Last updated on 2020-08-07 01:50:35 CEST.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| ATR | 12 | ||
| basefun | 12 | ||
| coin | 12 | ||
| cotram | 12 | ||
| exactRankTests | 12 | ||
| HSAUR | 12 | ||
| HSAUR2 | 12 | ||
| HSAUR3 | 2 | 10 | |
| inum | 12 | ||
| ipred | 1 | 11 | |
| libcoin | 5 | 7 | |
| maxstat | 12 | ||
| mlt | 12 | ||
| mlt.docreg | 12 | ||
| modeltools | 12 | ||
| multcomp | 12 | ||
| MVA | 12 | ||
| mvtnorm | 12 | ||
| party | 1 | 11 | |
| partykit | 2 | 10 | |
| tbm | 12 | ||
| TH.data | 7 | 5 | |
| tram | 12 | ||
| trtf | 12 | ||
| variables | 12 |
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: NOTE: 2, OK: 10
Version: 1.0-9
Check: installed package size
Result: NOTE
installed size is 5.0Mb
sub-directories of 1Mb or more:
doc 3.7Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-patched-solaris-x86
Current CRAN status: OK: 12
Current CRAN status: NOTE: 1, OK: 11
Version: 0.9-9
Check: Rd cross-references
Result: NOTE
Undeclared packages ‘randomForest’, ‘party’ in Rd xrefs
Flavor: r-devel-linux-x86_64-fedora-clang
Current CRAN status: ERROR: 5, OK: 7
Version: 1.0-5
Check: whether package can be installed
Result: WARN
Found the following significant warnings:
libcoin.c:7655:22: warning: incompatible pointer to integer conversion passing 'int *' to parameter of type 'int'; remove & [-Wint-conversion]
libcoin.c:7655:27: warning: incompatible pointer to integer conversion passing 'int *' to parameter of type 'int'; remove & [-Wint-conversion]
libcoin.c:7656:42: warning: incompatible pointer to integer conversion passing 'int *' to parameter of type 'int'; dereference with * [-Wint-conversion]
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in 'libcoin.Rnw'
...
> isequal <- function(a, b) {
+ attributes(a) <- NULL
+ attributes(b) <- NULL
+ if (!isTRUE(all.equal(a, b))) {
+ print(a, digits .... [TRUNCATED]
> library("libcoin")
> set.seed(290875)
> x <- gl(5, 20)
> y <- round(runif(length(x)), 1)
> ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1))
> ls1$LinearStatistic
[1] 8.8 9.5 10.3 9.8 10.5
> tapply(y, x, sum)
1 2 3 4 5
8.8 9.5 10.3 9.8 10.5
> ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
> all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
+ names(ls2))])
[1] TRUE
> X <- rbind(0, diag(nlevels(x)))
> ix <- unclass(x)
> ylev <- sort(unique(y))
> Y <- rbind(0, matrix(ylev, ncol = 1))
> iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
> ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
+ iy = factor(iy))
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
> all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
+ names(ls4))])
[1] TRUE
> ls3$Table
, , 1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 4 4 1 2 3 0 1 2 3 0
[3,] 0 2 2 1 2 2 5 0 1 1 3 1
[4,] 0 1 1 4 0 1 5 2 0 2 3 1
[5,] 0 0 2 2 4 2 2 1 3 2 1 1
[6,] 0 1 3 1 1 1 2 2 2 6 1 0
> xtabs(~ix + iy)
iy
ix 1 2 3 4 5 6 7 8 9 10 11
1 0 4 4 1 2 3 0 1 2 3 0
2 2 2 1 2 2 5 0 1 1 3 1
3 1 1 4 0 1 5 2 0 2 3 1
4 0 2 2 4 2 2 1 3 2 1 1
5 1 3 1 1 1 2 2 2 6 1 0
> ls1$Covariance
[1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
[7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[13] 1.3572364 -0.3393091 1.3572364
> vcov(ls1)
[,1] [,2] [,3] [,4] [,5]
[1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
[2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
[3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
[5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
> doTest(ls1, teststat = "maximum", pvalue = FALSE)
$TestStatistic
[1] 0.8411982
$p.value
[1] NA
> doTest(ls1, teststat = "maximum")
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.8852087
> doTest(ls1, teststat = "maximum", log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.108822
> doTest(ls1, teststat = "maximum", lower = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.1150168
> doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] -2.164164
> doTest(ls1, teststat = "quadratic")
$TestStatistic
[1] 1.077484
$p.value
[1] 0.897828
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> t1 <- ctabs(ix = ix, iy = iy)
> t2 <- xtabs(~ix + iy)
> max(abs(t1[-1, -1] - t2))
[1] 0
> N <- 20
> P <- 3
> Lx <- 10
> Ly <- 5
> Q <- 4
> B <- 2
> iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
> ix <- sample(1:Lx, size = N, replace = TRUE)
> levels(ix) <- 1:Lx
> ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
> x <- iX2d[ix + 1, ]
> Xfactor <- diag(Lx)[ix, ]
> iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
> iy <- sample(1:Ly, size = N, replace = TRUE)
> levels(iy) <- 1:Ly
> iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
> y <- iY2d[iy + 1, ]
> weights <- sample(0:5, size = N, replace = TRUE)
> block <- sample(gl(B, ceiling(N/B))[1:N])
> subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
> subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
> r1 <- rep(1:ncol(x), ncol(y))
> r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
> r2 <- rep(1:ncol(y), each = ncol(x))
> r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
> LECV <- function(X, Y, weights = integer(0), subset = integer(0),
+ block = integer(0)) {
+ if (length(weights) == 0)
+ weights <- .... [TRUNCATED]
> cmpr <- function(ret1, ret2) {
+ if (inherits(ret1, "LinStatExpCov")) {
+ if (!ret1$varonly)
+ ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
> LECVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset)
> LEVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset, varonly = TRUE)
> testit <- function(...) {
+ a <- LinStatExpCov(x, y, ...)
