘.’ 2 ‘-’ 4 ‘+’ 6 ‘$’ 8 > symnum(ii, cut= 2*(0:4), sym = c(".", "-", "+", "$"), show.max=TRUE) 0 1 2 3 4 5 6 7 8 . . . - - + + $ 8 attr(,"legend") [1] 0 ‘.’ 2 ‘-’ 4 ‘+’ 6 ‘$’ 8 > > symnum(1:12 %% 3 == 0)# --> "|" = TRUE, "." = FALSE for logical [1] . . | . . | . . | . . | > > ## Pascal's Triangle modulo 2 -- odd and even numbers: > N <- 38 > pascal <- t(sapply(0:N, function(n) round(choose(n, 0:N - (N-n)%/%2)))) > rownames(pascal) <- rep("", 1+N) # <-- to improve "graphic" > symnum(pascal %% 2, symbols = c(" ", "A"), numeric = FALSE) A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A > > ##-- Symbolic correlation matrices: > symnum(cor(attitude), diag = FALSE) rt cm p l rs cr a rating complaints + privileges . . learning , . . raises . , . , critical . advance . . . attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cor(attitude), abbr.= NULL) rating 1 complaints + 1 privileges . . 1 learning , . . 1 raises . , . , 1 critical . 1 advance . . . 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cor(attitude), abbr.= FALSE) rating complaints privileges learning raises critical advance rating 1 complaints + 1 privileges . . 1 learning , . . 1 raises . , . , 1 critical . 1 advance . . . 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cor(attitude), abbr.= 2) rt cm pr lr rs cr ad rating 1 complaints + 1 privileges . . 1 learning , . . 1 raises . , . , 1 critical . 1 advance . . . 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > > symnum(cor(rbind(1, rnorm(25), rnorm(25)^2))) [1,] 1 [2,] + 1 [3,] . 1 [4,] . B 1 [5,] + B 1 [6,] . B B 1 [7,] , * . . * 1 [8,] . + . , + . B 1 [9,] . + . , * . B B 1 [10,] . B B B . . . 1 [11,] B B . * . , , B 1 [12,] , B . B B + * . 1 [13,] 1 + . . + . , . . . , 1 [14,] , . * + B * + , 1 [15,] . * B . + , + , * B . , 1 [16,] * B B + , , * * . 1 [17,] * B B + , , * * . B 1 [18,] , , + , , * B * , + , . * . . 1 [19,] . + , , + . B B B , + + . . + , , B 1 [20,] + B B B + * . B + . B B , + 1 [21,] , , + , , * B B , + + . * . . B B , 1 [22,] . , , , + . B B B , + + . . + , , B B + B 1 [23,] B , , . , , . . , . , B + + + , 1 [24,] * , , . , , . , . . * * . , , . B 1 [25,] B + . . + . , . . . , B , * * . + . B B 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cor(matrix(rexp(30, 1), 5, 18))) # <<-- PATTERN ! -- [1,] 1 [2,] . 1 [3,] 1 [4,] . . 1 [5,] , . 1 [6,] , , 1 [7,] 1 . . 1 [8,] . 1 . , , . 1 [9,] 1 . 1 [10,] . . 1 . . 1 [11,] , . 1 , , . 1 [12,] , , 1 , , 1 [13,] 1 . . 1 . . 1 [14,] . 1 . , , . 1 . , , . 1 [15,] 1 . 1 . 1 [16,] . . 1 . . 1 . . 1 [17,] , . 1 , , . 1 , , . 1 [18,] , , 1 , , 1 , , 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cm1 <- cor(matrix(rnorm(90) , 5, 18))) # < White Noise SMALL n [1,] 1 [2,] . 1 [3,] . . 1 [4,] , , 1 [5,] . , 1 [6,] . * * 1 [7,] , . . . 1 [8,] , . . 1 [9,] . . . . . . 1 [10,] , . . . B . 1 [11,] , + . + + , . 1 [12,] , + + * . . , 1 [13,] . + + , + . 1 [14,] . . . + , , 1 [15,] . . . . . . + 1 [16,] . , . . . . . . 1 [17,] , + , + . . * . . 1 [18,] . . * , . . . , , . . 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cm1, diag=FALSE) [1,] [2,] . [3,] . . [4,] , , [5,] . , [6,] . * * [7,] , . . . [8,] , . . [9,] . . . . . . [10,] , . . . B . [11,] , + . + + , . [12,] , + + * . . , [13,] . + + , + . [14,] . . . + , , [15,] . . . . . . + [16,] . , . . . . . . [17,] , + , + . . * . . [18,] . . * , . . . , , . . attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cm2 <- cor(matrix(rnorm(900), 50, 18))) # < White Noise "BIG" n [1,] 1 [2,] 1 [3,] 1 [4,] 1 [5,] 1 [6,] 1 [7,] 1 [8,] 1 [9,] 1 [10,] 1 [11,] 1 [12,] 1 [13,] . . 1 [14,] 1 [15,] 1 [16,] 1 [17,] 1 [18,] 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > symnum(cm2, lower=FALSE) [1,] 1 [2,] 1 [3,] 1 [4,] 1 [5,] 1 [6,] 1 [7,] 1 [8,] 1 . [9,] 1 [10,] 1 [11,] 1 . [12,] 1 [13,] . . 1 [14,] 1 [15,] 1 [16,] 1 [17,] 1 [18,] 1 attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 > > ## NA's: > Cm <- cor(matrix(rnorm(60), 10, 6)); Cm[c(3,6), 2] <- NA > symnum(Cm, show.max=NULL) [1,] [2,] [3,] . ? [4,] . , [5,] . [6,] , ? attr(,"legend") [1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1 \t ## NA: ‘?’ > > ## Graphical P-values (aka "significance stars"): > pval <- rev(sort(c(outer(1:6, 10^-(1:3))))) > symp <- symnum(pval, corr=FALSE, + cutpoints = c(0, .001,.01,.05, .1, 1), + symbols = c("***","**","*","."," ")) > noquote(cbind(P.val = format(pval), Signif= symp)) P.val Signif [1,] 0.600 [2,] 0.500 [3,] 0.400 [4,] 0.300 [5,] 0.200 [6,] 0.100 . [7,] 0.060 . [8,] 0.050 * [9,] 0.040 * [10,] 0.030 * [11,] 0.020 * [12,] 0.010 ** [13,] 0.006 ** [14,] 0.005 ** [15,] 0.004 ** [16,] 0.003 ** [17,] 0.002 ** [18,] 0.001 *** > > > > cleanEx() > nameEx("t.test") > ### * t.test > > flush(stderr()); flush(stdout()) > > ### Name: t.test > ### Title: Student's t-Test > ### Aliases: t.test t.test.default t.test.formula > ### Keywords: htest > > ### ** Examples > > require(graphics) > > t.test(1:10,y=c(7:20)) # P = .00001855 Welch Two Sample t-test data: 1:10 and c(7:20) t = -5.4349, df = 21.982, p-value = 1.855e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -11.052802 -4.947198 sample estimates: mean of x mean of y 5.5 13.5 > t.test(1:10,y=c(7:20, 200)) # P = .1245 -- NOT significant anymore Welch Two Sample t-test data: 1:10 and c(7:20, 200) t = -1.6329, df = 14.165, p-value = 0.1245 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -47.242900 6.376233 sample estimates: mean of x mean of y 5.50000 25.93333 > > ## Classical example: Student's sleep data > plot(extra ~ group, data = sleep) > ## Traditional interface > with(sleep, t.test(extra[group == 1], extra[group == 2])) Welch Two Sample t-test data: extra[group == 1] and extra[group == 2] t = -1.8608, df = 17.776, p-value = 0.07939 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -3.3654832 0.2054832 sample estimates: mean of x mean of y 0.75 2.33 > ## Formula interface > t.test(extra ~ group, data = sleep) Welch Two Sample t-test data: extra by group t = -1.8608, df = 17.776, p-value = 0.07939 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -3.3654832 0.2054832 sample estimates: mean in group 1 mean in group 2 0.75 2.33 > > > > cleanEx() > nameEx("termplot") > ### * termplot > > flush(stderr()); flush(stdout()) > > ### Name: termplot > ### Title: Plot Regression Terms > ### Aliases: termplot > ### Keywords: hplot regression > > ### ** Examples > > require(graphics) > > had.splines <- "package:splines" %in% search() > if(!had.splines) rs <- require(splines) Loading required package: splines > x <- 1:100 > z <- factor(rep(LETTERS[1:4],25)) > y <- rnorm(100, sin(x/10)+as.numeric(z)) > model <- glm(y ~ ns(x,6) + z) > > par(mfrow=c(2,2)) ## 2 x 2 plots for same model : > termplot(model, main = paste("termplot( ", deparse(model$call)," ...)")) > termplot(model, rug=TRUE) > termplot(model, partial.resid=TRUE, se = TRUE, main = TRUE) > termplot(model, partial.resid=TRUE, smooth=panel.smooth, span.smth=1/4) > if(!had.splines && rs) detach("package:splines") > > > > graphics::par(get("par.postscript", pos = 'CheckExEnv')) > cleanEx() > nameEx("terms.object") > ### * terms.object > > flush(stderr()); flush(stdout()) > > ### Name: terms.object > ### Title: Description of Terms Objects > ### Aliases: terms.object > ### Keywords: models > > ### ** Examples > > ## use of specials (as used for gam() in packages mgcv and gam) > (tf <- terms(y ~ x + x:z + s(x), specials = "s")) y ~ x + x:z + s(x) attr(,"variables") list(y, x, z, s(x)) attr(,"factors") x s(x) x:z y 0 0 0 x 1 0 2 z 0 0 1 s(x) 0 1 0 attr(,"term.labels") [1] "x" "s(x)" "x:z" attr(,"specials") attr(,"specials")$s [1] 4 attr(,"order") [1] 1 1 2 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > ## Note that the "factors" attribute has variables as row names > ## and term labels as column names, both as character vectors. > attr(tf, "specials") # index 's' variable(s) $s [1] 4 > rownames(attr(tf, "factors"))[attr(tf, "specials")$s] [1] "s(x)" > > ## we can keep the order by > terms(y ~ x + x:z + s(x), specials = "s", keep.order = TRUE) y ~ x + x:z + s(x) attr(,"variables") list(y, x, z, s(x)) attr(,"factors") x x:z s(x) y 0 0 0 x 1 2 0 z 0 1 0 s(x) 0 0 1 attr(,"term.labels") [1] "x" "x:z" "s(x)" attr(,"specials") attr(,"specials")$s [1] 4 attr(,"order") [1] 1 2 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") > > > > cleanEx() > nameEx("time") > ### * time > > flush(stderr()); flush(stdout()) > > ### Name: time > ### Title: Sampling Times of Time Series > ### Aliases: time cycle frequency deltat time.default > ### Keywords: ts > > ### ** Examples > > require(graphics) > > cycle(presidents) Qtr1 Qtr2 Qtr3 Qtr4 1945 1 2 3 4 1946 1 2 3 4 1947 1 2 3 4 1948 1 2 3 4 1949 1 2 3 4 1950 1 2 3 4 1951 1 2 3 4 1952 1 2 3 4 1953 1 2 3 4 1954 1 2 3 4 1955 1 2 3 4 1956 1 2 3 4 1957 1 2 3 4 1958 1 2 3 4 1959 1 2 3 4 1960 1 2 3 4 1961 1 2 3 4 1962 1 2 3 4 1963 1 2 3 4 1964 1 2 3 4 1965 1 2 3 4 1966 1 2 3 4 1967 1 2 3 4 1968 1 2 3 4 1969 1 2 3 4 1970 1 2 3 4 1971 1 2 3 4 1972 1 2 3 4 1973 1 2 3 4 1974 1 2 3 4 > # a simple series plot > plot(as.vector(time(presidents)), as.vector(presidents), type="l") > > > > cleanEx() > nameEx("toeplitz") > ### * toeplitz > > flush(stderr()); flush(stdout()) > > ### Name: toeplitz > ### Title: Form