The R Project SVN
R
(root)/tags/R-1-6-2/tests/reg-tests-2.Rout.save – Rev 22676
Rev 21943 | Blame | Compare with Previous | Last modification | View Log | Download | RSS feed
R : Copyright 2002, The R Development Core TeamVersion 1.6.1 Patched (2002-11-17)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.R is a collaborative project with many contributors.Type `contributors()' for more information.Type `demo()' for some demos, `help()' for on-line help, or`help.start()' for a HTML browser interface to help.Type `q()' to quit R.> ### Regression tests for which the printed output is the issue>> ## PR 715 (Printing list elements w/attributes)> ##> l <- list(a=10)> attr(l$a, "xx") <- 23> l$a[1] 10attr(,"xx")[1] 23> ## Comments:> ## should print as> # $a:> # [1] 10> # attr($a, "xx"):> # [1] 23>> ## On the other hand> m <- matrix(c(1, 2, 3, 0, 10, NA), 3, 2)> na.omit(m)[,1] [,2][1,] 1 0[2,] 2 10attr(,"na.action")[1] 3attr(,"na.action")attr(,"class")[1] "omit"> ## should print as> # [,1] [,2]> # [1,] 1 0> # [2,] 2 10> # attr(,"na.action")> # [1] 3> # attr(,"na.action")attr(,"class")> # [1] "omit">> ## and> x <- 1> attr(x, "foo") <- list(a="a")> x[1] 1attr(,"foo")attr(,"foo")$a[1] "a"> ## should print as> # [1] 1> # attr(,"foo")> # attr(,"foo")$a> # [1] "a">>> ## PR 746 (printing of lists)> ##> test.list <- list(A = list(formula=Y~X, subset=TRUE),+ B = list(formula=Y~X, subset=TRUE))>> test.list$A$A$formulaY ~ X$A$subset[1] TRUE$B$B$formulaY ~ X$B$subset[1] TRUE> ## Comments:> ## should print as> # $A> # $A$formula> # Y ~ X> #> # $A$subset> # [1] TRUE> #> #> # $B> # $B$formula> # Y ~ X> #> # $B$subset> # [1] TRUE>> ## Marc Feldesman 2001-Feb-01. Precision in summary.data.frame & *.matrix> data(attenu)> summary(attenu)event mag station distMin. : 1.00 Min. :5.000 117 : 5 Min. : 0.501st Qu.: 9.00 1st Qu.:5.300 1028 : 4 1st Qu.: 11.32Median :18.00 Median :6.100 113 : 4 Median : 23.40Mean :14.74 Mean :6.084 112 : 3 Mean : 45.603rd Qu.:20.00 3rd Qu.:6.600 135 : 3 3rd Qu.: 47.55Max. :23.00 Max. :7.700 (Other):147 Max. :370.00NA's : 16accelMin. :0.003001st Qu.:0.04425Median :0.11300Mean :0.154223rd Qu.:0.21925Max. :0.81000> summary(attenu, digits = 5)event mag station distMin. : 1.000 Min. :5.0000 117 : 5 Min. : 0.5001st Qu.: 9.000 1st Qu.:5.3000 1028 : 4 1st Qu.: 11.325Median :18.000 Median :6.1000 113 : 4 Median : 23.400Mean :14.742 Mean :6.0841 112 : 3 Mean : 45.6033rd Qu.:20.000 3rd Qu.:6.6000 135 : 3 3rd Qu.: 47.550Max. :23.000 Max. :7.7000 (Other):147 Max. :370.000NA's : 16accelMin. :0.003001st Qu.:0.04425Median :0.11300Mean :0.154223rd Qu.:0.21925Max. :0.81000> summary(data.matrix(attenu), digits = 5)# the same for matrixevent mag station distMin. : 1.000 Min. :5.0000 Min. : 1.000 Min. : 0.5001st Qu.: 9.000 1st Qu.:5.3000 1st Qu.: 24.250 1st Qu.: 11.325Median :18.000 Median :6.1000 Median : 56.500 Median : 23.400Mean :14.742 Mean :6.0841 Mean : 56.928 Mean : 45.6033rd Qu.:20.000 3rd Qu.:6.6000 3rd Qu.: 86.750 3rd Qu.: 47.550Max. :23.000 Max. :7.7000 Max. :117.000 Max. :370.000NA's : 16.000accelMin. :0.003001st Qu.:0.04425Median :0.11300Mean :0.154223rd Qu.:0.21925Max. :0.81000> ## Comments:> ## No difference between these in 1.2.1 and earlier> set.seed(1)> x <- c(round(runif(10), 2), 10000)> summary(x)Min. 1st Qu. Median Mean 3rd Qu. Max.0.000 0.050 0.550 909.400 0.675 10000.000> summary(data.frame(x))xMin. : 0.0001st Qu.: 0.050Median : 0.550Mean : 909.4233rd Qu.: 0.675Max. :10000.000> ## Comments:> ## All entries show all 3 digits after the decimal point now.