+ b <- LECV(x, y, ...)
+ d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> testit <- function(...) {
+ a <- LinStatExpCov(X = ix, y, ...)
+ b <- LECV(Xfactor, y, ...)
+ d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
+ weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
*** caught segfault ***
address 0x6d94000, cause 'memory not mapped'
*** caught bus error ***
address (nil), cause 'unknown'
Bus error
... incomplete output. Crash?
'libcoin.Rnw' using 'UTF-8'... failed to complete the test
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building 'libcoin.Rnw' using Sweave
*** caught segfault ***
address 0x4fd5000, cause 'memory not mapped'
Segmentation fault
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0-5
Check: whether package can be installed
Result: WARN
Found the following significant warnings:
libcoin.c:7655:22: warning: passing argument 1 of ‘S_rcont2’ makes integer from pointer without a cast [-Wint-conversion]
libcoin.c:7655:27: warning: passing argument 2 of ‘S_rcont2’ makes integer from pointer without a cast [-Wint-conversion]
libcoin.c:7656:53: warning: passing argument 5 of ‘S_rcont2’ makes integer from pointer without a cast [-Wint-conversion]
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘libcoin.Rnw’
...
> isequal <- function(a, b) {
+ attributes(a) <- NULL
+ attributes(b) <- NULL
+ if (!isTRUE(all.equal(a, b))) {
+ print(a, digits .... [TRUNCATED]
> library("libcoin")
> set.seed(290875)
> x <- gl(5, 20)
> y <- round(runif(length(x)), 1)
> ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1))
> ls1$LinearStatistic
[1] 8.8 9.5 10.3 9.8 10.5
> tapply(y, x, sum)
1 2 3 4 5
8.8 9.5 10.3 9.8 10.5
> ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
> all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
+ names(ls2))])
[1] TRUE
> X <- rbind(0, diag(nlevels(x)))
> ix <- unclass(x)
> ylev <- sort(unique(y))
> Y <- rbind(0, matrix(ylev, ncol = 1))
> iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
> ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
+ iy = factor(iy))
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
> all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
+ names(ls4))])
[1] TRUE
> ls3$Table
, , 1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 4 4 1 2 3 0 1 2 3 0
[3,] 0 2 2 1 2 2 5 0 1 1 3 1
[4,] 0 1 1 4 0 1 5 2 0 2 3 1
[5,] 0 0 2 2 4 2 2 1 3 2 1 1
[6,] 0 1 3 1 1 1 2 2 2 6 1 0
> xtabs(~ix + iy)
iy
ix 1 2 3 4 5 6 7 8 9 10 11
1 0 4 4 1 2 3 0 1 2 3 0
2 2 2 1 2 2 5 0 1 1 3 1
3 1 1 4 0 1 5 2 0 2 3 1
4 0 2 2 4 2 2 1 3 2 1 1
5 1 3 1 1 1 2 2 2 6 1 0
> ls1$Covariance
[1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
[7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[13] 1.3572364 -0.3393091 1.3572364
> vcov(ls1)
[,1] [,2] [,3] [,4] [,5]
[1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
[2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
[3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
[5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
> doTest(ls1, teststat = "maximum", pvalue = FALSE)
$TestStatistic
[1] 0.8411982
$p.value
[1] NA
> doTest(ls1, teststat = "maximum")
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.8852087
> doTest(ls1, teststat = "maximum", log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.108822
> doTest(ls1, teststat = "maximum", lower = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.1150168
> doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] -2.164164
> doTest(ls1, teststat = "quadratic")
$TestStatistic
[1] 1.077484
$p.value
[1] 0.897828
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> t1 <- ctabs(ix = ix, iy = iy)
> t2 <- xtabs(~ix + iy)
> max(abs(t1[-1, -1] - t2))
[1] 0
> N <- 20
> P <- 3
> Lx <- 10
> Ly <- 5
> Q <- 4
> B <- 2
> iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
> ix <- sample(1:Lx, size = N, replace = TRUE)
> levels(ix) <- 1:Lx
> ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
> x <- iX2d[ix + 1, ]
> Xfactor <- diag(Lx)[ix, ]
> iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
> iy <- sample(1:Ly, size = N, replace = TRUE)
> levels(iy) <- 1:Ly
> iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
> y <- iY2d[iy + 1, ]
> weights <- sample(0:5, size = N, replace = TRUE)
> block <- sample(gl(B, ceiling(N/B))[1:N])
> subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
> subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
> r1 <- rep(1:ncol(x), ncol(y))
> r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
> r2 <- rep(1:ncol(y), each = ncol(x))
> r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
> LECV <- function(X, Y, weights = integer(0), subset = integer(0),
+ block = integer(0)) {
+ if (length(weights) == 0)
+ weights <- .... [TRUNCATED]
> cmpr <- function(ret1, ret2) {
+ if (inherits(ret1, "LinStatExpCov")) {
+ if (!ret1$varonly)
+ ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
> LECVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset)
> LEVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset, varonly = TRUE)
> testit <- function(...) {
+ a <- LinStatExpCov(x, y, ...)
