## Regression tests for which the printed output is the issue ### _and_ must work (no Recommended packages, please) postscript("reg-tests-2.ps") RNGversion("1.6.2") ### moved from various .Rd files ## abbreviate data(state) for(m in 1:5) { cat("\n",m,":\n") print(as.vector(abbreviate(state.name, minl=m))) } ## apply x <- cbind(x1 = 3, x2 = c(4:1, 2:5)) dimnames(x)[[1]] <- letters[1:8] apply(x, 2, summary) # 6 x n matrix apply(x, 1, quantile)# 5 x n matrix d.arr <- 2:5 arr <- array(1:prod(d.arr), d.arr, list(NULL,letters[1:d.arr[2]],NULL,paste("V",4+1:d.arr[4],sep=""))) aa <- array(1:20,c(2,2,5)) str(apply(aa[FALSE,,,drop=FALSE], 1, dim))# empty integer, `incorrect' dim. stopifnot( apply(arr, 1:2, sum) == t(apply(arr, 2:1, sum)), aa == apply(aa,2:3,function(x) x), all.equal(apply(apply(aa,2:3, sum),2,sum), 10+16*0:4, tol=4*.Machine$double.eps) ) marg <- list(1:2, 2:3, c(2,4), c(1,3), 2:4, 1:3, 1:4) for(m in marg) print(apply(arr, print(m), sum)) for(m in marg) ## 75% of the time here was spent on the names print(dim(apply(arr, print(m), quantile, names=FALSE)) == c(5,d.arr[m])) ## Bessel nus <- c(0:5,10,20) x0 <- 2^(-20:10) plot(x0,x0, log='xy', ylab="", ylim=c(.1,1e60),type='n', main = "Bessel Functions -Y_nu(x) near 0\n log - log scale") for(nu in sort(c(nus,nus+.5))) lines(x0, -besselY(x0,nu=nu), col = nu+2) legend(3,1e50, leg=paste("nu=", paste(nus,nus+.5, sep=",")), col=nus+2, lwd=1) x <- seq(3,500);yl <- c(-.3, .2) plot(x,x, ylim = yl, ylab="",type='n', main = "Bessel Functions Y_nu(x)") for(nu in nus){xx <- x[x > .6*nu]; lines(xx,besselY(xx,nu=nu), col = nu+2)} legend(300,-.08, leg=paste("nu=",nus), col = nus+2, lwd=1) x <- seq(10,50000,by=10);yl <- c(-.1, .1) plot(x,x, ylim = yl, ylab="",type='n', main = "Bessel Functions Y_nu(x)") for(nu in nus){xx <- x[x > .6*nu]; lines(xx,besselY(xx,nu=nu), col = nu+2)} summary(bY <- besselY(2,nu = nu <- seq(0,100,len=501))) which(bY >= 0) summary(bY <- besselY(2,nu = nu <- seq(3,300,len=51))) summary(bI <- besselI(x = x <- 10:700, 1)) ## end of moved from Bessel.Rd ## data.frame set.seed(123) L3 <- LETTERS[1:3] str(d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, repl=TRUE))) (d0 <- d[, FALSE]) # NULL dataframe with 10 rows (d.0 <- d[FALSE, ]) # <0 rows> dataframe (3 cols) (d00 <- d0[FALSE,]) # NULL dataframe with 0 rows (d000 <- data.frame()) #but not quite the same as d00: !identical(d00, d000) dput(d00) dput(d000) stopifnot(identical(d, cbind(d, d0)), identical(d, cbind(d0, d)), identical(d, rbind(d,d.0)), identical(d, rbind(d.0,d)), identical(d, rbind(d00,d)), identical(d, rbind(d,d00)), TRUE ) ## Comments: failed before ver. 1.4.0 ## diag diag(array(1:4, dim=5)) ## test behaviour with 0 rows or columns diag(0) z <- matrix(0, 0, 4) diag(z) diag(z) <- numeric(0) z ## end of moved from diag.Rd ## format ## handling of quotes zz <- data.frame(a=I("abc"), b=I("def\"gh")) format(zz) ## " (E fontification) ## printing more than 16 is platform-dependent for(i in c(1:5,10,15,16)) cat(i,":\t",format(pi,digits=i),"\n") p <- c(47,13,2,.1,.023,.0045, 1e-100)/1000 format.pval(p) format.pval(p / 0.9) format.pval(p / 0.9, dig=3) ## end of moved from format.Rd ## is.finite x <- c(100,-1e-13,Inf,-Inf, NaN, pi, NA) x # 1.000000 -3.000000 Inf -Inf NA 3.141593 NA