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reg-tests-2.R
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reg-tests-2.R
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## Regression tests for which the printed output is the issue
### _and_ must work (no Recommended packages, please)
postscript("reg-tests-2.ps", encoding = "ISOLatin1.enc")
RNGversion("1.6.2")
### moved from various .Rd files
## abbreviate
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
## is.na was returning unset values on nested lists
ll <- list(list(1))
for (i in 1:5) print(as.integer(is.na(ll)))
## 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
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 <ligges@statistik.uni-dortmund.de>
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.
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 <John.Peters@tip.csiro.au>
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
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 <NA>
## 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'
## ...and 1.9.1 had it as `Jan 1960'!!
## 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$z <- data.frame(x=1:10,yyy=11:20)
summary(x)
## 1.6.2 had NULL labels on output with z columns stacked.
## re-orderings in terms.formula (PR#2206)
form <- formula(y ~ a + b:c + d + e + e:d)
(tt <- terms(form))
(tt2 <- terms(formula(tt)))
stopifnot(identical(tt, tt2))
terms(delete.response(tt))
## both tt and tt2 re-ordered the formula < 1.7.0
## now try with a dot
terms(breaks ~ ., data = warpbreaks)
terms(breaks ~ . - tension, data = warpbreaks)
terms(breaks ~ . - tension, data = warpbreaks, simplify = TRUE)
terms(breaks ~ . ^2, data = warpbreaks)
terms(breaks ~ . ^2, data = warpbreaks, simplify = TRUE)
## 1.6.2 expanded these formulae out as in simplify = TRUE
## printing attributes (PR#2506)
(x <- structure(1:4, other=as.factor(LETTERS[1:3])))
## < 1.7.0 printed the codes of the factor attribute
## add logical matrix replacement indexing for data frames
TEMP <- data.frame(VAR1=c(1,2,3,4,5), VAR2=c(5,4,3,2,1), VAR3=c(1,1,1,1,NA))
TEMP[,c(1,3)][TEMP[,c(1,3)]==1 & !is.na(TEMP[,c(1,3)])] < -10
TEMP
##
## moved from reg-plot.R as exact output depends on rounding error
## PR 390 (axis for small ranges)
relrange <- function(x) {
## The relative range in EPS units
r <- range(x)
diff(r)/max(abs(r))/.Machine$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
## 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
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)
##