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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] 10 attr(,"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 10 attr(,"na.action") [1] 3 attr(,"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] 1 attr(,"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 $A$formula Y ~ X $A$subset [1] TRUE $B $B$formula Y ~ 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 dist Min. : 1.00 Min. :5.000 117 : 5 Min. : 0.50 1st Qu.: 9.00 1st Qu.:5.300 1028 : 4 1st Qu.: 11.32 Median :18.00 Median :6.100 113 : 4 Median : 23.40 Mean :14.74 Mean :6.084 112 : 3 Mean : 45.60 3rd Qu.:20.00 3rd Qu.:6.600 135 : 3 3rd Qu.: 47.55 Max. :23.00 Max. :7.700 (Other):147 Max. :370.00 NA's : 16 accel Min. :0.00300 1st Qu.:0.04425 Median :0.11300 Mean :0.15422 3rd Qu.:0.21925 Max. :0.81000 > summary(attenu, digits = 5) event mag station dist Min. : 1.000 Min. :5.0000 117 : 5 Min. : 0.500 1st Qu.: 9.000 1st Qu.:5.3000 1028 : 4 1st Qu.: 11.325 Median :18.000 Median :6.1000 113 : 4 Median : 23.400 Mean :14.742 Mean :6.0841 112 : 3 Mean : 45.603 3rd Qu.:20.000 3rd Qu.:6.6000 135 : 3 3rd Qu.: 47.550 Max. :23.000 Max. :7.7000 (Other):147 Max. :370.000 NA's : 16 accel Min. :0.00300 1st Qu.:0.04425 Median :0.11300 Mean :0.15422 3rd Qu.:0.21925 Max. :0.81000 > summary(data.matrix(attenu), digits = 5)# the same for matrix event mag station dist Min. : 1.000 Min. :5.0000 Min. : 1.000 Min. : 0.500 1st Qu.: 9.000 1st Qu.:5.3000 1st Qu.: 24.250 1st Qu.: 11.325 Median :18.000 Median :6.1000 Median : 56.500 Median : 23.400 Mean :14.742 Mean :6.0841 Mean : 56.928 Mean : 45.603 3rd Qu.:20.000 3rd Qu.:6.6000 3rd Qu.: 86.750 3rd Qu.: 47.550 Max. :23.000 Max. :7.7000 Max. :117.000 Max. :370.000 NA's : 16.000 accel Min. :0.00300 1st Qu.:0.04425 Median :0.11300 Mean :0.15422 3rd Qu.:0.21925 Max. :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)) x Min. : 0.000 1st Qu.: 0.050 Median : 0.550 Mean : 909.423 3rd Qu.: 0.675 Max. :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.m0 Call: glm(formula = cbind(X, M) ~ 1, family = binomial, data = detg1) Coefficients: (Intercept) 0.01587 Degrees of Freedom: 11 Total (i.e. Null); 11 Residual Null Deviance: 32.83 Residual Deviance: 32.83 AIC: 92.52 > step(detg1.m0,scope=list(upper=~M.user*Temp*Soft)) Start: AIC= 92.52 cbind(X, M) ~ 1 Df Deviance AIC + M.user 1 12.244 73.942 + Temp 1 28.464 90.162 32.826 92.524 + Soft 2 32.430 96.128 Step: AIC= 73.94 cbind(X, M) ~ M.user Df Deviance AIC + Temp 1 8.444 72.142 12.244 73.942 + Soft 2 11.967 77.665 - M.user 1 32.826 92.524 Step: AIC= 72.14 cbind(X, M) ~ M.user + Temp Df Deviance AIC + M.user:Temp 1 5.656 71.354 8.444 72.142 - Temp 1 12.244 73.942 + Soft 2 8.228 75.926 - M.user 1 28.464 90.162 Step: AIC= 71.35 cbind(X, M) ~ M.user + Temp + M.user:Temp Df Deviance AIC 5.656 71.354 - M.user:Temp 1 8.444 72.142 + Soft 2 5.495 75.193 Call: glm(formula = cbind(X, M) ~ M.user + Temp + M.user:Temp, family = binomial, data = detg1) Coefficients: (Intercept) M.userY TempLow M.userY:TempLow 0.26236 -0.85183 0.04411 0.44427 Degrees of Freedom: 11 Total (i.e. Null); 8 Residual Null Deviance: 32.83 Residual Deviance: 5.656 AIC: 71.35 > > ## PR 829 (empty values in all.vars) > ## This example by Uwe Ligges > > 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 Height Height 352.4365 Volume 123.5503 261.5802 > dist(z, method="maximum") Girth Height Height 72.7 Volume 56.4 63.3 > dist(z, method="manhattan") Girth Height Height 