names(x) <- formatC(x, dig=3) is.finite(x) ##- 100 -1e-13 Inf -Inf NaN 3.14 NA ##- T T . . . T . is.na(x) ##- 100 -1e-13 Inf -Inf NaN 3.14 NA ##- . . . . T . T which(is.na(x) & !is.nan(x))# only 'NA': 7 is.na(x) | is.finite(x) ##- 100 -1e-13 Inf -Inf NaN 3.14 NA ##- T T . . T T T is.infinite(x) ##- 100 -1e-13 Inf -Inf NaN 3.14 NA ##- . . T T . . . ##-- either finite or infinite or NA: all(is.na(x) != is.finite(x) | is.infinite(x)) # TRUE all(is.nan(x) != is.finite(x) | is.infinite(x)) # FALSE: have 'real' NA ##--- Integer (ix <- structure(as.integer(x),names= names(x))) ##- 100 -1e-13 Inf -Inf NaN 3.14 NA ##- 100 0 NA NA NA 3 NA all(is.na(ix) != is.finite(ix) | is.infinite(ix)) # TRUE (still) storage.mode(ii <- -3:5) storage.mode(zm <- outer(ii,ii, FUN="*"))# integer storage.mode(zd <- outer(ii,ii, FUN="/"))# double range(zd, na.rm=TRUE)# -Inf Inf zd[,ii==0] (storage.mode(print(1:1 / 0:0)))# Inf "double" (storage.mode(print(1:1 / 1:1)))# 1 "double" (storage.mode(print(1:1 + 1:1)))# 2 "integer" (storage.mode(print(2:2 * 2:2)))# 4 "integer" ## end of moved from is.finite.Rd ## kronecker fred <- matrix(1:12, 3, 4, dimnames=list(LETTERS[1:3], LETTERS[4:7])) bill <- c("happy" = 100, "sad" = 1000) kronecker(fred, bill, make.dimnames = TRUE) bill <- outer(bill, c("cat"=3, "dog"=4)) kronecker(fred, bill, make.dimnames = TRUE) # dimnames are hard work: let's test them thoroughly dimnames(bill) <- NULL kronecker(fred, bill, make=TRUE) kronecker(bill, fred, make=TRUE) dim(bill) <- c(2, 2, 1) dimnames(bill) <- list(c("happy", "sad"), NULL, "") kronecker(fred, bill, make=TRUE) bill <- array(1:24, c(3, 4, 2)) dimnames(bill) <- list(NULL, NULL, c("happy", "sad")) kronecker(bill, fred, make=TRUE) kronecker(fred, bill, make=TRUE) fred <- outer(fred, c("frequentist"=4, "bayesian"=4000)) kronecker(fred, bill, make=TRUE) ## end of moved from kronecker.Rd ## merge authors <- data.frame( surname = c("Tukey", "Venables", "Tierney", "Ripley", "McNeil"), nationality = c("US", "Australia", "US", "UK", "Australia"), deceased = c("yes", rep("no", 4))) books <- data.frame( name = c("Tukey", "Venables", "Tierney", "Ripley", "Ripley", "McNeil", "R Core"), title = c("Exploratory Data Analysis", "Modern Applied Statistics ...", "LISP-STAT", "Spatial Statistics", "Stochastic Simulation", "Interactive Data Analysis", "An Introduction to R"), other.author = c(NA, "Ripley", NA, NA, NA, NA, "Venables & Smith")) b2 <- books; names(b2)[1] <- names(authors)[1] merge(authors, b2, all.x = TRUE) merge(authors, b2, all.y = TRUE) ## empty d.f. : merge(authors, b2[7,]) merge(authors, b2[7,], all.y = TRUE) merge(authors, b2[7,], all.x = TRUE) ## end of moved from merge.Rd ## NA is.na(c(1,NA)) is.na(paste(c(1,NA))) is.na(list())# logical(0) ll <- list(pi,"C",NaN,Inf, 1:3, c(0,NA), NA) is.na (ll) is.nan(ll) ## end of moved from NA.Rd ## scale ## test out NA handling tm <- matrix(c(2,1,0,1,0,NA,NA,NA,0), nrow=3) scale(tm, , FALSE) scale(tm) ## end of moved from scale.Rd ## tabulate tabulate(numeric(0)) ## end of moved from tabulate.Rd ## ts # Ensure working arithmetic for `ts' objects : stopifnot(z == z) stopifnot(z-z == 0) ts(1:5, start=2, end=4) # truncate ts(1:5, start=3, end=17)# repeat ## end of moved from ts.Rd ### end of moved ## PR 715 (Printing list