>> ## Chong Gu 2001-Feb-16. step on binomials> "detg1" <-+ structure(list(Temp = structure(c(2, 1, 2, 1, 2, 1, 2, 1, 2,+ 1, 2, 1), .Label = c("High", "Low"), class = "factor"), M.user = structure(c(1,+ 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2), .Label = c("N", "Y"), class = "factor"),+ Soft = structure(c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3), .Label = c("Hard",+ "Medium", "Soft"), class = "factor"), M = c(42, 30, 52, 43,+ 50, 23, 55, 47, 53, 27, 49, 29), X = c(68, 42, 37, 24, 66,+ 33, 47, 23, 63, 29, 57, 19)), .Names = c("Temp", "M.user",+ "Soft", "M", "X"), class = "data.frame", row.names = c("1", "3",+ "5", "7", "9", "11", "13", "15", "17", "19", "21", "23"))> detg1.m0 <- glm(cbind(X,M)~1,binomial,detg1)> detg1.m0Call: glm(formula = cbind(X, M) ~ 1, family = binomial, data = detg1)Coefficients:(Intercept)0.01587Degrees of Freedom: 11 Total (i.e. Null); 11 ResidualNull Deviance: 32.83Residual Deviance: 32.83 AIC: 92.52> step(detg1.m0,scope=list(upper=~M.user*Temp*Soft))Start: AIC= 92.52cbind(X, M) ~ 1Df Deviance AIC+ M.user 1 12.244 73.942+ Temp 1 28.464 90.162<none> 32.826 92.524+ Soft 2 32.430 96.128Step: AIC= 73.94cbind(X, M) ~ M.userDf Deviance AIC+ Temp 1 8.444 72.142<none> 12.244 73.942+ Soft 2 11.967 77.665- M.user 1 32.826 92.524Step: AIC= 72.14cbind(X, M) ~ M.user + TempDf Deviance AIC+ M.user:Temp 1 5.656 71.354<none> 8.444 72.142- Temp 1 12.244 73.942+ Soft 2 8.228 75.926- M.user 1 28.464 90.162Step: AIC= 71.35cbind(X, M) ~ M.user + Temp + M.user:TempDf Deviance AIC<none> 5.656 71.354- M.user:Temp 1 8.444 72.142+ Soft 2 5.495 75.193Call: glm(formula = cbind(X, M) ~ M.user + Temp + M.user:Temp, family = binomial, data = detg1)Coefficients:(Intercept) M.userY TempLow M.userY:TempLow0.26236 -0.85183 0.04411 0.44427Degrees of Freedom: 11 Total (i.e. Null); 8 ResidualNull Deviance: 32.83Residual Deviance: 5.656 AIC: 71.35>> ## PR 829 (empty values in all.vars)> ## This example by Uwe Ligges <ligges@statistik.uni-dortmund.de>>> temp <- matrix(1:4, 2)> all.vars(temp ~ 3) # OK[1] "temp"> all.vars(temp[1, ] ~ 3) # wrong in 1.2.1[1] "temp">> ## 2001-Feb-22 from David Scott.> ## rank-deficient residuals in a manova model.> gofX.df<-+ structure(list(A = c(0.696706709347165, 0.362357754476673,+ -0.0291995223012888,+ 0.696706709347165, 0.696706709347165, -0.0291995223012888, 0.696706709347165,+ -0.0291995223012888, 0.362357754476673, 0.696706709347165, -0.0291995223012888,+ 0.362357754476673, -0.416146836547142, 0.362357754476673, 0.696706709347165,+ 0.696706709347165, 0.362357754476673, -0.416146836547142, -0.0291995223012888,+ -0.416146836547142, 0.696706709347165, -0.416146836547142, 0.362357754476673,+ -0.0291995223012888), B = c(0.717356090899523, 0.932039085967226,+ 0.999573603041505, 0.717356090899523, 0.717356090899523, 0.999573603041505,+ 0.717356090899523, 0.999573603041505, 0.932039085967226, 0.717356090899523,+ 0.999573603041505, 0.932039085967226, 0.909297426825682, 0.932039085967226,+ 0.717356090899523, 0.717356090899523, 0.932039085967226, 0.909297426825682,+ 0.999573603041505, 0.909297426825682, 0.717356090899523, 0.909297426825682,+ 0.932039085967226, 0.999573603041505), C = c(-0.0291995223012888,+ -0.737393715541246, -0.998294775794753, -0.0291995223012888,+ -0.0291995223012888, -0.998294775794753, -0.0291995223012888,+ -0.998294775794753, -0.737393715541246, -0.0291995223012888,+ -0.998294775794753, -0.737393715541246, -0.653643620863612, -0.737393715541246,+ -0.0291995223012888, -0.0291995223012888, -0.737393715541246,+ -0.653643620863612, -0.998294775794753, -0.653643620863612,+ -0.0291995223012888,+ -0.653643620863612, -0.737393715541246, -0.998294775794753),+ D = c(0.999573603041505, 0.67546318055115, -0.0583741434275801,+ 0.999573603041505, 0.999573603041505, -0.0583741434275801,+ 0.999573603041505, -0.0583741434275801, 0.67546318055115,+ 0.999573603041505, -0.0583741434275801, 0.67546318055115,+ -0.756802495307928, 0.67546318055115, 0.999573603041505,+ 0.999573603041505, 0.67546318055115, -0.756802495307928,+ -0.0583741434275801, -0.756802495307928, 0.999573603041505,+ -0.756802495307928, 0.67546318055115, -0.0583741434275801+ ), groups = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,+ 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3), class = "factor", .Label = c("1",+ "2", "3"))), .Names = c("A", "B", "C", "D", "groups"), row.names = c("1",+ "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",+ "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"+ ), class = "data.frame")>> gofX.manova <- manova(formula = cbind(A, B, C, D) ~ groups, data = gofX.df)> try(summary(gofX.manova))Error in summary.manova(gofX.manova) : residuals have rank 3 < 4> ## should fail with an error message `residuals have rank 3 < 4'>> ## Prior to 1.3.0 dist did not handle missing values, and the> ## internal C code was incorrectly scaling for missing values.> library(mva)> data(trees)> z <- as.matrix(t(trees))> z[1,1] <- z[2,2] <- z[3,3] <- z[2,4] <- NA> dist(z, method="euclidean")Girth HeightHeight 352.4365Volume 123.5503 261.5802> dist(z, method="maximum")Girth HeightHeight 72.7Volume 56.4 63.3> dist(z, method="manhattan")Girth HeightHeight 1954.8821Volume 557.1448 1392.343> dist(z, method="canberra")Girth HeightHeight 21.66477Volume 10.96200 13.63365> detach("package:mva")>> ## F. Tusell 2001-03-07. printing kernels.> library(ts)> kernel("daniell", m=5)Daniell(5)coef[-5] = 0.09091coef[-4] = 0.09091coef[-3] = 0.09091coef[-2] = 0.09091coef[-1] = 0.09091coef[ 0] = 0.09091coef[ 1] = 0.09091coef[ 2] = 0.09091coef[ 3] = 0.09091coef[ 4] = 0.09091coef[ 5] = 0.09091> kernel("modified.daniell", m=5)mDaniell(5)coef[-5] = 0.05coef[-4] = 0.10coef[-3] = 0.10coef[-2] = 0.10coef[-1] = 0.10coef[ 0] = 0.10coef[ 1] = 0.10coef[ 2] = 0.10coef[ 3] = 0.10coef[ 4] = 0.10coef[ 5] = 0.05> kernel("daniell", m=c(3,5,7))unknowncoef[-15] = 0.0008658coef[-14] = 0.0025974coef[-13] = 0.0051948coef[-12] = 0.0086580coef[-11] = 0.0129870coef[-10] = 0.0181818coef[ -9] = 0.0242424coef[ -8] = 0.0303030coef[ -7] = 0.0363636coef[ -6] = 0.0424242coef[ -5] = 0.0484848coef[ -4] = 0.0536797coef[ -3] = 0.0580087coef[ -2] = 0.0614719coef[ -1] = 0.0640693coef[ 0] = 0.0649351coef[ 1] = 0.0640693coef[ 2] = 0.0614719coef[ 3] = 0.0580087coef[ 4] = 0.0536797coef[ 5] = 0.0484848coef[ 6] = 0.0424242coef[ 7] = 0.0363636coef[ 8] = 0.0303030coef[ 9] = 0.0242424coef[ 10] = 0.0181818coef[ 11] = 0.0129870coef[ 12] = 0.0086580coef[ 13] = 0.0051948coef[ 14] = 0.0025974coef[ 15] = 0.0008658> ## fixed by patch from Adrian Trapletti 2001-03-08>> ## Start new year (i.e. line) at Jan:> (tt <- ts(1:10, start = c(1920,7), end = c(1921,4), freq = 12))Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec1920 1 2 3 4 5 61921 7 8 9 10> cbind(tt, tt + 1)tt tt + 1Jul 1920 1 2Aug 1920 2 3Sep 1920 3 4Oct 1920 4 5Nov 1920 5 6Dec 1920 6 7Jan 1921 7 8Feb 1921 8 9Mar 1921 9 10Apr 1921 10 11>>> ## PR 883 (cor(x,y) when is.null(y))> try(cov(rnorm(10), NULL))Error in cov(rnorm(10), NULL) : supply both x and y or a matrix-like x> try(cor(rnorm(10), NULL))Error in cor(rnorm(10), NULL) : supply both x and y or a matrix-like x> ## gave the variance and 1 respectively in 1.2.2.> try(var(NULL))Error in var(NULL) : `x' is empty> try(var(numeric(0)))Error in var(numeric(0)) : `x' is empty> ## gave NA in 1.2.2>>> ## PR 960 (format() of a character matrix converts to vector)> ## example from <John.Peters@tip.csiro.au>> a <- matrix(c("axx","b","c","d","e","f","g","h"), nrow=2)> format(a)[,1] [,2] [,3] [,4][1,] "axx" "c " "e " "g "[2,] "b " "d " "f " "h "> format(a, justify="right")[,1] [,2] [,3] [,4][1,] "axx" " c" " e" " g"[2,] " b" " d" " f" " h"> ## lost dimensions in 1.2.3>>> ## PR 963> svd(rbind(1:7))## $v lost dimensions in 1.2.3$d[1] 11.83216$u[,1][1,] 1$v[,1][1,] 0.08451543[2,] 0.16903085[3,] 0.25354628[4,] 0.33806170[5,] 0.42257713[6,] 0.50709255[7,] 0.59160798>>> ## Make sure on.exit() keeps being evaluated in the proper env [from PD]:> ## A more complete example:> g1 <- function(fitted) { on.exit(remove(fitted)); return(function(foo) foo) }> g2 <- function(fitted) { on.exit(remove(fitted)); function(foo) foo }> f <- function(g) { fitted <- 1; h <- g(fitted); print(fitted)+ ls(envir=environment(h)) }> f(g1)[1] 1character(0)> f(g2)[1] 1character(0)>> f2 <- function()+ {+ g.foo <- g1+ g.bar <- g2+ g <- function(x,...) UseMethod("g")+ fitted <- 1; class(fitted) <- "foo"+ h <- g(fitted); print(fitted); print(ls(envir=environment(h)))+ fitted <- 1; class(fitted) <- "bar"+ h <- g(fitted); print(fitted); print(ls(envir=environment(h)))+ invisible(NULL)+ }> f2()[1] 1attr(,"class")[1] "foo"character(0)[1] 1attr(,"class")[1] "bar"character(0)> ## The first case in f2() is broken in 1.3.0(-patched).>> ## on.exit() consistency check from Luke:> g <- function() as.environment(-1)> f <- function(x) UseMethod("f")> f.foo <- function(x) { on.exit(e <<- g()); NULL }> f.bar <- function(x) { on.exit(e <<- g()); return(NULL) }> f(structure(1,class = "foo"))NULL> ls(env = e)# only "x", i.e. *not* the GlobalEnv[1] "x"> f(structure(1,class = "bar"))NULL> stopifnot("x" == ls(env = e))# as above; wrongly was .GlobalEnv in R 1.3.x>>> ## some tests that R supports logical variables in formulae> ## it coerced them to numeric prior to 1.4.0> ## they should appear like 2-level factors, following S>> oldCon <- options("contrasts")> y <- rnorm(10)> x <- rep(c(TRUE, FALSE), 5)> model.matrix(y ~ x)(Intercept) xTRUE1 1 12 1 03 1 14 1 05 1 16 1 07 1 18 1 09 1 110 1 0attr(,"assign")[1] 0 1attr(,"contrasts")attr(,"contrasts")$x[1] "contr.treatment"> lm(y ~ x)Call:lm(formula = y ~ x)Coefficients:(Intercept) xTRUE0.1230 0.3170> DF <- data.frame(x, y)> lm(y ~ x, data=DF)Call:lm(formula = y ~ x, data = DF)Coefficients:(Intercept) xTRUE0.1230 0.3170> options(contrasts=c("contr.helmert", "contr.poly"))> model.matrix(y ~ x)(Intercept) x11 1 12 1 -13 1 14 1 -15 1 16 1 -17 1 18 1 -19 1 110 1 -1attr(,"assign")[1] 0 1attr(,"contrasts")attr(,"contrasts")$x[1] "contr.helmert"> lm(y ~ x, data=DF)Call:lm(formula = y ~ x, data = DF)Coefficients:(Intercept) x10.2814 0.1585> z <- 1:10> lm(y ~ x*z)Call:lm(formula = y ~ x * z)Coefficients:(Intercept) x1 z x1:z0.49064 -0.68273 -0.02433 0.15074> lm(y ~ x*z - 1)Call:lm(formula = y ~ x * z - 1)Coefficients:xFALSE xTRUE z x1:z1.17337 -0.19209 -0.02433 0.15074> options(oldCon)>> ## diffinv, Adrian Trapletti, 2001-08-27> library(ts)> x <- ts(1:10)> diffinv(diff(x),xi=x[1])Time Series:Start = 1End = 10Frequency = 1[1] 1 2 3 4 5 6 7 8 9 10> diffinv(diff(x,lag=1,differences=2),lag=1,differences=2,xi=x[1:2])Time Series:Start = 1End = 10Frequency = 1[1] 1 2 3 4 5 6 7 8 9 10> ## last had wrong start and end> detach("package:ts")>> ## PR#1072 (Reading Inf and NaN values)> as.numeric(as.character(NaN))[1] NaN> as.numeric(as.character(Inf))[1] Inf> ## were NA on Windows at least under 1.3.0.>> ## PR#1092 (rowsum dimnames)> rowsum(matrix(1:12, 3,4), c("Y","X","Y"))[,1] [,2] [,3] [,4]X 2 5 8 11Y 4 10 16 22> ## rownames were 1,2 in <= 1.3.1.>> ## PR#1115 (saving strings with ascii=TRUE)> x <- y <- unlist(as.list(+ parse(text=paste("\"\\",+ as.character(structure(0:255,class="octmode")),+ "\"",sep=""))))> save(x, ascii=T, file=(fn <- tempfile()))> load(fn)> all(x==y)[1] TRUE> unlink(fn)> ## 1.3.1 had trouble with \>>> ## Some tests of sink() and connections()> ## capture all the output to a file.> zz <- file("all.Rout", open="wt")> sink(zz)> sink(zz, type="message")> try(log("a"))> ## back to the console> sink(type="message")> sink()> try(log("a"))Error in log(x) : Non-numeric argument to mathematical function>> ## capture all the output to a file.> zz <- file("all.Rout", open="wt")> sink(zz)> sink(zz, type="message")> try(log("a"))>> ## bail out> closeAllConnections()> (foo <- showConnections())description class mode text isopen can read can write> stopifnot(nrow(foo) == 0)> try(log("a"))Error in log(x) : Non-numeric argument to mathematical function> unlink("all.Rout")> ## many of these were untested before 1.4.0.>>> ## test mean() works on logical but not factor> x <- c(TRUE, FALSE, TRUE, TRUE)> mean(x)[1] 0.75> mean(as.factor(x))[1] NAWarning message:argument is not numeric or logical: returning NA in: mean.default(as.factor(x))> ## last had confusing error message in 1.3.1.>>> ## Kurt Hornik 2001-Nov-13> z <- table(x = 1:2, y = 1:2)> z - 1yx 1 21 0 -12 -1 0> unclass(z - 1)yx 1 21 0 -12 -1 0> ## lost object bit prior to 1.4.0, so printed class attribute.>>> ## PR#1226 (predict.mlm ignored newdata)> 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)> data <- data.frame(weight, group)> fit <- lm(cbind(w=weight, w2=weight^2) ~ group, data=data)> predict(fit, newdata=data[1:2, ])w w21 5.032 25.627022 5.032 25.62702> ## was 20 rows in R <= 1.4.0>>> ## Chong Gu 2002-Feb-8: `.' not expanded in drop1> data(HairEyeColor)> lab <- dimnames(HairEyeColor)> HairEye <- cbind(expand.grid(Hair=lab$Hair, Eye=lab$Eye, Sex=lab$Sex),+ Fr=as.vector(HairEyeColor))> HairEye.fit <- glm(Fr ~ . ^2, poisson, HairEye)> drop1(HairEye.fit)Single term deletionsModel:Fr ~ .^2Df Deviance AIC<none> 8.19 192.94Hair:Eye 9 162.21 328.96Hair:Sex 3 22.03 200.78Eye:Sex 3 23.08 201.83> ## broken around 1.2.1 it seems.>>> ## PR#1329 (subscripting matrix lists)> m <- list(a1=1:3, a2=4:6, a3=pi, a4=c("a","b","c"))> dim(m) <- c(2,2)> m[,1] [,2][1,] Integer,3 3.141593[2,] Integer,3 Character,3> m[,2][[1]][1] 3.141593[[2]][1] "a" "b" "c"> m[2,2][[1]][1] "a" "b" "c"> ## 1.4.1 returned null components: the case was missing from a switch.