+ b <- LECV(x, y, ...)
+ d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> testit <- function(...) {
+ a <- LinStatExpCov(X = ix, y, ...)
+ b <- LECV(Xfactor, y, ...)
+ d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
+ weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
*** caught segfault ***
address 0x55d712d3b000, cause 'memory not mapped'
Segmentation fault
... incomplete output. Crash?
‘libcoin.Rnw’ using ‘UTF-8’... failed to complete the test
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
...
--- re-building ‘libcoin.Rnw’ using Sweave
*** caught segfault ***
address 0x55f579315ebc, cause 'memory not mapped'
Traceback:
1: .LinStatExpCov2d(X = X, Y = Y, ix = ix, iy = iy, weights = weights, subset = subset, block = block, varonly = varonly, checkNAs = checkNAs, nresample = nresample, standardise = standardise, tol = tol)
2: LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy, weights = weights, subset = subset, nresample = 10)
3: eval(expr, .GlobalEnv)
4: eval(expr, .GlobalEnv)
5: withVisible(eval(expr, .GlobalEnv))
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
10: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
11: evalFunc(ce, options)
12: tryCatchList(expr, classes, parentenv, handlers)
13: tryCatch(evalFunc(ce, options), finally = { cat("\n") sink()})
14: driver$runcode(drobj, chunk, chunkopts)
15: utils::Sweave(...)
16: engine$weave(file, quiet = quiet, encoding = enc)
17: doTryCatch(return(expr), name, parentenv, handler)
18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
19: tryCatchList(expr, classes, parentenv, handlers)
20: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) } outputs <- c(outputs, output)}, error = function(e) { thisOK <<- FALSE fails <<- c(fails, file) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
21: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/libcoin.Rcheck/vign_test/libcoin", ser_elibs = "/home/hornik/tmp/scratch/RtmpaI828g/file38d47bdd2485.rds")
An irrecoverable exception occurred. R is aborting now ...
Segmentation fault
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘libcoin.Rnw’
> isequal <- function(a, b) {
+ attributes(a) <- NULL
+ attributes(b) <- NULL
+ if (!isTRUE(all.equal(a, b))) {
+ print(a, digits .... [TRUNCATED]
> library("libcoin")
> set.seed(290875)
> x <- gl(5, 20)
> y <- round(runif(length(x)), 1)
> ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1))
> ls1$LinearStatistic
[1] 8.8 9.5 10.3 9.8 10.5
> tapply(y, x, sum)
1 2 3 4 5
8.8 9.5 10.3 9.8 10.5
> ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
> all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
+ names(ls2))])
[1] TRUE
> X <- rbind(0, diag(nlevels(x)))
> ix <- unclass(x)
> ylev <- sort(unique(y))
> Y <- rbind(0, matrix(ylev, ncol = 1))
> iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
> ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
+ iy = factor(iy))
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
> all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
+ names(ls4))])
[1] TRUE
> ls3$Table
, , 1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 4 4 1 2 3 0 1 2 3 0
[3,] 0 2 2 1 2 2 5 0 1 1 3 1
[4,] 0 1 1 4 0 1 5 2 0 2 3 1
[5,] 0 0 2 2 4 2 2 1 3 2 1 1
[6,] 0 1 3 1 1 1 2 2 2 6 1 0
> xtabs(~ix + iy)
iy
ix 1 2 3 4 5 6 7 8 9 10 11
1 0 4 4 1 2 3 0 1 2 3 0
2 2 2 1 2 2 5 0 1 1 3 1
3 1 1 4 0 1 5 2 0 2 3 1
4 0 2 2 4 2 2 1 3 2 1 1
5 1 3 1 1 1 2 2 2 6 1 0
> ls1$Covariance
[1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
[7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[13] 1.3572364 -0.3393091 1.3572364
> vcov(ls1)
[,1] [,2] [,3] [,4] [,5]
[1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