Symmetric Toeplitz Matrix > ### Aliases: toeplitz > ### Keywords: ts > > ### ** Examples > > x <- 1:5 > toeplitz (x) [,1] [,2] [,3] [,4] [,5] [1,] 1 2 3 4 5 [2,] 2 1 2 3 4 [3,] 3 2 1 2 3 [4,] 4 3 2 1 2 [5,] 5 4 3 2 1 > > > > cleanEx() > nameEx("ts") > ### * ts > > flush(stderr()); flush(stdout()) > > ### Name: ts > ### Title: Time-Series Objects > ### Aliases: ts as.ts as.ts.default is.ts Ops.ts cbind.ts is.mts [.ts t.ts > ### Keywords: ts > > ### ** Examples > > require(graphics) > > ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959 Qtr1 Qtr2 Qtr3 Qtr4 1959 1 2 3 1960 4 5 6 7 1961 8 9 10 > print( ts(1:10, frequency = 7, start = c(12, 2)), calendar = TRUE) p1 p2 p3 p4 p5 p6 p7 12 1 2 3 4 5 6 13 7 8 9 10 > # print.ts(.) > ## Using July 1954 as start date: > gnp <- ts(cumsum(1 + round(rnorm(100), 2)), + start = c(1954, 7), frequency = 12) > plot(gnp) # using 'plot.ts' for time-series plot > > ## Multivariate > z <- ts(matrix(rnorm(300), 100, 3), start=c(1961, 1), frequency=12) > class(z) [1] "mts" "ts" > plot(z) > plot(z, plot.type="single", lty=1:3) > > ## A phase plot: > plot(nhtemp, c(nhtemp[-1], NA), cex = .8, col="blue", + main = "Lag plot of New Haven temperatures") > ## a clearer way to do this would be > ## Not run: > ##D plot(nhtemp, lag(nhtemp, 1), cex = .8, col="blue", > ##D main = "Lag plot of New Haven temperatures") > ## End(Not run) > > > cleanEx() > nameEx("ts.plot") > ### * ts.plot > > flush(stderr()); flush(stdout()) > > ### Name: ts.plot > ### Title: Plot Multiple Time Series > ### Aliases: ts.plot > ### Keywords: ts > > ### ** Examples > > require(graphics) > > ts.plot(ldeaths, mdeaths, fdeaths, + gpars=list(xlab="year", ylab="deaths", lty=c(1:3))) > > > > cleanEx() > nameEx("ts.union") > ### * ts.union > > flush(stderr()); flush(stdout()) > > ### Name: ts.union > ### Title: Bind Two or More Time Series > ### Aliases: ts.union ts.intersect > ### Keywords: ts > > ### ** Examples > > ts.union(mdeaths, fdeaths) mdeaths fdeaths Jan 1974 2134 901 Feb 1974 1863 689 Mar 1974 1877 827 Apr 1974 1877 677 May 1974 1492 522 Jun 1974 1249 406 Jul 1974 1280 441 Aug 1974 1131 393 Sep 1974 1209 387 Oct 1974 1492 582 Nov 1974 1621 578 Dec 1974 1846 666 Jan 1975 2103 830 Feb 1975 2137 752 Mar 1975 2153 785 Apr 1975 1833 664 May 1975 1403 467 Jun 1975 1288 438 Jul 1975 1186 421 Aug 1975 1133 412 Sep 1975 1053 343 Oct 1975 1347 440 Nov 1975 1545 531 Dec 1975 2066 771 Jan 1976 2020 767 Feb 1976 2750 1141 Mar 1976 2283 896 Apr 1976 1479 532 May 1976 1189 447 Jun 1976 1160 420 Jul 1976 1113 376 Aug 1976 970 330 Sep 1976 999 357 Oct 1976 1208 445 Nov 1976 1467 546 Dec 1976 2059 764 Jan 1977 2240 862 Feb 1977 1634 660 Mar 1977 1722 663 Apr 1977 1801 643 May 1977 1246 502 Jun 1977 1162 392 Jul 1977 1087 411 Aug 1977 1013 348 Sep 1977 959 387 Oct 1977 1179 385 Nov 1977 1229 411 Dec 1977 1655 638 Jan 1978 2019 796 Feb 1978 2284 853 Mar 1978 1942 737 Apr 1978 1423 546 May 1978 1340 530 Jun 1978 1187 446 Jul 1978 1098 431 Aug 1978 1004 362 Sep 1978 970 387 Oct 1978 1140 430 Nov 1978 1110 425 Dec 1978 1812 679 Jan 1979 2263 821 Feb 1979 1820 785 Mar 1979 1846 727 Apr 1979 1531 612 May 1979 1215 478 Jun 1979 1075 429 Jul 1979 1056 405 Aug 1979 975 379 Sep 1979 940 393 Oct 1979 1081 411 Nov 1979 1294 487 Dec 1979 1341 574 > cbind(mdeaths, fdeaths) # same as the previous line mdeaths fdeaths Jan 1974 2134 901 Feb 1974 1863 689 Mar 1974 1877 827 Apr 1974 1877 677 May 1974 1492 522 Jun 1974 1249 406 Jul 1974 1280 441 Aug 1974 1131 393 Sep 1974 1209 387 Oct 1974 1492 582 Nov 1974 1621 578 Dec 1974 1846 666 Jan 1975 2103 830 Feb 1975 2137 752 Mar 1975 2153 785 Apr 1975 1833 664 May 1975 1403 467 Jun 1975 1288 438 Jul 1975 1186 421 Aug 1975 1133 412 Sep 1975 1053 343 Oct 1975 1347 440 Nov 1975 1545 531 Dec 1975 2066 771 Jan 1976 2020 767 Feb 1976 2750 1141 Mar 1976 2283 896 Apr 1976 1479 532 May 1976 1189 447 Jun 1976 1160 420 Jul 1976 1113 376 Aug 1976 970 330 Sep 1976 999 357 Oct 1976 1208 445 Nov 1976 1467 546 Dec 1976 2059 764 Jan 1977 2240 862 Feb 1977 1634 660 Mar 1977 1722 663 Apr 1977 1801 643 May 1977 1246 502 Jun 1977 1162 392 Jul 1977 1087 411 Aug 1977 1013 348 Sep 1977 959 387 Oct 1977 1179 385 Nov 1977 1229 411 Dec 1977 1655 638 Jan 1978 2019 796 Feb 1978 2284 853 Mar 1978 1942 737 Apr 1978 1423 546 May 1978 1340 530 Jun 1978 1187 446 Jul 1978 1098 431 Aug 1978 1004 362 Sep 1978 970 387 Oct 1978 1140 430 Nov 1978 1110 425 Dec 1978 1812 679 Jan 1979 2263 821 Feb 1979 1820 785 Mar 1979 1846 727 Apr 1979 1531 612 May 1979 1215 478 Jun 1979 1075 429 Jul 1979 1056 405 Aug 1979 975 379 Sep 1979 940 393 Oct 1979 1081 411 Nov 1979 1294 487 Dec 1979 1341 