>> m <- list(a1=1:3, a2=4:6, a3=pi, a4=c("a","b","c"))> matrix(m, 2, 2)[,1] [,2][1,] Integer,3 3.141593[2,] Integer,3 Character,3> ## 1.4.1 gave `Unimplemented feature in copyVector'>> x <- vector("list",6)> dim(x) <- c(2,3)> x[1,2] <- list(letters[10:11])> x[,1] [,2] [,3][1,] NULL Character,2 NULL[2,] NULL NULL NULL> ## 1.4.1 gave `incompatible types in subset assignment'>>> ## printing of matrix lists> m <- list(as.integer(1), pi, 3+5i, "testit", TRUE, factor("foo"))> dim(m) <- c(1, 6)> m[,1] [,2] [,3] [,4] [,5] [,6][1,] 1 3.141593 3+5i "testit" TRUE factor,1> ## prior to 1.5.0 had quotes for 2D case (but not kD, k > 2),> ## gave "numeric,1" etc, (even "numeric,1" for integers and factors)>>> ## ensure RNG is unaltered.> for(type in c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper",+ "Mersenne-Twister", "Knuth-TAOCP", "Knuth-TAOCP-2002"))+ {+ set.seed(123, type)+ print(RNGkind())+ runif(100); print(runif(4))+ set.seed(1000, type)+ runif(100); print(runif(4))+ set.seed(77, type)+ runif(100); print(runif(4))+ }[1] "Wichmann-Hill" "Kinderman-Ramage"[1] 0.8308841 0.4640221 0.9460082 0.8764644[1] 0.12909876 0.07294851 0.45594560 0.68884911[1] 0.4062450 0.7188432 0.6241738 0.2511611[1] "Marsaglia-Multicarry" "Kinderman-Ramage"[1] 0.3479705 0.9469351 0.2489207 0.7329251[1] 0.5041512 0.3617873 0.1469184 0.3798119[1] 0.14388128 0.04196294 0.36214015 0.86053575[1] "Super-Duper" "Kinderman-Ramage"[1] 0.2722510 0.9230240 0.3971743 0.8284474[1] 0.5706241 0.1806023 0.9633860 0.8434444[1] 0.09356585 0.41081124 0.38635627 0.72993396[1] "Mersenne-Twister" "Kinderman-Ramage"[1] 0.5999890 0.3328235 0.4886130 0.9544738[1] 0.5993679 0.4516818 0.1368254 0.7261788[1] 0.09594961 0.31235651 0.81244335 0.72330846[1] "Knuth-TAOCP" "Kinderman-Ramage"[1] 0.9445502 0.3366297 0.6296881 0.5914161[1] 0.9213954 0.5468138 0.8817100 0.4442237[1] 0.8016962 0.9226080 0.1473484 0.8827707[1] "Knuth-TAOCP-2002" "Kinderman-Ramage"[1] 0.9303634 0.2812239 0.1085806 0.8053228[1] 0.2916627 0.9085017 0.7958965 0.1980655[1] 0.05247575 0.28290867 0.20930324 0.16794887> RNGkind(normal.kind = "Kinderman-Ramage")> set.seed(123)> RNGkind()[1] "Knuth-TAOCP-2002" "Kinderman-Ramage"> rnorm(4)[1] -1.9699090 -2.2429340 0.5339321 0.2097153> RNGkind(normal.kind = "Ahrens-Dieter")> set.seed(123)> RNGkind()[1] "Knuth-TAOCP-2002" "Ahrens-Dieter"> rnorm(4)[1] 0.06267229 0.12421568 -1.86653499 -0.14535921> RNGkind(normal.kind = "Box-Muller")> set.seed(123)> RNGkind()[1] "Knuth-TAOCP-2002" "Box-Muller"> rnorm(4)[1] 2.26160990 0.59010303 0.30176045 -0.01346139> set.seed(123)> runif(4)[1] 0.04062130 0.06511825 0.99290488 0.95540467> set.seed(123, "default")> runif(4)[1] 0.1200427 0.1991600 0.7292821 0.8115922> ## last set.seed failed < 1.5.0.>>> ## merging, ggrothendieck@yifan.net, 2002-03-16> d.df <- data.frame(x = 1:3, y = c("A","D","E"), z = c(6,9,10))> merge(d.df[1,], d.df)x y z1 1 A 6> ## 1.4.1 got confused by inconsistencies in as.character>>> ## PR#1394 (levels<-.factor)> f <- factor(c("a","b"))> levels(f) <- list(C="C", A="a", B="b")> f[1] A BLevels: C A B> ## was [1] C A; Levels: C A in 1.4.1>>> ## PR#1408 Inconsistencies in sum()> x <- as.integer(2^30)> sum(x, x) # did not warn in 1.4.1[1] NAWarning message:Integer overflow in sum(.); use sum(as.numeric(.))