[2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
[3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
[5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
> doTest(ls1, teststat = "maximum", pvalue = FALSE)
$TestStatistic
[1] 0.8411982
$p.value
[1] NA
> doTest(ls1, teststat = "maximum")
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.8852087
> doTest(ls1, teststat = "maximum", log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.108822
> doTest(ls1, teststat = "maximum", lower = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.1150168
> doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] -2.164164
> doTest(ls1, teststat = "quadratic")
$TestStatistic
[1] 1.077484
$p.value
[1] 0.897828
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> t1 <- ctabs(ix = ix, iy = iy)
> t2 <- xtabs(~ix + iy)
> max(abs(t1[-1, -1] - t2))
[1] 0
> N <- 20
> P <- 3
> Lx <- 10
> Ly <- 5
> Q <- 4
> B <- 2
> iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
> ix <- sample(1:Lx, size = N, replace = TRUE)
> levels(ix) <- 1:Lx
> ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
> x <- iX2d[ix + 1, ]
> Xfactor <- diag(Lx)[ix, ]
> iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
> iy <- sample(1:Ly, size = N, replace = TRUE)
> levels(iy) <- 1:Ly
> iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
> y <- iY2d[iy + 1, ]
> weights <- sample(0:5, size = N, replace = TRUE)
> block <- sample(gl(B, ceiling(N/B))[1:N])
> subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
> subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
> r1 <- rep(1:ncol(x), ncol(y))
> r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
> r2 <- rep(1:ncol(y), each = ncol(x))
> r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
> LECV <- function(X, Y, weights = integer(0), subset = integer(0),
+ block = integer(0)) {
+ if (length(weights) == 0)
+ weights <- .... [TRUNCATED]
> cmpr <- function(ret1, ret2) {
+ if (inherits(ret1, "LinStatExpCov")) {
+ if (!ret1$varonly)
+ ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
> LECVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset)
> LEVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset, varonly = TRUE)
> testit <- function(...) {
+ a <- LinStatExpCov(x, y, ...)
+ b <- LECV(x, y, ...)
+ d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> testit <- function(...) {
+ a <- LinStatExpCov(X = ix, y, ...)
+ b <- LECV(Xfactor, y, ...)
+ d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
+ weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
*** caught segfault ***
address 0xfffffffe9864ad0c, cause 'memory not mapped'
Traceback:
1: .LinStatExpCov2d(X = X, Y = Y, ix = ix, iy = iy, weights = weights, subset = subset, block = block, varonly = varonly, checkNAs = checkNAs, nresample = nresample, standardise = standardise, tol = tol)
2: LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy, weights = weights, subset = subset, nresample = 10)
3: eval(ei, envir)
4: eval(ei, envir)
5: withVisible(eval(ei, envir))
6: source(output, echo = TRUE)
7: doTryCatch(return(expr), name, parentenv, handler)
8: tryCatchOne(expr, names, parentenv, handlers[[1L]])
9: tryCatchList(expr, classes, parentenv, handlers)
10: tryCatch({ source(output, echo = TRUE)}, error = function(e) { cat("\n When sourcing ", sQuote(output), ":\n", sep = "") stop(conditionMessage(e), call. = FALSE, domain = NA)})
11: tools:::.run_one_vignette("libcoin.Rnw", "/data/gannet/ripley/R/packages/tests-clang/libcoin/vignettes", encoding = "UTF-8", pkgdir = "/data/gannet/ripley/R/packages/tests-clang/libcoin")
An irrecoverable exception occurred. R is aborting now ...
... incomplete output. Crash?