574 > ts.intersect(window(mdeaths, 1976), window(fdeaths, 1974, 1978)) window(mdeaths, 1976) window(fdeaths, 1974, 1978) Jan 1976 2020 767 Feb 1976 2750 1141 Mar 1976 2283 896 Apr 1976 1479 532 May 1976 1189 447 Jun 1976 1160 420 Jul 1976 1113 376 Aug 1976 970 330 Sep 1976 999 357 Oct 1976 1208 445 Nov 1976 1467 546 Dec 1976 2059 764 Jan 1977 2240 862 Feb 1977 1634 660 Mar 1977 1722 663 Apr 1977 1801 643 May 1977 1246 502 Jun 1977 1162 392 Jul 1977 1087 411 Aug 1977 1013 348 Sep 1977 959 387 Oct 1977 1179 385 Nov 1977 1229 411 Dec 1977 1655 638 Jan 1978 2019 796 > > sales1 <- ts.union(BJsales, lead = BJsales.lead) > ts.intersect(sales1, lead3 = lag(BJsales.lead, -3)) Time Series: Start = 4 End = 150 Frequency = 1 sales1.BJsales sales1.lead lead3 4 198.9 9.75 10.01 5 199.0 10.33 10.07 6 200.2 10.13 10.32 7 198.6 10.36 9.75 8 200.0 10.32 10.33 9 200.3 10.13 10.13 10 201.2 10.16 10.36 11 201.6 10.58 10.32 12 201.5 10.62 10.13 13 201.5 10.86 10.16 14 203.5 11.20 10.58 15 204.9 10.74 10.62 16 207.1 10.56 10.86 17 210.5 10.48 11.20 18 210.5 10.77 10.74 19 209.8 11.33 10.56 20 208.8 10.96 10.48 21 209.5 11.16 10.77 22 213.2 11.70 11.33 23 213.7 11.39 10.96 24 215.1 11.42 11.16 25 218.7 11.94 11.70 26 219.8 11.24 11.39 27 220.5 11.59 11.42 28 223.8 10.96 11.94 29 222.8 11.40 11.24 30 223.8 11.02 11.59 31 221.7 11.01 10.96 32 222.3 11.23 11.40 33 220.8 11.33 11.02 34 219.4 10.83 11.01 35 220.1 10.84 11.23 36 220.6 11.14 11.33 37 218.9 10.38 10.83 38 217.8 10.90 10.84 39 217.7 11.05 11.14 40 215.0 11.11 10.38 41 215.3 11.01 10.90 42 215.9 11.22 11.05 43 216.7 11.21 11.11 44 216.7 11.91 11.01 45 217.7 11.69 11.22 46 218.7 10.93 11.21 47 222.9 10.99 11.91 48 224.9 11.01 11.69 49 222.2 10.84 10.93 50 220.7 10.76 10.99 51 220.0 10.77 11.01 52 218.7 10.88 10.84 53 217.0 10.49 10.76 54 215.9 10.50 10.77 55 215.8 11.00 10.88 56 214.1 10.98 10.49 57 212.3 10.61 10.50 58 213.9 10.48 11.00 59 214.6 10.53 10.98 60 213.6 11.07 10.61 61 212.1 10.61 10.48 62 211.4 10.86 10.53 63 213.1 10.34 11.07 64 212.9 10.78 10.61 65 213.3 10.80 10.86 66 211.5 10.33 10.34 67 212.3 10.44 10.78 68 213.0 10.50 10.80 69 211.0 10.75 10.33 70 210.7 10.40 10.44 71 210.1 10.40 10.50 72 211.4 10.34 10.75 73 210.0 10.55 10.40 74 209.7 10.46 10.40 75 208.8 10.82 10.34 76 208.8 10.91 10.55 77 208.8 10.87 10.46 78 210.6 10.67 10.82 79 211.9 11.11 10.91 80 212.8 10.88 10.87 81 212.5 11.28 10.67 82 214.8 11.27 11.11 83 215.3 11.44 10.88 84 217.5 11.52 11.28 85 218.8 12.10 11.27 86 220.7 11.83 11.44 87 222.2 12.62 11.52 88 226.7 12.41 12.10 89 228.4 12.43 11.83 90 233.2 12.73 12.62 91 235.7 13.01 12.41 92 237.1 12.74 12.43 93 240.6 12.73 12.73 94 243.8 12.76 13.01 95 245.3 12.92 12.74 96 246.0 12.64 12.73 97 246.3 12.79 12.76 98 247.7 13.05 12.92 99 247.6 12.69 12.64 100 247.8 13.01 12.79 101 249.4 12.90 13.05 102 249.0 13.12 12.69 103 249.9 12.47 13.01 104 250.5 12.47 12.90 105 251.5 12.94 13.12 106 249.0 13.10 12.47 107 247.6 12.91 12.47 108 248.8 13.39 12.94 109 250.4 13.13 13.10 110 250.7 13.34 12.91 111 253.0 13.34 13.39 112 253.7 13.14 13.13 113 255.0 13.49 13.34 114 256.2 13.87 13.34 115 256.0 13.39 13.14 116 257.4 13.59 13.49 117 260.4 13.27 13.87 118 260.0 13.70 13.39 119 261.3 13.20 13.59 120 260.4 13.32 13.27 121 261.6 13.15 13.70 122 260.8 13.30 13.20 123 259.8 12.94 13.32 124 259.0 13.29 13.15 125 258.9 13.26 13.30 126 257.4 13.08 12.94 127 257.7 13.24 13.29 128 257.9 13.31 13.26 129 257.4 13.52 13.08 130 257.3 13.02 13.24 131 257.6 13.25 13.31 132 258.9 13.12 13.52 133 257.8 13.26 13.02 134 257.7 13.11 13.25 135 257.2 13.30 13.12 136 257.5 13.06 13.26 137 256.8 13.32 13.11 138 257.5 13.10 13.30 139 257.0 13.27 13.06 140 257.6 13.64 13.32 141 257.3 13.58 13.10 142 257.5 13.87 13.27 143 259.6 13.53 13.64 144 261.1 13.41 13.58 145 262.9 13.25 13.87 146 263.3 13.50 13.53 147 262.8 13.58 13.41 148 261.8 13.51 13.25 149 262.2 13.77 13.50 150 262.7 13.40 13.58 > > > > cleanEx() > nameEx("tsdiag") > ### * tsdiag > > flush(stderr()); flush(stdout()) > > ### Name: tsdiag > ### Title: Diagnostic Plots for Time-Series Fits > ### Aliases: tsdiag tsdiag.arima0 tsdiag.Arima tsdiag.StructTS > ### Keywords: ts > > ### ** Examples > > ## Not run: > ##D require(graphics) > ##D > ##D fit <- arima(lh, c(1,0,0)) > ##D tsdiag(fit) > ##D > ##D ## see also examples(arima) > ##D > ##D (fit <- StructTS(log10(JohnsonJohnson), type="BSM")) > ##D tsdiag(fit) > ## End(Not run) > > > cleanEx() > nameEx("uniroot") > ### * uniroot > > flush(stderr()); flush(stdout()) > > ### Name: uniroot > ### Title: One Dimensional Root (Zero) Finding > ### Aliases: uniroot > ### Keywords: optimize > > ### ** Examples > > > cleanEx() > nameEx("update") > ### * update > > flush(stderr()); flush(stdout()) > > ### Name: update > ### Title: Update and Re-fit a Model Call > ### Aliases: update update.default getCall getCall.default > ### Keywords: models > > ### ** Examples > > oldcon <- options(contrasts = c("contr.treatment", "contr.poly")) > ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". > ## Page 9: Plant Weight Data. > ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) > trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) > group <- gl(2, 10, 20, labels = c("Ctl", "Trt")) > weight <- c(ctl, trt) > lm.D9 <- lm(weight ~ group) > lm.D9 Call: lm(formula = weight ~ group) Coefficients: (Intercept) groupTrt 5.032 -0.371 > summary(lm.D90 <- update(lm.D9, . ~ . - 1)) Call: lm(formula = weight ~ group - 1) Residuals: Min 1Q Median 3Q Max -1.0710 -0.4938 0.0685 0.2462 1.3690 Coefficients: Estimate Std. Error t value Pr(>|t|) groupCtl 5.0320 0.2202 22.85 9.55e-15 *** groupTrt 4.6610 0.2202 21.16 3.62e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6964 on 18 degrees of freedom Multiple R-squared: 0.9818, Adjusted R-squared: 0.9798 F-statistic: 485.1 on 2 and 18 DF, p-value: < 2.2e-16 > options(contrasts = c("contr.helmert", "contr.poly")) > update(lm.D9) Call: lm(formula = weight ~ group) Coefficients: (Intercept) group1 4.8465 -0.1855 > getCall(lm.D90)# "through the origin" lm(formula = weight ~ group - 1) > > options(oldcon) > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > cleanEx() > nameEx("update.formula") > ### * update.formula > > flush(stderr()); flush(stdout()) > > ### Name: update.formula > ### Title: Model Updating > ### Aliases: update.formula > ### Keywords: models > > ### ** Examples > > update(y ~ x, ~ . + x2) #> y ~ x + x2 y ~ x + x2 > update(y ~ x, log(.) ~ . ) #> log(y) ~ x log(y) ~ x > > > > cleanEx() > nameEx("var.test") > ### * var.test > > flush(stderr()); flush(stdout()) > > ### Name: var.test > ### Title: F Test to Compare Two Variances > ### Aliases: var.test var.test.default var.test.formula > ### Keywords: htest > > ### ** Examples > > x <- rnorm(50, mean = 0, sd = 2) > y <- rnorm(30, mean = 1, sd = 1) > var.test(x, y) # Do x and y have the same variance? F test to compare two variances data: x and y F = 2.6522, num df = 49, denom df = 29, p-value = 0.006232 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 1.332510 4.989832 sample estimates: ratio of variances 2.652168 > var.test(lm(x ~ 1), lm(y ~ 1)) # The same. F test to compare two variances data: lm(x ~ 1) and lm(y ~ 1) F = 2.6522, num df = 49, denom df = 29, p-value = 0.006232 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 1.332510 4.989832 sample estimates: ratio of variances 2.652168 > > > > cleanEx() > nameEx("varimax") > ### * varimax > > flush(stderr()); flush(stdout()) > > ### Name: varimax > ### Title: Rotation Methods for Factor Analysis > ### Aliases: promax varimax > ### Keywords: multivariate > > ### ** Examples > > ## varimax with normalize = TRUE is the default > fa <- factanal( ~., 2, data = swiss) > varimax(loadings(fa), normalize = FALSE) $loadings Loadings: Factor1 Factor2 Fertility -0.650 0.398 Agriculture -0.628 0.337 Examination 0.681 -0.515 Education 0.997 Catholic -0.117 0.962 Infant.Mortality 0.176 Factor1 Factor2 SS loadings 2.297 1.496 Proportion Var 0.383 0.249 Cumulative Var 0.383 0.632 $rotmat [,1] [,2] [1,] 0.999973881 -0.007227595 [2,] 0.007227595 0.999973881 > promax(loadings(fa)) $loadings Loadings: Factor1 Factor2 Fertility -0.595 0.227 Agriculture -0.599 0.160 Examination 0.577 -0.360 Education 1.192 0.363 Catholic 0.326 1.147 Infant.Mortality 0.180 Factor1 Factor2 SS loadings 2.574 1.686 Proportion Var 0.429 0.281 Cumulative Var 0.429 0.710 $rotmat [,1] [,2] [1,] 1.2114045 0.4029296 [2,] 0.4956199 1.2453063 > > > > cleanEx() > nameEx("weighted.mean") > ### * weighted.mean > > flush(stderr()); flush(stdout()) > > ### Name: weighted.mean > ### Title: Weighted Arithmetic Mean > ### Aliases: weighted.mean weighted.mean.default > ### Keywords: univar > > ### ** Examples > > ## GPA from Siegel 1994 > wt <- c(5, 5, 4, 1)/15 > x <- c(3.7,3.3,3.5,2.8) > xm <- weighted.mean(x, wt) > > > > cleanEx() > nameEx("weighted.residuals") > ### * weighted.residuals > > flush(stderr()); flush(stdout()) > > ### Name: weighted.residuals > ### Title: Compute Weighted Residuals > ### Aliases: weighted.residuals > ### Keywords: regression > > ### ** Examples > > ## following on from example(lm) > ## Don't show: > utils::example("lm", echo = FALSE) > ## End Don't show > all.equal(weighted.residuals(lm.D9), + residuals(lm.D9)) [1] TRUE > x <- 1:10 > w <- 0:9 > y <- rnorm(x) > weighted.residuals(lmxy <- lm(y ~ x, weights = w)) 2 3 4 5 6 7 -0.15744267 -1.63534445 2.24282864 0.09895229 -2.41528559 0.60771924 8 9 10 1.37406419 1.06675699 -1.45098819 > weighted.residuals(lmxy, drop0 = FALSE) 1 2 3 4 5 6 0.00000000 -0.15744267 -1.63534445 2.24282864 0.09895229 -2.41528559 7 8 9 10 0.60771924 1.37406419 1.06675699 -1.45098819 > > > > cleanEx() > nameEx("wilcox.test") > ### * wilcox.test > > flush(stderr()); flush(stdout()) > > ### Name: wilcox.test > ### Title: Wilcoxon Rank Sum and Signed Rank Tests > ### Aliases: wilcox.test wilcox.test.default wilcox.test.formula > ### Keywords: htest > > ### ** Examples > > require(graphics) > ## One-sample test. > ## Hollander & Wolfe (1973), 29f. > ## Hamilton depression scale factor measurements in 9 patients with > ## mixed anxiety and depression, taken at the first (x) and second > ## (y) visit after initiation of a therapy (administration of a > ## tranquilizer). > x <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30) > y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29) > wilcox.test(x, y, paired = TRUE, alternative = "greater") Wilcoxon signed rank test data: x and y V = 40, p-value = 0.01953 alternative hypothesis: true location shift is greater than 0 > wilcox.test(y - x, alternative = "less") # The same. Wilcoxon signed rank test data: y - x V = 5, p-value = 0.01953 alternative hypothesis: true location is less than 0 > wilcox.test(y - x, alternative = "less", + exact = FALSE, correct = FALSE) # H&W large sample Wilcoxon signed rank test data: y - x V = 5, p-value = 0.01908 alternative hypothesis: true location is less than 0 > # approximation > > ## Two-sample test. > ## Hollander & Wolfe (1973), 69f. > ## Permeability constants of the human chorioamnion (a placental > ## membrane) at term (x) and between 12 to 26 weeks gestational > ## age (y). The alternative of interest is greater permeability > ## of the human chorioamnion for the term pregnancy. > x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46) > y <- c(1.15, 0.88, 0.90, 0.74, 1.21) > wilcox.test(x, y, alternative = "g") # greater Wilcoxon rank sum test data: x and y W = 35, p-value = 0.1272 alternative hypothesis: true location shift is greater than 0 > wilcox.test(x, y, alternative = "greater", + exact = FALSE, correct = FALSE) # H&W large sample Wilcoxon rank sum test data: x and y W = 35, p-value = 0.1103 alternative hypothesis: true location shift is greater than 0 > # approximation > > wilcox.test(rnorm(10), rnorm(10, 2), conf.int = TRUE) Wilcoxon rank sum test data: rnorm(10) and rnorm(10, 2) W = 7, p-value = 0.0004871 alternative hypothesis: true location shift is not equal to 0 95 percent confidence interval: -3.024352 -1.348555 sample estimates: difference in location -2.262424 > > ## Formula interface. > boxplot(Ozone ~ Month, data = airquality) > wilcox.test(Ozone ~ Month, data = airquality, + subset = Month %in% c(5, 8)) Warning in wilcox.test.default(x = c(41L, 36L, 12L, 18L, 28L, 23L, 19L, : cannot compute exact p-value with ties Wilcoxon rank sum test with continuity correction data: Ozone by Month W = 127.5, p-value = 0.0001208 alternative hypothesis: true location shift is not equal to 0 > > > > cleanEx() > nameEx("window") > ### * window > > flush(stderr()); flush(stdout()) > > ### Name: window > ### Title: Time Windows > ### Aliases: window window.default window.ts window<- window<-.ts > ### Keywords: ts > > ### ** Examples > > window(presidents, 1960, c(1969,4)) # values in the 1960's Qtr1 Qtr2 Qtr3 Qtr4 1960 71 62 61 57 1961 72 83 71 78 1962 79 71 62 74 1963 76 64 62 57 1964 80 73 69 69 1965 71 64 69 62 1966 63 46 56 44 1967 44 52 38 46 1968 36 49 35 44 1969 59 65 65 56 > window(presidents, deltat=1) # All Qtr1s Time Series: Start = 1945 End = 1974 Frequency = 1 [1] NA 63 35 36 69 45 36 25 59 71 71 76 79 60 57 71 72 79 76 80 71 63 44 36 59 [26] 66 51 49 68 28 > window(presidents, start=c(1945,3), deltat=1) # All Qtr3s Time Series: Start = 1945.5 End = 1974.5 Frequency = 1 [1] 82 43 54 NA 57 46 32 NA 75 71 79 67 63 48 61 61 71 62 62 69 69 56 38 35 65 [26] 61 54 NA 40 24 > window(presidents, 1944, c(1979,2), extend=TRUE) Qtr1 Qtr2 Qtr3 Qtr4 1944 NA NA NA NA 1945 NA 87 82 75 1946 63 50 43 32 1947 35 60 54 55 1948 36 39 NA NA 1949 69 57 57 51 1950 45 37 46 39 1951 36 24 32 23 1952 25 32 NA 32 1953 59 74 75 60 1954 71 61 71 57 1955 71 68 79 73 1956 76 71 67 75 1957 79 62 63 57 1958 60 49 48 52 1959 57 62 61 66 1960 71 62 61 57 1961 72 83 71 78 1962 79 71 62 74 1963 76 64 62 57 1964 80 73 69 69 1965 71 64 69 62 1966 63 46 56 44 1967 44 52 38 46 1968 36 49 