elements w/attributes) ## l <- list(a=10) attr(l$a, "xx") <- 23 l ## 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) ## should print as # [,1] [,2] # [1,] 1 0 # [2,] 2 10 # attr(,"na.action") # [1] 3 # attr(,"na.action") # [1] "omit" ## and x <- 1 attr(x, "foo") <- list(a="a") x ## 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 ## 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) summary(attenu, digits = 5) summary(data.matrix(attenu), digits = 5)# the same for matrix ## Comments: ## No difference between these in 1.2.1 and earlier set.seed(1) x <- c(round(runif(10), 2), 10000) summary(x) summary(data.frame(x)) ## 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 step(detg1.m0,scope=list(upper=~M.user*Temp*Soft)) ## PR 829 (empty values in all.vars) ## This example by Uwe Ligges temp <- matrix(1:4, 2) all.vars(temp ~ 3) # OK all.vars(temp[1, ] ~ 3) # wrong in 1.2.1 ## 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)) ## 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. data(trees) z <- as.matrix(t(trees)) z[1,1] <- z[2,2] <- z[3,3] <- z[2,4] <- NA dist(z, method="euclidean") dist(z, method="maximum") dist(z, method="manhattan") dist(z, method="canberra") ## F. Tusell 2001-03-07. printing kernels. kernel("daniell", m=5) kernel("modified.daniell", m=5) kernel("daniell", m=c(3,5,7)) ## 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)) cbind(tt, tt + 1) ## PR 883 (cor(x,y) when is.null(y)) try(cov(rnorm(10), NULL)) try(cor(rnorm(10), NULL)) ## gave the variance and 1 respectively in 1.2.2. try(var(NULL)) try(var(numeric(0))) ## 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) format(a, justify="right") ## lost dimensions in 1.2.3 ## PR 963 res <- svd(rbind(1:7))## $v lost dimensions in 1.2.3 if(res$u[1,1] < 0) {res$u <- -res$u; res$v <- -res$v} res ## 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) f(g2) 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() ## 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")) ls(env = e)# only "x", i.e. *not* the GlobalEnv f(structure(1,class = "bar")) 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) lm(y ~ x) DF <- data.frame(x, y) lm(y ~ x, data=DF) options(contrasts=c("contr.helmert", "contr.poly")) model.matrix(y ~ x) lm(y ~ x, data=DF) z <- 1:10 lm(y ~ x*z) lm(y ~ x*z - 1) options(oldCon) ## diffinv, Adrian Trapletti, 2001-08-27 x <- ts(1:10) diffinv(diff(x),xi=x[1]) diffinv(diff(x,lag=1,differences=2),lag=1,differences=2,xi=x[1:2]) ## last had wrong start and end ## PR#1072 (Reading Inf and NaN values) as.numeric(as.character(NaN)) as.numeric(as.character(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")) ## 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) 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")) ## 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()) stopifnot(nrow(foo) == 0) try(log("a")) 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) mean(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 unclass(z - 1) ## 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, ]) ## 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) ## 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 m[,2] m[2,2] ## 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) ## 1.4.1 gave `Unimplemented feature in copyVector' x <- vector("list",6) dim(x) <- c(2,3) x[1,2] <- list(letters[10:11]) x ## 1.4.1 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 ## 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)) } RNGkind(normal.kind = "Kinderman-Ramage") set.seed(123) RNGkind() rnorm(4) RNGkind(normal.kind = "Ahrens-Dieter") set.seed(123) RNGkind() rnorm(4) RNGkind(normal.kind = "Box-Muller") set.seed(123) RNGkind() rnorm(4) set.seed(123) runif(4) set.seed(123, "default") set.seed(123, "Marsaglia-Multicarry") ## Careful, not the default anymore runif(4) ## 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) ## 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 ## 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 sum(c(x, x)) # did warn (z <- sum(x, x, 0.0)) # was NA in 1.4.1 typeof(z) ## NA levels in factors (x <- factor(c("a", "NA", "b"), exclude=NULL)) ## 1.4.1 had wrong order for levels is.na(x)[3] <- TRUE x ## missing entry prints as ## printing/formatting NA strings (x <- c("a", "NA", NA, "b")) print(x, quote = FALSE) paste(x) format(x) format(x, justify = "right") format(x, justify = "none") ## 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) ## 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))) ## failed because start = NULL in 1.4.1 ## now gives useful error message glm(y ~ x, family = poisson(identity), start = c(1,0)) ## 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)) warnings() ## extending char arrrays x <- y <- LETTERS[1:2] x[5] <- "C" length(y) <- 5 x y ## 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) lm(y ~ A:U + A:V - 1)$coef # 1.5.1 used dummies coding for V lm(y ~ (A + B) : (U + V) - 1) # 1.5.1 used dummies coding for A:V but not B:V 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))) x <- c(-Inf, -Inf, Inf, Inf) median(x) quantile(x) ## 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)) z ## 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) rbind(test1, test) ## 1.6.1 treated matrix as a vector. ## escapes in non-quoted printing x <- "\\abc\\" names(x) <- 1 x print(x, quote=FALSE) ## 1.6.2 had label misaligned ## summary on data frames containing data frames (PR#1891) x <- data.frame(1:10) x$z <- data.frame(x=1:10,yyy=11:20) summary(x) ## 1.6.2 had NULL labels on output with z columns stacked. ## re-orderings in terms.formula (PR#2206) form <- formula(y ~ a + b:c + d + e + e:d) (tt <- terms(form)) (tt2 <- terms(formula(tt))) stopifnot(identical(tt, tt2)) terms(delete.response(tt)) ## both tt and tt2 re-ordered the formula < 1.7.0 ## now try with a dot data(warpbreaks) terms(breaks ~ ., data = warpbreaks) terms(breaks ~ . - tension, data = warpbreaks) terms(breaks ~ . - tension, data = warpbreaks, simplify = TRUE) terms(breaks ~ . ^2, data = warpbreaks) terms(breaks ~ . ^2, data = warpbreaks, simplify = TRUE) ## 1.6.2 expanded these formulae out as in simplify = TRUE ## printing attributes (PR#2506) (x <- structure(1:4, other=as.factor(LETTERS[1:3]))) ## < 1.7.0 printed the codes of the factor attribute ## add logical matrix replacement indexing for data frames TEMP <- data.frame(VAR1=c(1,2,3,4,5), VAR2=c(5,4,3,2,1), VAR3=c(1,1,1,1,NA)) TEMP[,c(1,3)][TEMP[,c(1,3)]==1 & !is.na(TEMP[,c(1,3)])] < -10 TEMP ## ## moved from