CRAN Package Check Results for Package hdi

Last updated on 2019-03-20 12:46:35 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1-6 8.10 180.13 188.23 ERROR
r-devel-linux-x86_64-debian-gcc 0.1-6 7.42 134.97 142.39 ERROR
r-devel-linux-x86_64-fedora-clang 0.1-6 223.16 ERROR
r-devel-linux-x86_64-fedora-gcc 0.1-6 212.71 ERROR
r-devel-windows-ix86+x86_64 0.1-6 19.00 259.00 278.00 ERROR
r-patched-linux-x86_64 0.1-6 5.69 181.07 186.76 OK
r-patched-solaris-x86 0.1-6 352.10 OK
r-release-linux-x86_64 0.1-6 6.15 181.17 187.32 OK
r-release-windows-ix86+x86_64 0.1-6 14.00 263.00 277.00 OK
r-release-osx-x86_64 0.1-6 OK
r-oldrel-windows-ix86+x86_64 0.1-6 8.00 284.00 292.00 OK
r-oldrel-osx-x86_64 0.1-6 OK

Check Details

Version: 0.1-6
Check: tests
Result: ERROR
     Running 'ex-clusterGroupBound.R' [8s/9s]
     Running 'ex-lasso.proj.R' [9s/11s]
     Running 'ex-plotClusterGroupBound.R' [7s/9s]
     Running 'groupTsts.R' [2s/3s]
     Running 'test-lasso.R' [14s/15s]
     Running 'test-multisplit.R' [43s/47s]
     Running 'test-ridge.R' [7s/9s]
     Running 'test-stability.R' [6s/6s]
    Running the tests in 'tests/test-lasso.R' failed.
    Complete output:
     > #####################################
     > ## Load stuff for testing purposes ##
     > #####################################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > data(riboflavin)
     >
     > x <- riboflavin[,-1]
     > y <- riboflavin[,1]
     >
     > dim(x)
     [1] 71 4088
     > ##- [1] 71 4088
     > length(y)
     [1] 71
     > ##- [1] 71
     >
     > doExtras <- interactive() # i.e., FALSE for routine R CMD check
     >
     > p. <- if(doExtras) 50 else 16 # smaller for speed
     > p.
     [1] 16
     >
     > x.use <- x[,1:p.]
     >
     > ######################
     > ## Lasso projection ##
     > ######################
     >
     > ## set seed because of cv
     > set.seed(3) ; fit.lasso <- lasso.proj(x = x.use, y = y)
     > ## Check standardization, i.e., equivariance :
     > set.seed(3) ; fit.lasso2 <- lasso.proj(x = 2 + 4 * x.use, y = y)
     >
     > ## verbose
     > ncores <- if(.Platform$OS.type == "windows") 1 else getOption("mc.cores", 2L)
     >
     > set.seed(3) ; fit.tmp <- lasso.proj(x = x.use, y = y, verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     > set.seed(3) ; fit.tmp2 <- lasso.proj(x = x.use, y = y,
     + parallel = TRUE, ncores = ncores,
     + verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     >
     > ## confidence intervals
     > ci.lasso <- confint(fit.lasso, level = 0.95)
     > ci.lasso2 <- confint(fit.lasso2, level = 0.95)
     >
     > stopifnot(
     + all.equal(fit.lasso$pval, fit.lasso2$pval)
     + ,
     + all.equal(c(0,0), range(fit.lasso$bhat / fit.lasso2$bhat - 4))
     + ,
     + all.equal(ci.lasso, ci.lasso2 * 4)
     + ,
     + TRUE)
     >
     > if(!doExtras) {
     + stopifnot(
     + all.equal(as.vector(fit.lasso$bhat),
     + c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501,
     + -0.63576549, -0.095448048, 0.40801737, -1.2194818, -0.11113313,
     + 0.3474404, 1.1425587, -0.54460967, 0.45298509, -0.31922868, 0.42184791),
     + tol = 4e-7)# 1e-8
     + ,
     + all.equal(unname(ci.lasso),
     + matrix(c(-0.186587, -1.88574, -1.03822, -0.274364, -0.808168, -1.30643,
     + -0.994648, -0.446016, -2.29657, -1.23525, -0.728214, 0.256838,
     + -1.297, -0.416467, -1.16564, -0.593977, 1.27959, 0.598448,
     + 1.19786, 0.802489, 0.380058, 0.0348979, 0.803752, 1.26205,
     + -0.142394, 1.01298, 1.42309, 2.02828, 0.207783, 1.32244,
     + 0.527183, 1.43767),
     + 16, 2), tol = 5e-5)
     + )
     + }
     Error: as.vector(fit.lasso$bhat) and c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501, .... are not equal:
     Mean relative difference: 0.01599114
     Execution halted
    Running the tests in 'tests/test-multisplit.R' failed.