35 44 1969 59 65 65 56 1970 66 53 61 52 1971 51 48 54 49 1972 49 61 NA NA 1973 68 44 40 27 1974 28 25 24 24 1975 NA NA NA NA 1976 NA NA NA NA 1977 NA NA NA NA 1978 NA NA NA NA 1979 NA NA > > pres <- window(presidents, 1945, c(1949,4)) # values in the 1940's > window(pres, 1945.25, 1945.50) <- c(60, 70) > window(pres, 1944, 1944.75) <- 0 # will generate a warning Warning: extending time series when replacing values > window(pres, c(1945,4), c(1949,4), frequency=1) <- 85:89 > pres Qtr1 Qtr2 Qtr3 Qtr4 1944 0 0 0 0 1945 NA 60 70 85 1946 63 50 43 86 1947 35 60 54 87 1948 36 39 NA 88 1949 69 57 57 89 > > > > cleanEx() > nameEx("xtabs") > ### * xtabs > > flush(stderr()); flush(stdout()) > > ### Name: xtabs > ### Title: Cross Tabulation > ### Aliases: xtabs print.xtabs > ### Keywords: category > > ### ** Examples > > ## 'esoph' has the frequencies of cases and controls for all levels of > ## the variables 'agegp', 'alcgp', and 'tobgp'. > xtabs(cbind(ncases, ncontrols) ~ ., data = esoph) , , tobgp = 0-9g/day, = ncases alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 0 0 0 0 35-44 0 0 0 2 45-54 1 6 3 4 55-64 2 9 9 5 65-74 5 17 6 3 75+ 1 2 1 2 , , tobgp = 10-19, = ncases alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 0 0 0 1 35-44 1 3 0 0 45-54 0 4 6 3 55-64 3 6 8 6 65-74 4 3 4 1 75+ 2 1 1 1 , , tobgp = 20-29, = ncases alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 0 0 0 0 35-44 0 1 0 2 45-54 0 5 1 2 55-64 3 4 3 2 65-74 2 5 2 1 75+ 0 0 0 0 , , tobgp = 30+, = ncases alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 0 0 0 0 35-44 0 0 0 0 45-54 0 5 2 4 55-64 4 3 4 5 65-74 0 0 1 1 75+ 1 1 0 0 , , tobgp = 0-9g/day, = ncontrols alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 40 27 2 1 35-44 60 35 11 3 45-54 46 38 16 4 55-64 49 40 18 10 65-74 48 34 13 4 75+ 18 5 1 2 , , tobgp = 10-19, = ncontrols alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 10 7 1 1 35-44 14 23 6 3 45-54 18 21 14 4 55-64 22 21 15 7 65-74 14 10 12 2 75+ 6 3 1 1 , , tobgp = 20-29, = ncontrols alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 6 4 0 1 35-44 7 14 2 4 45-54 10 15 5 3 55-64 12 17 6 3 65-74 7 9 3 1 75+ 0 3 0 0 , , tobgp = 30+, = ncontrols alcgp agegp 0-39g/day 40-79 80-119 120+ 25-34 5 7 2 2 35-44 8 8 1 0 45-54 4 7 4 4 55-64 6 6 4 6 65-74 2 0 1 1 75+ 3 1 0 0 > ## Output is not really helpful ... flat tables are better: > ftable(xtabs(cbind(ncases, ncontrols) ~ ., data = esoph)) ncases ncontrols agegp alcgp tobgp 25-34 0-39g/day 0-9g/day 0 40 10-19 0 10 20-29 0 6 30+ 0 5 40-79 0-9g/day 0 27 10-19 0 7 20-29 0 4 30+ 0 7 80-119 0-9g/day 0 2 10-19 0 1 20-29 0 0 30+ 0 2 120+ 0-9g/day 0 1 10-19 1 1 20-29 0 1 30+ 0 2 35-44 0-39g/day 0-9g/day 0 60 10-19 1 14 20-29 0 7 30+ 0 8 40-79 0-9g/day 0 35 10-19 3 23 20-29 1 14 30+ 0 8 80-119 0-9g/day 0 11 10-19 0 6 20-29 0 2 30+ 0 1 120+ 0-9g/day 2 3 10-19 0 3 20-29 2 4 30+ 0 0 45-54 0-39g/day 0-9g/day 1 46 10-19 0 18 20-29 0 10 30+ 0 4 40-79 0-9g/day 6 38 10-19 4 21 20-29 5 15 30+ 5 7 80-119 0-9g/day 3 16 10-19 6 14 20-29 1 5 30+ 2 4 120+ 0-9g/day 4 4 10-19 3 4 20-29 2 3 30+ 4 4 55-64 0-39g/day 0-9g/day 2 49 10-19 3 22 20-29 3 12 30+ 4 6 40-79 0-9g/day 9 40 10-19 6 21 20-29 4 17 30+ 3 6 80-119 0-9g/day 9 18 10-19 8 15 20-29 3 6 30+ 4 4 120+ 0-9g/day 5 10 10-19 6 7 20-29 2 3 30+ 5 6 65-74 0-39g/day 0-9g/day 5 48 10-19 4 14 20-29 2 7 30+ 0 2 40-79 0-9g/day 17 34 10-19 3 10 20-29 5 9 30+ 0 0 80-119 0-9g/day 6 13 10-19 4 12 20-29 2 3 30+ 1 1 120+ 0-9g/day 3 4 10-19 1 2 20-29 1 1 30+ 1 1 75+ 0-39g/day 0-9g/day 1 18 10-19 2 6 20-29 0 0 30+ 1 3 40-79 0-9g/day 2 5 10-19 1 3 20-29 0 3 30+ 1 1 80-119 0-9g/day 1 1 10-19 1 1 20-29 0 0 30+ 0 0 120+ 0-9g/day 2 2 10-19 1 1 20-29 0 0 30+ 0 0 > ## In particular if we have fewer factors ... > ftable(xtabs(cbind(ncases, ncontrols) ~ agegp, data = esoph)) ncases ncontrols agegp 25-34 1 116 35-44 9 199 45-54 46 213 55-64 76 242 65-74 55 161 75+ 13 44 > > ## This is already a contingency table in array form. > DF <- as.data.frame(UCBAdmissions) > ## Now 'DF' is a data frame with a grid of the factors and the counts > ## in variable 'Freq'. > DF Admit Gender Dept Freq 1 Admitted Male A 512 2 Rejected Male A 313 3 Admitted Female A 89 4 Rejected Female A 19 5 Admitted Male B 353 6 Rejected Male B 207 7 Admitted Female B 17 8 Rejected Female B 8 9 Admitted Male C 120 10 Rejected Male C 205 11 Admitted Female C 202 12 Rejected Female C 391 13 Admitted Male D 138 14 Rejected Male D 279 15 Admitted Female D 131 16 Rejected Female D 244 17 Admitted Male E 53 18 Rejected Male E 138 19 Admitted Female E 94 20 Rejected Female E 299 21 Admitted Male F 22 22 Rejected Male F 351 23 