‘libcoin.Rnw’ using ‘UTF-8’... failed to complete the test
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘libcoin.Rnw’ using Sweave
*** caught segfault ***
address 0x6032000, cause 'memory not mapped'
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘libcoin.Rnw’
> isequal <- function(a, b) {
+ attributes(a) <- NULL
+ attributes(b) <- NULL
+ if (!isTRUE(all.equal(a, b))) {
+ print(a, digits .... [TRUNCATED]
> library("libcoin")
> set.seed(290875)
> x <- gl(5, 20)
> y <- round(runif(length(x)), 1)
> ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1))
> ls1$LinearStatistic
[1] 8.8 9.5 10.3 9.8 10.5
> tapply(y, x, sum)
1 2 3 4 5
8.8 9.5 10.3 9.8 10.5
> ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
> all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
+ names(ls2))])
[1] TRUE
> X <- rbind(0, diag(nlevels(x)))
> ix <- unclass(x)
> ylev <- sort(unique(y))
> Y <- rbind(0, matrix(ylev, ncol = 1))
> iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
> ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
+ iy = factor(iy))
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
> all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
+ names(ls4))])
[1] TRUE
> ls3$Table
, , 1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 4 4 1 2 3 0 1 2 3 0
[3,] 0 2 2 1 2 2 5 0 1 1 3 1
[4,] 0 1 1 4 0 1 5 2 0 2 3 1
[5,] 0 0 2 2 4 2 2 1 3 2 1 1
[6,] 0 1 3 1 1 1 2 2 2 6 1 0
> xtabs(~ix + iy)
iy
ix 1 2 3 4 5 6 7 8 9 10 11
1 0 4 4 1 2 3 0 1 2 3 0
2 2 2 1 2 2 5 0 1 1 3 1
3 1 1 4 0 1 5 2 0 2 3 1
4 0 2 2 4 2 2 1 3 2 1 1
5 1 3 1 1 1 2 2 2 6 1 0
> ls1$Covariance
[1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
[7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[13] 1.3572364 -0.3393091 1.3572364
> vcov(ls1)
[,1] [,2] [,3] [,4] [,5]
[1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
[2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
[3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
[5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
> doTest(ls1, teststat = "maximum", pvalue = FALSE)
$TestStatistic
[1] 0.8411982
$p.value
[1] NA
> doTest(ls1, teststat = "maximum")
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.8852087
> doTest(ls1, teststat = "maximum", log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.108822
> doTest(ls1, teststat = "maximum", lower = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.1150168
> doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] -2.164164
> doTest(ls1, teststat = "quadratic")
$TestStatistic
[1] 1.077484
$p.value
[1] 0.897828
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> t1 <- ctabs(ix = ix, iy = iy)
> t2 <- xtabs(~ix + iy)
> max(abs(t1[-1, -1] - t2))
[1] 0
> N <- 20
> P <- 3
> Lx <- 10
> Ly <- 5
> Q <- 4
> B <- 2
> iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
> ix <- sample(1:Lx, size = N, replace = TRUE)
> levels(ix) <- 1:Lx
> ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
> x <- iX2d[ix + 1, ]
> Xfactor <- diag(Lx)[ix, ]
> iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
> iy <- sample(1:Ly, size = N, replace = TRUE)
> levels(iy) <- 1:Ly
> iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
> y <- iY2d[iy + 1, ]
> weights <- sample(0:5, size = N, replace = TRUE)
> block <- sample(gl(B, ceiling(N/B))[1:N])
> subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
> subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
> r1 <- rep(1:ncol(x), ncol(y))
> r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
> r2 <- rep(1:ncol(y), each = ncol(x))
> r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
> LECV <- function(X, Y, weights = integer(0), subset = integer(0),
+ block = integer(0)) {
+ if (length(weights) == 0)
+ weights <- .... [TRUNCATED]
> cmpr <- function(ret1, ret2) {
+ if (inherits(ret1, "LinStatExpCov")) {
+ if (!ret1$varonly)
+ ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
> LECVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset)
> LEVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset, varonly = TRUE)
> testit <- function(...) {
+ a <- LinStatExpCov(x, y, ...)
+ b <- LECV(x, y, ...)
+ d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> testit <- function(...) {
+ a <- LinStatExpCov(X = ix, y, ...)
+ b <- LECV(Xfactor, y, ...)
+ d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
+ weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
*** caught segfault ***
address 0x2ec1702c, cause 'memory not mapped'
Traceback:
1: .LinStatExpCov2d(X = X, Y = Y, ix = ix, iy = iy, weights = weights, subset = subset, block = block, varonly = varonly, checkNAs = checkNAs, nresample = nresample, standardise = standardise, tol = tol)
2: LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy, weights = weights, subset = subset, nresample = 10)
3: eval(ei, envir)
4: eval(ei, envir)
5: withVisible(eval(ei, envir))
6: source(output, echo = TRUE)
7: doTryCatch(return(expr), name, parentenv, handler)
8: tryCatchOne(expr, names, parentenv, handlers[[1L]])
9: tryCatchList(expr, classes, parentenv, handlers)
10: tryCatch({ source(output, echo = TRUE)}, error = function(e) { cat("\n When sourcing ", sQuote(output), ":\n", sep = "") stop(conditionMessage(e), call. = FALSE, domain = NA)})
11: tools:::.run_one_vignette("libcoin.Rnw", "/data/gannet/ripley/R/packages/tests-devel/libcoin/vignettes", encoding = "UTF-8", pkgdir = "/data/gannet/ripley/R/packages/tests-devel/libcoin")
An irrecoverable exception occurred. R is aborting now ...
... incomplete output. Crash?