reg-plot.R as exact output depends on rounding error ## PR 390 (axis for small ranges) relrange <- function(x) { ## The relative range in EPS units r <- range(x) diff(r)/max(abs(r))/.Machine$double.eps } x <- c(0.12345678912345678, 0.12345678912345679, 0.12345678912345676) relrange(x) ## 1.0125 plot(x) # `extra horizontal' ; +- ok on Solaris; label off on Linux y <- c(0.9999563255363383973418, 0.9999563255363389524533, 0.9999563255363382863194) ## The relative range number: relrange(y) ## 3.000131 plot(y)# once gave infinite loop on Solaris [TL]; y-axis too long ## Comments: The whole issue was finally deferred to main/graphics.c l.1944 ## error("relative range of values is too small to compute accurately"); ## which is not okay. set.seed(101) par(mfrow = c(3,3)) for(j.fac in 1e-12* c(10, 1, .7, .3, .2, .1, .05, .03, .01)) { ## ==== #set.seed(101) # or don't x <- pi + jitter(numeric(101), f = j.fac) rrtxt <- paste("rel.range =", formatC(relrange(x), dig = 4),"* EPS") cat("j.f = ", format(j.fac)," ; ", rrtxt,"\n",sep="") plot(x, type = "l", main = rrtxt) cat("par(\"usr\")[3:4]:", formatC(par("usr")[3:4], wid = 10),"\n", "par(\"yaxp\") : ", formatC(par("yaxp"), wid = 10),"\n\n", sep="") } par(mfrow = c(1,1)) ## The warnings from inside GScale() will differ in their relrange() ... ## >> do sloppy testing ## 2003-02-03 hopefully no more. BDR ## end of PR 390 ## print/show dispatch hasMethods <- .isMethodsDispatchOn() require(methods) setClass("bar", representation(a="numeric")) foo <- new("bar", a=pi) foo show(foo) print(foo) setMethod("show", "bar", function(object){cat("show method\n")}) show(foo) foo print(foo) print(foo, digits = 4) print.bar <- function(x, ...) cat("print method\n") foo print(foo) show(foo) setMethod("print", "bar", function(x, ...){cat("S4 print method\n")}) foo print(foo) show(foo) print(foo, digits = 4) if(!hasMethods) detach("package:methods") ## ## scoping rules calling step inside a function "cement" <- structure(list(x1 = c(7, 1, 11, 11, 7, 11, 3, 1, 2, 21, 1, 11, 10), x2 = c(26, 29, 56, 31, 52, 55, 71, 31, 54, 47, 40, 66, 68), x3 = c(6, 15, 8, 8, 6, 9, 17, 22, 18, 4, 23, 9, 8), x4 = c(60, 52, 20, 47, 33, 22, 6, 44, 22, 26, 34, 12, 12), y = c(78.5, 74.3, 104.3, 87.6, 95.9, 109.2, 102.7, 72.5, 93.1, 115.9, 83.8, 113.3, 109.4)), .Names = c("x1", "x2", "x3", "x4", "y"), class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13")) teststep <- function(formula, data) { d2 <- data fit <- lm(formula, data=d2) step(fit) } teststep(formula(y ~ .), cement) ## failed in 1.6.2 str(array(1))# not a scalar ## na.print="" shouldn't apply to (dim)names! (tf <- table(ff <- factor(c(1:2,NA,2), exclude=NULL))) identical(levels(ff), dimnames(tf)[[1]]) str(levels(ff)) ## not quite ok previous to 1.7.0 ## PR#3058 printing with na.print and right=TRUE a <- matrix( c(NA, "a", "b", "10", NA, NA, "d", "12", NA, NA, NA, "14"), byrow=T, ncol=4 ) print(a, right=TRUE, na.print=" ") print(a, right=TRUE, na.print="----") ## misaligned in 1.7.0 ## assigning factors to dimnames A <- matrix(1:4, 2) aa <- factor(letters[1:2]) dimnames(A) <- list(aa, NULL) A dimnames(A) ## 1.7.0 gave internal codes as display and dimnames() ## 1.7.1beta gave NAs via dimnames() ## 1.8.0 converts factors to character ## wishlist