1954.8821 Volume 557.1448 1392.343 > dist(z, method="canberra") Girth Height Height 21.66477 Volume 10.96200 13.63365 > detach("package:mva") > > ## F. Tusell 2001-03-07. printing kernels. > library(ts) > kernel("daniell", m=5) Daniell(5) coef[-5] = 0.09091 coef[-4] = 0.09091 coef[-3] = 0.09091 coef[-2] = 0.09091 coef[-1] = 0.09091 coef[ 0] = 0.09091 coef[ 1] = 0.09091 coef[ 2] = 0.09091 coef[ 3] = 0.09091 coef[ 4] = 0.09091 coef[ 5] = 0.09091 > kernel("modified.daniell", m=5) mDaniell(5) coef[-5] = 0.05 coef[-4] = 0.10 coef[-3] = 0.10 coef[-2] = 0.10 coef[-1] = 0.10 coef[ 0] = 0.10 coef[ 1] = 0.10 coef[ 2] = 0.10 coef[ 3] = 0.10 coef[ 4] = 0.10 coef[ 5] = 0.05 > kernel("daniell", m=c(3,5,7)) unknown coef[-15] = 0.0008658 coef[-14] = 0.0025974 coef[-13] = 0.0051948 coef[-12] = 0.0086580 coef[-11] = 0.0129870 coef[-10] = 0.0181818 coef[ -9] = 0.0242424 coef[ -8] = 0.0303030 coef[ -7] = 0.0363636 coef[ -6] = 0.0424242 coef[ -5] = 0.0484848 coef[ -4] = 0.0536797 coef[ -3] = 0.0580087 coef[ -2] = 0.0614719 coef[ -1] = 0.0640693 coef[ 0] = 0.0649351 coef[ 1] = 0.0640693 coef[ 2] = 0.0614719 coef[ 3] = 0.0580087 coef[ 4] = 0.0536797 coef[ 5] = 0.0484848 coef[ 6] = 0.0424242 coef[ 7] = 0.0363636 coef[ 8] = 0.0303030 coef[ 9] = 0.0242424 coef[ 10] = 0.0181818 coef[ 11] = 0.0129870 coef[ 12] = 0.0086580 coef[ 13] = 0.0051948 coef[ 14] = 0.0025974 coef[ 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 Dec 1920 1 2 3 4 5 6 1921 7 8 9 10 > cbind(tt, tt + 1) tt tt + 1 Jul 1920 1 2 Aug 1920 2 3 Sep 1920 3 4 Oct 1920 4 5 Nov 1920 5 6 Dec 1920 6 7 Jan 1921 7 8 Feb 1921 8 9 Mar 1921 9 10 Apr 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 > 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 $v [,1] [1,] 0.08451543 [2,] 0.16903085 [3,] 0.25354628 [4,] 0.33806170 [5,] 0.42257713 [6,] 0.50709255 [7,] 0.59160798 > > > ## Make sure on.exit() keeps being evaluated in the proper env [from PD]: > ## A more complete example: > g1 <- function(fitted) { on.exit(remove(fitted)); return(function(foo) foo) } > g2 <- function(fitted) { on.exit(remove(fitted)); function(foo) foo } > f <- function(g) { fitted <- 1; h <- g(fitted); print(fitted) + ls(envir=environment(h)) } > f(g1) [1] 1 character(0) > f(g2) [1] 1 character(0) > > f2 <- function() + { + g.foo <- g1 + g.bar <- g2 + g <- function(x,...) UseMethod("g") + fitted <- 1; class(fitted) <- "foo" + h <- g(fitted); print(fitted); print(ls(envir=environment(h))) + fitted <- 1; class(fitted) <- "bar" + h <- g(fitted); print(fitted); print(ls(envir=environment(h))) + invisible(NULL) + } > f2() [1] 1 attr(,"class") [1] "foo" character(0) [1] 1 attr(,"class") [1] "bar" character(0) > ## The first case in f2() is broken in 1.3.0(-patched). > > ## on.exit() consistency check from Luke: > g <- function() as.environment(-1) > f <- function(x) UseMethod("f") > f.foo <- function(x) { on.exit(e <<- g()); NULL } > f.bar <- function(x) { on.exit(e <<- g()); return(NULL) } > f(structure(1,class = "foo")) NULL > ls(env = e)# only "x", i.e. *not* the GlobalEnv [1] "x" > f(structure(1,class = "bar")) NULL > stopifnot("x" == ls(env = e))# as above; wrongly was .GlobalEnv in R 1.3.x > > > ## some tests that R supports logical variables in formulae > ## it coerced them to numeric prior to 1.4.0 > ## they should appear like 2-level factors, following S > > oldCon <- options("contrasts") > y <- rnorm(10) > x <- rep(c(TRUE, FALSE), 5) > model.matrix(y ~ x) (Intercept) xTRUE 1 1 1 2 1 0 3 1 1 4 1 0 5 1 1 