‘libcoin.Rnw’ using ‘UTF-8’... failed to complete the test
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building ‘libcoin.Rnw’ using Sweave
*** caught segfault ***
address 0x1f8edfa9c, cause 'memory not mapped'
Traceback:
1: .LinStatExpCov2d(X = X, Y = Y, ix = ix, iy = iy, weights = weights, subset = subset, block = block, varonly = varonly, checkNAs = checkNAs, nresample = nresample, standardise = standardise, tol = tol)
2: LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy, weights = weights, subset = subset, nresample = 10)
3: eval(expr, .GlobalEnv)
4: eval(expr, .GlobalEnv)
5: withVisible(eval(expr, .GlobalEnv))
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
10: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
11: evalFunc(ce, options)
12: tryCatchList(expr, classes, parentenv, handlers)
13: tryCatch(evalFunc(ce, options), finally = { cat("\n") sink()})
14: driver$runcode(drobj, chunk, chunkopts)
15: utils::Sweave(...)
16: engine$weave(file, quiet = quiet, encoding = enc)
17: doTryCatch(return(expr), name, parentenv, handler)
18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
19: tryCatchList(expr, classes, parentenv, handlers)
20: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) } outputs <- c(outputs, output)}, error = function(e) { thisOK <<- FALSE fails <<- c(fails, file) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
21: tools:::buildVignettes(dir = "/data/gannet/ripley/R/packages/tests-devel/libcoin.Rcheck/vign_test/libcoin", ser_elibs = "/tmp/RtmpR9WHAM/file36a9181fe71f08.rds")
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.0-5
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in 'libcoin.Rnw'
> isequal <- function(a, b) {
+ attributes(a) <- NULL
+ attributes(b) <- NULL
+ if (!isTRUE(all.equal(a, b))) {
+ print(a, digits .... [TRUNCATED]
> library("libcoin")
> set.seed(290875)
> x <- gl(5, 20)
> y <- round(runif(length(x)), 1)
> ls1 <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1))
> ls1$LinearStatistic
[1] 8.8 9.5 10.3 9.8 10.5
> tapply(y, x, sum)
1 2 3 4 5
8.8 9.5 10.3 9.8 10.5
> ls2 <- LinStatExpCov(X = x, Y = matrix(y, ncol = 1))
> all.equal(ls1[-grep("Xfactor", names(ls1))], ls2[-grep("Xfactor",
+ names(ls2))])
[1] TRUE
> X <- rbind(0, diag(nlevels(x)))
> ix <- unclass(x)
> ylev <- sort(unique(y))
> Y <- rbind(0, matrix(ylev, ncol = 1))
> iy <- .bincode(y, breaks = c(-Inf, ylev, Inf))
> ls3 <- LinStatExpCov(X = X, ix = ix, Y = Y, iy = iy)
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls3 <- LinStatExpCov(X = X, ix = factor(ix), Y = Y,
+ iy = factor(iy))
> all.equal(ls1[-grep("Table", names(ls1))], ls3[-grep("Table",
+ names(ls3))])
[1] TRUE
> ls4 <- LinStatExpCov(ix = ix, Y = Y, iy = iy)
> all.equal(ls3[-grep("Xfactor", names(ls3))], ls4[-grep("Xfactor",
+ names(ls4))])
[1] TRUE
> ls3$Table
, , 1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 4 4 1 2 3 0 1 2 3 0
[3,] 0 2 2 1 2 2 5 0 1 1 3 1
[4,] 0 1 1 4 0 1 5 2 0 2 3 1
[5,] 0 0 2 2 4 2 2 1 3 2 1 1
[6,] 0 1 3 1 1 1 2 2 2 6 1 0
> xtabs(~ix + iy)
iy
ix 1 2 3 4 5 6 7 8 9 10 11
1 0 4 4 1 2 3 0 1 2 3 0
2 2 2 1 2 2 5 0 1 1 3 1
3 1 1 4 0 1 5 2 0 2 3 1
4 0 2 2 4 2 2 1 3 2 1 1
5 1 3 1 1 1 2 2 2 6 1 0
> ls1$Covariance
[1] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
[7] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[13] 1.3572364 -0.3393091 1.3572364
> vcov(ls1)