Admitted Female F 24 24 Rejected Female F 317 > ## Nice for taking margins ... > xtabs(Freq ~ Gender + Admit, DF) Admit Gender Admitted Rejected Male 1198 1493 Female 557 1278 > ## And for testing independence ... > summary(xtabs(Freq ~ ., DF)) Call: xtabs(formula = Freq ~ ., data = DF) Number of cases in table: 4526 Number of factors: 3 Test for independence of all factors: Chisq = 2000.3, df = 16, p-value = 0 > > ## Create a nice display for the warp break data. > warpbreaks$replicate <- rep(1:9, len = 54) > ftable(xtabs(breaks ~ wool + tension + replicate, data = warpbreaks)) replicate 1 2 3 4 5 6 7 8 9 wool tension A L 26 30 54 25 70 52 51 26 67 M 18 21 29 17 12 18 35 30 36 H 36 21 24 18 10 43 28 15 26 B L 27 14 29 19 29 31 41 20 44 M 42 26 19 16 39 28 21 39 29 H 20 21 24 17 13 15 15 16 28 > > ### ---- Sparse Examples ---- > > if(require("Matrix")) { + ## similar to "nlme"s 'ergoStool' : + d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)), + Subj = gl(9,4, 36*4)) + print(xtabs(~ Type + Subj, data=d.ergo)) # 4 replicates each + set.seed(15) # a subset of cases: + print(xtabs(~ Type + Subj, data=d.ergo[sample(36, 10),], sparse=TRUE)) + + ## Hypothetical two level setup: + inner <- factor(sample(letters[1:25], 100, replace = TRUE)) + inout <- factor(sample(LETTERS[1:5], 25, replace = TRUE)) + fr <- data.frame(inner = inner, outer = inout[as.integer(inner)]) + print(xtabs(~ inner + outer, fr, sparse = TRUE)) + } Loading required package: Matrix Loading required package: lattice Subj Type 1 2 3 4 5 6 7 8 9 T1 4 4 4 4 4 4 4 4 4 T2 4 4 4 4 4 4 4 4 4 T3 4 4 4 4 4 4 4 4 4 T4 4 4 4 4 4 4 4 4 4 4 x 9 sparse Matrix of class "dgCMatrix" 1 2 3 4 5 6 7 8 9 T1 . 1 . 1 . 1 . 1 . T2 1 . . . . . 1 . 1 T3 . . . . 1 . . . . T4 1 . . . . . 1 . . 25 x 5 sparse Matrix of class "dgCMatrix" A B C D E a 2 . . . . b . . 1 . . c . 6 . . . d . . . 5 . e . . . 3 . f 1 . . . . g . 9 . . . h . . 3 . . i . . . . 5 j . . . 1 . k 3 . . . . l . 2 . . . m 6 . . . . n . . 1 . . o 2 . . . . p . . 2 . . q . 5 . . . r . . . . 6 s . . 4 . . t . . . 7 . u . 4 . . . v . . . 7 . w . . . 7 . x . . . . 6 y . . . . 2 > > > > cleanEx() detaching ‘package:Matrix’, ‘package:lattice’ > nameEx("zC") > ### * zC > > flush(stderr()); flush(stdout()) > > ### Name: C > ### Title: Sets Contrasts for a Factor > ### Aliases: C > ### Keywords: models > > ### ** Examples > > ## reset contrasts to defaults > options(contrasts=c("contr.treatment", "contr.poly")) > tens <- with(warpbreaks, C(tension, poly, 1)) > ## tension SHOULD be an ordered factor, but as it is not we can use > aov(breaks ~ wool + tens + tension, data=warpbreaks) Call: aov(formula = breaks ~ wool + tens + tension, data = warpbreaks) Terms: wool tens tension Residuals Sum of Squares 450.667 1950.694 83.565 6747.889 Deg. of Freedom 1 1 1 50 Residual standard error: 11.61713 1 out of 5 effects not estimable Estimated effects may be unbalanced > > ## show the use of ... The default contrast is contr.treatment here > summary(lm(breaks ~ wool + C(tension, base=2), data=warpbreaks)) Call: lm(formula = breaks ~ wool + C(tension, base = 2), data = warpbreaks) Residuals: Min 1Q Median 3Q Max -19.500 -8.083 -2.139 6.472 30.722 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 29.278 3.162 9.260 2e-12 *** woolB -5.778 3.162 -1.827 0.0736 . C(tension, base = 2)1 10.000 3.872 2.582 0.0128 * C(tension, base = 2)3 -4.722 3.872 -1.219 0.2284 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.62 on 50 degrees of freedom Multiple R-squared: 0.2691, Adjusted R-squared: 0.2253 F-statistic: 6.138 on 3 and 50 DF, p-value: 0.00123 > > > # following on from help(esoph) > model3 <- glm(cbind(ncases, ncontrols) ~ agegp + C(tobgp, , 1) + + C(alcgp, , 1), data = esoph, family = binomial()) > summary(model3) Call: glm(formula = cbind(ncases, ncontrols) ~ agegp + C(tobgp, , 1) + C(alcgp, , 1), family = binomial(), data = esoph) Deviance Residuals: Min 1Q Median 3Q Max -1.7628 -0.6426 -0.2709 0.3043 2.0421 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.72420 0.19582 -8.805 < 2e-16 *** agegp.L 2.96113 0.65092 4.549 5.39e-06 *** agegp.Q -1.33735 0.58918 -2.270 0.02322 * agegp.C 0.15292 0.44792 0.341 0.73281 agegp^4 0.06668 0.30776 0.217 0.82848 agegp^5 -0.20288 0.19523 -1.039 0.29872 C(tobgp, , 1).L 0.58501 0.18331 3.191 0.00142 ** C(alcgp, , 1).L 1.46034 0.18899 7.727 1.10e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 227.241 on 87 degrees of freedom Residual deviance: 59.277 on 80 degrees of freedom AIC: 222.76 Number of Fisher Scoring iterations: 6 > > > > options(contrasts = c(unordered = "contr.treatment",ordered = "contr.poly")) > ### *