PR#2776: aliased coefs in lm/glm set.seed(123) x2 <- x1 <- 1:10 x3 <- 0.1*(1:10)^2 y <- x1 + rnorm(10) (fit <- lm(y ~ x1 + x2 + x3)) summary(fit, cor = TRUE) (fit <- glm(y ~ x1 + x2 + x3)) summary(fit, cor = TRUE) ## omitted silently in summary.glm < 1.8.0 ## list-like indexing of data frames with drop specified data(women) women["height"] women["height", drop = FALSE] # same with a warning women["height", drop = TRUE] # ditto women[,"height", drop = FALSE] # no warning women[,"height", drop = TRUE] # a vector ## second and third were interpreted as women["height", , drop] in 1.7.x ## make.names make.names("") make.names(".aa") ## was "X.aa" in 1.7.1 make.names(".2") make.names(".2a") # not valid in R make.names(as.character(NA)) ## ## strange names in data frames as.data.frame(list(row.names=17)) # 0 rows in 1.7.1 aa <- data.frame(aa=1:3) aa[["row.names"]] <- 4:6 aa # fine in 1.7.1 A <- matrix(4:9, 3, 2) colnames(A) <- letters[1:2] aa[["row.names"]] <- A aa ## wrong printed names in 1.7.1 ## assigning to NULL a <- NULL a[["a"]] <- 1 a a <- NULL a[["a"]] <- "something" a a <- NULL a[["a"]] <- 1:3 a ## Last was an error in 1.7.1 ## examples of 0-rank models, some empty, some rank-deficient y <- rnorm(10) x <- rep(0, 10) (fit <- lm(y ~ 0)) summary(fit) anova(fit) predict(fit) predict(fit, data.frame(x=x), se=TRUE) predict(fit, type="terms", se=TRUE) variable.names(fit) #should be empty model.matrix(fit) (fit <- lm(y ~ x + 0)) summary(fit) anova(fit) predict(fit) predict(fit, data.frame(x=x), se=TRUE) predict(fit, type="terms", se=TRUE) variable.names(fit) #should be empty model.matrix(fit) (fit <- glm(y ~ 0)) summary(fit) anova(fit) predict(fit) predict(fit, data.frame(x=x), se=TRUE) predict(fit, type="terms", se=TRUE) (fit <- glm(y ~ x + 0)) summary(fit) anova(fit) predict(fit) predict(fit, data.frame(x=x), se=TRUE) predict(fit, type="terms", se=TRUE) ## Lots of problems in 1.7.x ## lm.influence on deficient lm models dat <- data.frame(y=rnorm(10), x1=1:10, x2=1:10, x3 = 0, wt=c(0,rep(1, 9)), row.names=letters[1:10]) dat[3, 1] <- dat[4, 2] <- NA lm.influence(lm(y ~ x1 + x2, data=dat, weights=wt, na.action=na.omit)) lm.influence(lm(y ~ x1 + x2, data=dat, weights=wt, na.action=na.exclude)) lm.influence(lm(y ~ 0, data=dat, weights=wt, na.action=na.omit)) lm.influence(lm(y ~ 0, data=dat, weights=wt, na.action=na.exclude)) lm.influence(lm(y ~ 0 + x3, data=dat, weights=wt, na.action=na.omit)) lm.influence(lm(y ~ 0 + x3, data=dat, weights=wt, na.action=na.exclude)) lm.influence(lm(y ~ 0, data=dat, na.action=na.exclude)) ## last three misbehaved in 1.7.x, none had proper names. ## length of results in ARMAacf when lag.max is used ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=1) # was 4 in 1.7.1 ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=2) ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=3) ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=4) ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=5) # failed in 1.7.1 ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=6) ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=10) ## ## Indexing non-existent columns in a data frame x <- data.frame(a = 1, b = 2) try(x[c("a", "c")]) try(x[, c("a", "c")]) try(x[1, c("a", "c")]) ## Second