> sum(c(x, x)) # did warn[1] NAWarning message:Integer overflow in sum(.); use sum(as.numeric(.))> (z <- sum(x, x, 0.0)) # was NA in 1.4.1[1] 2147483648> typeof(z)[1] "double">>> ## NA levels in factors> (x <- factor(c("a", "NA", "b"), exclude=NULL))[1] a NA bLevels: NA a b> ## 1.4.1 had wrong order for levels> is.na(x)[3] <- TRUE> x[1] a NA <NA>Levels: NA a b> ## missing entry prints as <NA>>>> ## printing/formatting NA strings> (x <- c("a", "NA", NA, "b"))[1] "a" "NA" NA "b"> print(x, quote = FALSE)[1] a NA <NA> b> paste(x)[1] "a" "NA" "NA" "b"> format(x)[1] "a " "NA" "NA" "b "> format(x, justify = "right")[1] " a" "NA" "NA" " b"> format(x, justify = "none")[1] "a" "NA" NA "b"> ## not ideal.>>> ## print.ts problems ggrothendieck@yifan.net on R-help, 2002-04-01> x <- 1:20> tt1 <- ts(x,start=c(1960,2), freq=12)> tt2 <- ts(10+x,start=c(1960,2), freq=12)> cbind(tt1, tt2)tt1 tt2Feb 1960 1 11Mar 1960 2 12Apr 1960 3 13May 1960 4 14Jun 1960 5 15Jul 1960 6 16Aug 1960 7 17Sep 1960 8 18Oct 1960 9 19Nov 1960 10 20Dec 1960 11 21Jan 1960 12 22Feb 1961 13 23Mar 1961 14 24Apr 1961 15 25May 1961 16 26Jun 1961 17 27Jul 1961 18 28Aug 1961 19 29Sep 1961 20 30> ## 1.4.1 had `Jan 1961' as `NA 1961'>> ## glm boundary bugs (related to PR#1331)> x <- c(0.35, 0.64, 0.12, 1.66, 1.52, 0.23, -1.99, 0.42, 1.86, -0.02,+ -1.64, -0.46, -0.1, 1.25, 0.37, 0.31, 1.11, 1.65, 0.33, 0.89,+ -0.25, -0.87, -0.22, 0.71, -2.26, 0.77, -0.05, 0.32, -0.64, 0.39,+ 0.19, -1.62, 0.37, 0.02, 0.97, -2.62, 0.15, 1.55, -1.41, -2.35,+ -0.43, 0.57, -0.66, -0.08, 0.02, 0.24, -0.33, -0.03, -1.13, 0.32,+ 1.55, 2.13, -0.1, -0.32, -0.67, 1.44, 0.04, -1.1, -0.95, -0.19,+ -0.68, -0.43, -0.84, 0.69, -0.65, 0.71, 0.19, 0.45, 0.45, -1.19,+ 1.3, 0.14, -0.36, -0.5, -0.47, -1.31, -1.02, 1.17, 1.51, -0.33,+ -0.01, -0.59, -0.28, -0.18, -1.07, 0.66, -0.71, 1.88, -0.14,+ -0.19, 0.84, 0.44, 1.33, -0.2, -0.45, 1.46, 1, -1.02, 0.68, 0.84)> y <- c(1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,+ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,+ 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,+ 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,+ 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0)> try(glm(y ~ x, family = poisson(identity)))Error: no valid set of coefficients has been found:please supply starting valuesIn addition: Warning message:NaNs produced in: log(x)> ## failed because start = NULL in 1.4.1> ## now gives useful error message> glm(y ~ x, family = poisson(identity), start = c(1,0))Call: glm(formula = y ~ x, family = poisson(identity), start = c(1, 0))Coefficients:(Intercept) x0.5113 0.1709Degrees of Freedom: 99 Total (i.e. Null); 98 ResidualNull Deviance: 68.01Residual Deviance: 60.67 AIC: 168.7Warning messages:1: Step size truncated: out of bounds2: Step size truncated: out of bounds> ## step reduction failed in 1.4.1> set.seed(123)> y <- rpois(100, pmax(3*x, 0))> glm(y ~ x, family = poisson(identity), start = c(1,0))Call: glm(formula = y ~ x, family = poisson(identity), start = c(1, 0))Coefficients:(Intercept) x1.1078 0.4228Degrees of Freedom: 99 Total (i.e. Null); 98 ResidualNull Deviance: 317.2Residual Deviance: 230.2 AIC: 346.4There were 12 warnings (use warnings() to see them)> warnings()Warning messages:1: Step size truncated: out of bounds2: Step size truncated: out of bounds3: Step size truncated: out of bounds4: Step size truncated: out of bounds5: Step size truncated: out of bounds6: Step size truncated: out of bounds7: Step size truncated: out of bounds8: Step size truncated: out of bounds9: Step size truncated: out of bounds10: Step size truncated: out of bounds11: Algorithm did not converge in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, ...12: Algorithm stopped at boundary value in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, ...