[,1] [,2] [,3] [,4] [,5]
[1,] 1.3572364 -0.3393091 -0.3393091 -0.3393091 -0.3393091
[2,] -0.3393091 1.3572364 -0.3393091 -0.3393091 -0.3393091
[3,] -0.3393091 -0.3393091 1.3572364 -0.3393091 -0.3393091
[4,] -0.3393091 -0.3393091 -0.3393091 1.3572364 -0.3393091
[5,] -0.3393091 -0.3393091 -0.3393091 -0.3393091 1.3572364
> doTest(ls1, teststat = "maximum", pvalue = FALSE)
$TestStatistic
[1] 0.8411982
$p.value
[1] NA
> doTest(ls1, teststat = "maximum")
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.8852087
> doTest(ls1, teststat = "maximum", log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.108822
> doTest(ls1, teststat = "maximum", lower = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] 0.1150168
> doTest(ls1, teststat = "maximum", lower = TRUE, log = TRUE)
$TestStatistic
[1] 0.8411982
$p.value
[1] -2.164164
> doTest(ls1, teststat = "quadratic")
$TestStatistic
[1] 1.077484
$p.value
[1] 0.897828
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(y,
+ ncol = 1), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> set.seed(29)
> ls1d <- LinStatExpCov(X = model.matrix(~x - 1), Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> set.seed(29)
> ls1s <- LinStatExpCov(X = as.double(1:5)[x], Y = matrix(c(y,
+ y), ncol = 2), nresample = 10, standardise = TRUE)
> ls1c <- lmult(c(1:5), ls1d)
> stopifnot(isequal(ls1c, ls1s))
> t1 <- ctabs(ix = ix, iy = iy)
> t2 <- xtabs(~ix + iy)
> max(abs(t1[-1, -1] - t2))
[1] 0
> N <- 20
> P <- 3
> Lx <- 10
> Ly <- 5
> Q <- 4
> B <- 2
> iX2d <- rbind(0, matrix(runif(Lx * P), nrow = Lx))
> ix <- sample(1:Lx, size = N, replace = TRUE)
> levels(ix) <- 1:Lx
> ixf <- factor(ix, levels = 1:Lx, labels = 1:Lx)
> x <- iX2d[ix + 1, ]
> Xfactor <- diag(Lx)[ix, ]
> iY2d <- rbind(0, matrix(runif(Ly * Q), nrow = Ly))
> iy <- sample(1:Ly, size = N, replace = TRUE)
> levels(iy) <- 1:Ly
> iyf <- factor(iy, levels = 1:Ly, labels = 1:Ly)
> y <- iY2d[iy + 1, ]
> weights <- sample(0:5, size = N, replace = TRUE)
> block <- sample(gl(B, ceiling(N/B))[1:N])
> subset <- sort(sample(1:N, floor(N * 1.5), replace = TRUE))
> subsety <- sample(1:N, floor(N * 1.5), replace = TRUE)
> r1 <- rep(1:ncol(x), ncol(y))
> r1Xfactor <- rep(1:ncol(Xfactor), ncol(y))
> r2 <- rep(1:ncol(y), each = ncol(x))
> r2Xfactor <- rep(1:ncol(y), each = ncol(Xfactor))
> LECV <- function(X, Y, weights = integer(0), subset = integer(0),
+ block = integer(0)) {
+ if (length(weights) == 0)
+ weights <- .... [TRUNCATED]
> cmpr <- function(ret1, ret2) {
+ if (inherits(ret1, "LinStatExpCov")) {
+ if (!ret1$varonly)
+ ret1$Covariance <- vcov(ret1 .... [TRUNCATED]
> LECVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset)
> LEVxyws <- LinStatExpCov(x, y, weights = weights,
+ subset = subset, varonly = TRUE)
> testit <- function(...) {
+ a <- LinStatExpCov(x, y, ...)
+ b <- LECV(x, y, ...)
+ d <- LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> testit <- function(...) {
+ a <- LinStatExpCov(X = ix, y, ...)
+ b <- LECV(Xfactor, y, ...)
+ d <- LinStatExpCov(X = integer(0), ix = ix .... [TRUNCATED]
> stopifnot(testit() && testit(weights = weights) &&
+ testit(subset = subset) && testit(weights = weights, subset = subset) &&
+ testit(blo .... [TRUNCATED]
> LinStatExpCov(X = iX2d, ix = ix, Y = iY2d, iy = iy,
+ weights = weights, subset = subset, nresample = 10)$PermutedLinearStatistic
... incomplete output. Crash?