    Complete output:
     > #################
     > ## multi-split ##
     > #################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > set.seed(123)
     >
     > x <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
     > y <- x[,1] + x[,2] + rnorm(100)
     >
     > set.seed(3) ; fit.mult <- multi.split(x, y)
     > set.seed(3) ; fit.tmp <- multi.split(x, y, verbose = TRUE)
     ...Split 1
     ...Split 2
     ...Split 3
     ...Split 4
     ...Split 5
     ...Split 6
     ...Split 7
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     ...Split 9
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     ...Split 99
     ...Split 100
     >
     > ## dput(fit.mult$pval.corr)
     > stopifnot(all.equal(fit.mult$pval.corr,c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1)))
     Error: fit.mult$pval.corr and c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1, .... are not equal:
     Mean relative difference: 0.7118953
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.1-6
Check: tests
Result: ERROR
     Running ‘ex-clusterGroupBound.R’ [5s/8s]
     Running ‘ex-lasso.proj.R’ [6s/9s]
     Running ‘ex-plotClusterGroupBound.R’ [5s/7s]
     Running ‘groupTsts.R’ [2s/3s]
     Running ‘test-lasso.R’ [10s/12s]
     Running ‘test-multisplit.R’ [31s/47s]
     Running ‘test-ridge.R’ [6s/8s]
     Running ‘test-stability.R’ [5s/6s]
    Running the tests in ‘tests/test-lasso.R’ failed.
    Complete output:
     > #####################################
     > ## Load stuff for testing purposes ##
     > #####################################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > data(riboflavin)
     >
     > x <- riboflavin[,-1]
     > y <- riboflavin[,1]
     >
     > dim(x)
     [1] 71 4088
     > ##- [1] 71 4088
     > length(y)
     [1] 71
     > ##- [1] 71
     >
     > doExtras <- interactive() # i.e., FALSE for routine R CMD check
     >
     > p. <- if(doExtras) 50 else 16 # smaller for speed
     > p.
     [1] 16
     >
     > x.use <- x[,1:p.]
     >
     > ######################
     > ## Lasso projection ##
     > ######################
     >
     > ## set seed because of cv
     > set.seed(3) ; fit.lasso <- lasso.proj(x = x.use, y = y)
     > ## Check standardization, i.e., equivariance :
     > set.seed(3) ; fit.lasso2 <- lasso.proj(x = 2 + 4 * x.use, y = y)
     >
     > ## verbose
     > ncores <- if(.Platform$OS.type == "windows") 1 else getOption("mc.cores", 2L)
     >
     > set.seed(3) ; fit.tmp <- lasso.proj(x = x.use, y = y, verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     > set.seed(3) ; fit.tmp2 <- lasso.proj(x = x.use, y = y,
     + parallel = TRUE, ncores = ncores,
     + verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     >
     > ## confidence intervals
     > ci.lasso <- confint(fit.lasso, level = 0.95)
     > ci.lasso2 <- confint(fit.lasso2, level = 0.95)
     >
     > stopifnot(
     + all.equal(fit.lasso$pval, fit.lasso2$pval)
     + ,
     + all.equal(c(0,0), range(fit.lasso$bhat / fit.lasso2$bhat - 4))
     + ,
     + all.equal(ci.lasso, ci.lasso2 * 4)
     + ,
     + TRUE)
     >
     > if(!doExtras) {
     + stopifnot(
     + all.equal(as.vector(fit.lasso$bhat),
     + c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501,
     + -0.63576549, -0.095448048, 0.40801737, -1.2194818, -0.11113313,
     + 0.3474404, 1.1425587, -0.54460967, 0.45298509, -0.31922868, 0.42184791),
     + tol = 4e-7)# 1e-8
     + ,
     + all.equal(unname(ci.lasso),
     + matrix(c(-0.186587, -1.88574, -1.03822, -0.274364, -0.808168, -1.30643,
     + -0.994648, -0.446016, -2.29657, -1.23525, -0.728214, 0.256838,
     + -1.297, -0.416467, -1.16564, -0.593977, 1.27959, 0.598448,
     + 1.19786, 0.802489, 0.380058, 0.0348979, 0.803752, 1.26205,
     + -0.142394, 1.01298, 1.42309, 2.02828, 0.207783, 1.32244,
     + 0.527183, 1.43767),
     + 16, 2), tol = 5e-5)
     + )
     + }
     Error: as.vector(fit.lasso$bhat) and c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501, .... are not equal:
     Mean relative difference: 0.01599114
     Execution halted
    Running the tests in ‘tests/test-multisplit.R’ failed.