succeeded, third gave uniformative error message in 1.7.x. ## methods(class = ) with namespaces, .Primitives etc (many missing in 1.7.x): meth2gen <- function(cl) noquote(sub(paste("\\.",cl,"$",sep=""),"", methods(class = cl))) meth2gen("data.frame") meth2gen("dendrogram") ## --> the output may need somewhat frequent updating.. ## subsetting a 1D array lost the dimensions x <- array(1:5, dim=c(5)) dim(x) dim(x[, drop=TRUE]) dim(x[2:3]) dim(x[2]) dim(x[2, drop=FALSE]) dimnames(x) <- list(some=letters[1:5]) x[] x[2:3] x[2] x[2, drop=FALSE] ## both dim and dimnames lost in 1.8.0 ## print.dist() didn't show NA's prior to 1.8.1 x <- cbind(c(1,NA,2,3), c(NA,2,NA,1)) (d <- dist(x)) print(d, diag = TRUE) ## ## offsets in model terms where sometimes not deleted correctly attributes(terms(~ a + b + a:b + offset(c)))[c("offset", "term.labels")] attributes(terms(y ~ a + b + a:b + offset(c)))[c("offset", "term.labels")] attributes(terms(~ offset(c) + a + b + a:b))[c("offset", "term.labels")] attributes(terms(y ~ offset(c) + a + b + a:b))[c("offset", "term.labels")] ## errors prior to 1.8.1 ## 0-level factors gave nonsensical answers in model.matrix m <- model.frame(~x, data.frame(x=NA), na.action=na.pass) model.matrix(~x, m) lm.fit <- lm(y ~ x, data.frame(x=1:10, y=1:10)) try(predict(lm.fit, data.frame(x=NA))) ## wrong answers in 1.8.0, refused to run in 1.8.1 ## failure to print data frame containing arrays ## raised by John Fox on R-devel on 2004-01-08 y1 <- array(1:10, dim=10) y2 <- array(1:30, dim=c(10,3), dimnames=list(NULL, letters[1:3])) y3 <- array(1:40, dim=c(10,2,2), dimnames=list(NULL, letters[1:2], NULL)) data.frame(y=y1) data.frame(y=y2) data.frame(y=y3) as.data.frame(y1) as.data.frame(y2) as.data.frame(y3) X <- data.frame(x=1:10) X$y <- y1 X sapply(X, dim) X$y <- y2 X sapply(X, dim) X$y <- y3 X sapply(X, dim) ## The last one fails in S. ## test of user hooks for(id in c("A", "B")) { eval(substitute( { setHook(packageEvent("stats4", "onLoad"), function(pkgname, ...) cat("onLoad", sQuote(pkgname), id, "\n")); setHook(packageEvent("stats4", "attach"), function(pkgname, ...) cat("attach", sQuote(pkgname), id, "\n")); setHook(packageEvent("stats4", "detach"), function(pkgname, ...) cat("detach", sQuote(pkgname), id, "\n")); setHook(packageEvent("stats4", "onUnload"), function(pkgname, ...) cat("onUnload", sQuote(pkgname), id, "\n")) }, list(id=id))) } loadNamespace("stats4") library("stats4") detach("package:stats4") unloadNamespace("stats4") ## Just tests ## rep(0-length-vector, length.out > 0) rep(integer(0), length.out=0) rep(integer(0), length.out=10) typeof(.Last.value) rep(logical(0), length.out=0) rep(logical(0), length.out=10) typeof(.Last.value) rep(numeric(0), length.out=0) rep(numeric(0), length.out=10) typeof(.Last.value) rep(character(0), length.out=0) rep(character(0), length.out=10) typeof(.Last.value) rep(complex(0), length.out=0) rep(complex(0), length.out=10) typeof(.Last.value) rep(list(), length.out=0) rep(list(), length.out=10) ## always 0-length before 1.9.0 ## supplying 0-length data to array and matrix array(numeric(0), c(2, 2)) array(list(), c(2,2)) # worked < 1.8.0, error in 1.8.x matrix(character(0), 1, 2) matrix(integer(0), 1, 2) matrix(logical(0), 1, 2) matrix(numeric(0), 1, 2) matrix(complex(0), 