6 1 0 7 1 1 8 1 0 9 1 1 10 1 0 attr(,"assign") [1] 0 1 attr(,"contrasts") attr(,"contrasts")$x [1] "contr.treatment" > lm(y ~ x) Call: lm(formula = y ~ x) Coefficients: (Intercept) xTRUE 0.1230 0.3170 > DF <- data.frame(x, y) > lm(y ~ x, data=DF) Call: lm(formula = y ~ x, data = DF) Coefficients: (Intercept) xTRUE 0.1230 0.3170 > options(contrasts=c("contr.helmert", "contr.poly")) > model.matrix(y ~ x) (Intercept) x1 1 1 1 2 1 -1 3 1 1 4 1 -1 5 1 1 6 1 -1 7 1 1 8 1 -1 9 1 1 10 1 -1 attr(,"assign") [1] 0 1 attr(,"contrasts") attr(,"contrasts")$x [1] "contr.helmert" > lm(y ~ x, data=DF) Call: lm(formula = y ~ x, data = DF) Coefficients: (Intercept) x1 0.2814 0.1585 > z <- 1:10 > lm(y ~ x*z) Call: lm(formula = y ~ x * z) Coefficients: (Intercept) x1 z x1:z 0.49064 -0.68273 -0.02433 0.15074 > lm(y ~ x*z - 1) Call: lm(formula = y ~ x * z - 1) Coefficients: xFALSE xTRUE z x1:z 1.17337 -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 = 1 End = 10 Frequency = 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 = 1 End = 10 Frequency = 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 11 Y 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] NA Warning 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 - 1 y x 1 2 1 0 -1 2 -1 0 > unclass(z - 1) y x 1 2 1 0 -1 2 -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 w2 1 5.032 25.62702 2 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 deletions Model: Fr ~ .^2 Df Deviance AIC 8.19 192.94 Hair:Eye 9 162.21 328.96 Hair:Sex 3 22.03 200.78 Eye: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 z 1 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 B Levels: 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] NA Warning message: Integer overflow in sum(.); use sum(as.numeric(.)) > sum(c(x, x)) # did warn [1] NA Warning 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 b Levels: NA a b > ## 1.4.1 had wrong order for levels > is.na(x)[3] <- TRUE > x [1] a NA Levels: NA a b > ## missing entry prints as > > > ## printing/formatting NA strings > (x <- c("a", "NA", NA, "b")) [1] "a" "NA" NA "b" > print(x, quote = FALSE) [1] a 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 tt2 Feb 1960 1 11 Mar 1960 2 12 Apr 1960 3 13 May 1960 4 14 Jun 1960 5 15 Jul 1960 6 16 Aug 1960 7 17 Sep 1960 8 18 Oct 1960 9 19 Nov 1960 10 20 Dec 1960 11 21 Jan 1960 12 22 Feb 1961 13 23 Mar 1961 14 24 Apr 1961 15 25 May 1961 16 26 Jun 1961 17 27 Jul 1961 18 28 Aug 1961 19 29 Sep 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 values In 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) x 0.5113 0.1709 Degrees of Freedom: 99 Total (i.e. Null); 98 Residual Null Deviance: 68.01 Residual Deviance: 60.67 AIC: 168.7 Warning messages: 1: Step size truncated: out of bounds 2: 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) x 1.1078 0.4228 Degrees of Freedom: 99 Total (i.e. Null); 98 Residual Null Deviance: 317.2 Residual Deviance: 230.2 AIC: 346.4 There were 12 warnings (use warnings() to see them) > warnings() Warning messages: 1: Step size truncated: out of bounds 2: Step size truncated: out of bounds 3: Step size truncated: out of bounds 4: Step size truncated: out of bounds 5: Step size truncated: out of bounds 6: Step size truncated: out of bounds 7: Step size truncated: out of bounds 8: Step size truncated: out of bounds 9: Step size truncated: out of bounds 10: Step size truncated: out of bounds 11: 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, ... >