>>> ## extending char arrrays> x <- y <- LETTERS[1:2]> x[5] <- "C"> length(y) <- 5> x[1] "A" "B" NA NA "C"> y[1] "A" "B" NA NA NA> ## x was filled with "", y with NA in 1.5.0>>> ## formula with no intercept, 2002-07-22> oldcon <- options(contrasts = c("contr.helmert", "contr.poly"))> U <- gl(3, 6, 18, labels=letters[1:3])> V <- gl(3, 2, 18, labels=letters[1:3])> A <- rep(c(0, 1), 9)> B <- rep(c(1, 0), 9)> set.seed(1); y <- rnorm(18)> terms(y ~ A:U + A:V - 1)y ~ A:U + A:V - 1attr(,"variables")list(y, A, U, V)attr(,"factors")A:U A:Vy 0 0A 2 2U 2 0V 0 1attr(,"term.labels")[1] "A:U" "A:V"attr(,"order")[1] 2 2attr(,"intercept")[1] 0attr(,"response")[1] 1attr(,".Environment")<environment: R_GlobalEnv>> lm(y ~ A:U + A:V - 1)$coef # 1.5.1 used dummies coding for VA:Ua A:Ub A:Uc A:V1 A:V20.25303884 -0.21875499 -0.71708528 -0.61467193 -0.09030436> lm(y ~ (A + B) : (U + V) - 1) # 1.5.1 used dummies coding for A:V but not B:VCall:lm(formula = y ~ (A + B):(U + V) - 1)Coefficients:A:Ua A:Ub A:Uc A:V1 A:V2 B:Ua B:Ub B:Uc0.2530 -0.2188 -0.7171 -0.6147 -0.0903 1.7428 0.0613 0.7649B:V1 B:V2-0.4420 0.5388> options(oldcon)> ## 1.5.1 miscomputed the first factor in the formula.>>> ## quantile extremes, MM 13 Apr 2000 and PR#1852> for(k in 0:5)+ print(quantile(c(rep(-Inf,k+1), 0:k, rep(Inf, k)), pr=seq(0,1, .1)))0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%-Inf NaN NaN NaN NaN NaN NaN NaN NaN NaN 00% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%-Inf -Inf -Inf NaN NaN 0.0 0.4 0.8 Inf Inf Inf0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%-Inf -Inf -Inf NaN NaN 0.5 1.2 1.9 Inf Inf Inf0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%-Inf -Inf -Inf -Inf 0 1 2 3 Inf Inf Inf0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%-Inf -Inf -Inf -Inf 0.2 1.5 2.8 Inf Inf Inf Inf0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%-Inf -Inf -Inf -Inf 0.4 2.0 3.6 Inf Inf Inf Inf> x <- c(-Inf, -Inf, Inf, Inf)> median(x)[1] NaN> quantile(x)0% 25% 50% 75% 100%-Inf -Inf NaN Inf Inf> ## 1.5.1 had -Inf not NaN in several places>>> ## NAs in matrix dimnames> z <- matrix(1:9, 3, 3)> dimnames(z) <- list(c("x", "y", NA), c(1, NA, 3))> z1 <NA> 3x 1 4 7y 2 5 8<NA> 3 6 9> ## NAs in dimnames misaligned when printing in 1.5.1>>> ## weighted aov (PR#1930)> r <- c(10,23,23,26,17,5,53,55,32,46,10,8,10,8,23,0,3,22,15,32,3)> n <- c(39,62,81,51,39,6,74,72,51,79,13,16,30,28,45,4,12,41,30,51,7)> trt <- factor(rep(1:4,c(5,6,5,5)))> Y <- r/n> z <- aov(Y ~ trt, weights=n)> ## 1.5.1 gave unweighted RSS>>> ## rbind (PR#2266)> test <- as.data.frame(matrix(1:25, 5, 5))> test1 <- matrix(-(1:10), 2, 5)> rbind(test, test1)V1 V2 V3 V4 V51 1 6 11 16 212 2 7 12 17 223 3 8 13 18 234 4 9 14 19 245 5 10 15 20 2511 -1 -3 -5 -7 -922 -2 -4 -6 -8 -10> rbind(test1, test)V1 V2 V3 V4 V51 -1 -3 -5 -7 -92 -2 -4 -6 -8 -1011 1 6 11 16 2122 2 7 12 17 223 3 8 13 18 234 4 9 14 19 245 5 10 15 20 25> ## 1.6.1 treated matrix as a vector.>>