1, 2) matrix(list(), 1, 2) ## did not work < 1.9.0 ## S compatibility change in 1.9.0 rep(1:2, each=3, length=12) ## used to pad with NAs. ## PR#6510: aov() with error and -1 set.seed(1) test.df <- data.frame (y=rnorm(8), a=gl(2,1,8), b=gl(2,3,8),c=gl(2,4,8)) aov(y ~ a + b + Error(c), data=test.df) aov(y ~ a + b - 1 + Error(c), data=test.df) ## wrong assignment to strata labels < 1.9.0 ## Note this is unbalanced and not a good example binom.test(c(800,10))# p-value < epsilon ## Misleading error messages on integer overflow ## Uwe Ligges, R-devel, 2004-02-19 try(numeric(2^31)) try(matrix( , 2^31, 1)) try(matrix( , 2^31/10, 100)) try(array(dim=c(2^31/10, 100))) ## reported negative values (really integer NA) for R < 1.9.0 ## aov with a singular error model rd <- c(16.53, 12.12, 10.04, 15.32, 12.33, 10.1, 17.09, 11.69, 11.81, 14.75, 10.72, 8.79, 13.14, 9.79, 8.36, 15.62, 9.64, 8.72, 15.32, 11.35, 8.52, 13.27, 9.74, 8.78, 13.16, 10.16, 8.4, 13.08, 9.66, 8.16, 12.17, 9.13, 7.43, 13.28, 9.16, 7.92, 118.77, 78.83, 62.2, 107.29, 73.79, 58.59, 118.9, 66.35, 53.12, 372.62, 245.39, 223.72, 326.03, 232.67, 209.44, 297.55, 239.71, 223.8) sample.df <- data.frame(dep.variable=rd, subject=factor(rep(paste("subj",1:6, sep=""),each=9)), f1=factor(rep(rep(c("f1","f2","f3"),each=6),3)), f2=factor(rep(c("g1","g2","g3"),each=18)) ) sample.aov <- aov(dep.variable ~ f1 * f2 + Error(subject/(f1+f2)), data=sample.df) sample.aov summary(sample.aov) sample.aov <- aov(dep.variable ~ f1 * f2 + Error(subject/(f2+f1)), data=sample.df) sample.aov summary(sample.aov) ## failed in 1.8.1 ## PR#6645 stem() with near-constant values stem(rep(1, 100)) stem(rep(0.1, 10)) stem(c(rep(1, 10), 1+1.e-8)) stem(c(rep(1, 10), 1+1.e-9)) stem(c(rep(1, 10), 1+1.e-10), atom=0) # integer-overflow is avoided. ## had integer overflows in 1.8.1, and silly shifts of decimal point ## PR#6633 warnings with vector op matrix, and more set.seed(1) x1 <- rnorm(3) y1 <- rnorm(4) x1 * y1 x1 * as.matrix(y1) # no warning in 1.8.1 x1 * matrix(y1,2,2)# ditto z1 <- x1 > 0 z2 <- y1 > 0 z1 & z2 z1 & as.matrix(z2) # no warning in 1.8.1 x1 < y1 # no warning in 1.8.1 x1 < as.matrix(y1) # ditto ## ## summary method for mle library(stats4) N <- c(rep(3:6, 3), 7,7, rep(8,6), 9,9, 10,12)# sample from Pois(lam = 7) summary(mle(function(Lam = 1) -sum(dpois(N, Lam)))) ## "Coefficients" was "NULL" in 1.9.0's "devel" ## PR#6656 terms.formula(simplify = TRUE) was losing offset terms ## successive offsets caused problems df <- data.frame(x=1:4, y=sqrt( 1:4), z=c(2:4,1)) fit1 <- glm(y ~ offset(x) + z, data=df) update(fit1, ". ~.")$call ## lost offset in 1.7.0 to 1.8.1 terms(y ~ offset(x) + offset(log(x)) + z, data=df) ## failed to remove second offset from formula in 1.8.1 terms(y ~ offset(x) + z - z, data=df, simplify = TRUE) ## first fix failed for models with no non-offset terms. ## only the first two were wrong up to 1.8.1: 3:4 * 1e-100 8:11* 1e-100 1:2 * 1e-99 1:2 * 1e+99 8:11* 1e+99 3:4 * 1e+100 ## ## negative subscripts could be mixed with NAs x <- 1:3 try(x[-c(1, NA)]) ## worked on some platforms, segfaulted on others in 1.8.1 ## vector 'border' (and no 'pch', 'cex' nor 'bg'): data(InsectSprays) boxplot(count ~ spray, data = InsectSprays, border=2:7) ## gave warnings in 1.9.0