'libcoin.Rnw' using 'UTF-8'... failed to complete the test
Flavor: r-devel-windows-ix86+x86_64
Version: 1.0-5
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building 'libcoin.Rnw' using Sweave
Flavor: r-devel-windows-ix86+x86_64
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: NOTE: 1, OK: 11
Version: 1.3-5
Check: Rd cross-references
Result: NOTE
Undeclared package ‘randomForest’ in Rd xrefs
Flavor: r-devel-linux-x86_64-fedora-clang
Current CRAN status: NOTE: 2, OK: 10
Version: 1.2-9
Check: Rd cross-references
Result: NOTE
Undeclared package ‘randomForest’ in Rd xrefs
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2-9
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘RWeka’
Flavor: r-patched-solaris-x86
Version: 1.2-9
Check: running R code from vignettes
Result: NOTE
‘constparty.Rnw’... [12s/14s] NOTE
differences from ‘constparty.Rout.save’
84,85d83
< Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'RWeka'
88c86,87
< n= 2201
---
> J48 pruned tree
> ------------------
90,91c89,102
< node), split, n, loss, yval, (yprob)
< * denotes terminal node
---
> Gender = Male
> | Class = 1st
> | | Age = Child: Yes (5.0)
> | | Age = Adult: No (175.0/57.0)
> | Class = 2nd
> | | Age = Child: Yes (11.0)
> | | Age = Adult: No (168.0/14.0)
> | Class = 3rd: No (510.0/88.0)
> | Class = Crew: No (862.0/192.0)
> Gender = Female
> | Class = 1st: Yes (145.0/4.0)
> | Class = 2nd: Yes (106.0/13.0)
> | Class = 3rd: No (196.0/90.0)
> | Class = Crew: Yes (23.0/3.0)
93,101c104
< 1) root 2201 711 No (0.6769650 0.3230350)
< 2) Gender=Male 1731 367 No (0.7879838 0.2120162)
< 4) Age=Adult 1667 338 No (0.7972406 0.2027594) *
< 5) Age=Child 64 29 No (0.5468750 0.4531250)
< 10) Class=3rd 48 13 No (0.7291667 0.2708333) *
< 11) Class=1st,2nd 16 0 Yes (0.0000000 1.0000000) *
< 3) Gender=Female 470 126 Yes (0.2680851 0.7319149)
< 6) Class=3rd 196 90 No (0.5408163 0.4591837) *
< 7) Class=1st,2nd,Crew 274 20 Yes (0.0729927 0.9270073) *
---
> Number of Leaves : 10
102a106,108
> Size of the tree : 15
>
>
111,117c117,129
< | | [3] Age in Adult: No (n = 1667, err = 20.3%)
< | | [4] Age in Child
< | | | [5] Class in 3rd: No (n = 48, err = 27.1%)
< | | | [6] Class in 1st, 2nd: Yes (n = 16, err = 0.0%)
< | [7] Gender in Female
< | | [8] Class in 3rd: No (n = 196, err = 45.9%)
< | | [9] Class in 1st, 2nd, Crew: Yes (n = 274, err = 7.3%)
---
> | | [3] Class in 1st
> | | | [4] Age in Child: Yes (n = 5, err = 0.0%)
> | | | [5] Age in Adult: No (n = 175, err = 32.6%)
> | | [6] Class in 2nd
> | | | [7] Age in Child: Yes (n = 11, err = 0.0%)
> | | | [8] Age in Adult: No (n = 168, err = 8.3%)
> | | [9] Class in 3rd: No (n = 510, err = 17.3%)
> | | [10] Class in Crew: No (n = 862, err = 22.3%)
> | [11] Gender in Female
> | | [12] Class in 1st: Yes (n = 145, err = 2.8%)
> | | [13] Class in 2nd: Yes (n = 106, err = 12.3%)
> | | [14] Class in 3rd: No (n = 196, err = 45.9%)
> | | [15] Class in Crew: Yes (n = 23, err = 13.0%)
119,120c131,132
< Number of inner nodes: 4
< Number of terminal nodes: 5
---
> Number of inner nodes: 5
> Number of terminal nodes: 10
274,278c286,290
< [1] -409.3583 -379.6102 -424.6888 -400.2546 -387.4208 -390.8659
< [7] -405.2264 -382.5447 -391.3828 -376.3678 -394.3874 -403.8596
< [13] -385.1590 -393.2547 -386.2088 -409.0627 -412.4585 -401.3972
< [19] -403.0548 -406.7881 -389.8507 -408.0418 -392.1441 -389.4687
< [25] -399.4419
---
> [1] -390.2268 -410.8696 -377.7492 -416.7790 -396.5327 -383.9575
> [7] -389.4972 -405.8401 -379.9248 -384.7071 -381.1303 -394.8712
> [13] -409.8711 -385.3753 -396.2723 -385.6372 -408.9187 -412.4585
> [19] -401.3972 -403.0548 -406.7881 -389.8507 -408.0418 -392.1441
> [25] -389.4687
308c320
< Yes Yes No No Yes Yes No Yes No No No No Yes Yes No Yes
---
> Yes Yes No Yes Yes Yes No Yes No No No No Yes Yes No Yes
336c348
< 4 2 1
---
> 4 1 2
‘ctree.Rnw’ using ‘UTF-8’... [22s/27s] OK
‘partykit.Rnw’... OK
Flavor: r-patched-solaris-x86
Current CRAN status: OK: 12
Current CRAN status: NOTE: 7, OK: 5
Version: 1.0-10
Check: installed package size
Result: NOTE
installed size is 8.5Mb
sub-directories of 1Mb or more:
data 1.2Mb
rda 7.1Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Current CRAN status: OK: 12
Current CRAN status: OK: 12
Current CRAN status: OK: 12