    Complete output:
     > #################
     > ## multi-split ##
     > #################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > set.seed(123)
     >
     > x <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
     > y <- x[,1] + x[,2] + rnorm(100)
     >
     > set.seed(3) ; fit.mult <- multi.split(x, y)
     > set.seed(3) ; fit.tmp <- multi.split(x, y, verbose = TRUE)
     ...Split 1
     ...Split 2
     ...Split 3
     ...Split 4
     ...Split 5
     ...Split 6
     ...Split 7
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     >
     > ## dput(fit.mult$pval.corr)
     > stopifnot(all.equal(fit.mult$pval.corr,c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1)))
     Error: fit.mult$pval.corr and c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1, .... are not equal:
     Mean relative difference: 0.7118953
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.1-6
Check: tests
Result: ERROR
     Running ‘ex-clusterGroupBound.R’
     Running ‘ex-lasso.proj.R’ [10s/11s]
     Running ‘ex-plotClusterGroupBound.R’ [9s/10s]
     Running ‘groupTsts.R’
     Running ‘test-lasso.R’ [15s/14s]
     Running ‘test-multisplit.R’ [48s/56s]
     Running ‘test-ridge.R’ [9s/11s]
     Running ‘test-stability.R’
    Running the tests in ‘tests/test-lasso.R’ failed.
    Complete output:
     > #####################################
     > ## Load stuff for testing purposes ##
     > #####################################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > data(riboflavin)
     >
     > x <- riboflavin[,-1]
     > y <- riboflavin[,1]
     >
     > dim(x)
     [1] 71 4088
     > ##- [1] 71 4088
     > length(y)
     [1] 71
     > ##- [1] 71
     >
     > doExtras <- interactive() # i.e., FALSE for routine R CMD check
     >
     > p. <- if(doExtras) 50 else 16 # smaller for speed
     > p.
     [1] 16
     >
     > x.use <- x[,1:p.]
     >
     > ######################
     > ## Lasso projection ##
     > ######################
     >
     > ## set seed because of cv
     > set.seed(3) ; fit.lasso <- lasso.proj(x = x.use, y = y)
     > ## Check standardization, i.e., equivariance :
     > set.seed(3) ; fit.lasso2 <- lasso.proj(x = 2 + 4 * x.use, y = y)
     >
     > ## verbose
     > ncores <- if(.Platform$OS.type == "windows") 1 else getOption("mc.cores", 2L)
     >
     > set.seed(3) ; fit.tmp <- lasso.proj(x = x.use, y = y, verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     > set.seed(3) ; fit.tmp2 <- lasso.proj(x = x.use, y = y,
     + parallel = TRUE, ncores = ncores,
     + verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     >
     > ## confidence intervals
     > ci.lasso <- confint(fit.lasso, level = 0.95)
     > ci.lasso2 <- confint(fit.lasso2, level = 0.95)
     >
     > stopifnot(
     + all.equal(fit.lasso$pval, fit.lasso2$pval)
     + ,
     + all.equal(c(0,0), range(fit.lasso$bhat / fit.lasso2$bhat - 4))
     + ,
     + all.equal(ci.lasso, ci.lasso2 * 4)
     + ,
     + TRUE)
     >
     > if(!doExtras) {
     + stopifnot(
     + all.equal(as.vector(fit.lasso$bhat),
     + c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501,
     + -0.63576549, -0.095448048, 0.40801737, -1.2194818, -0.11113313,
     + 0.3474404, 1.1425587, -0.54460967, 0.45298509, -0.31922868, 0.42184791),
     + tol = 4e-7)# 1e-8
     + ,
     + all.equal(unname(ci.lasso),
     + matrix(c(-0.186587, -1.88574, -1.03822, -0.274364, -0.808168, -1.30643,
     + -0.994648, -0.446016, -2.29657, -1.23525, -0.728214, 0.256838,
     + -1.297, -0.416467, -1.16564, -0.593977, 1.27959, 0.598448,
     + 1.19786, 0.802489, 0.380058, 0.0348979, 0.803752, 1.26205,
     + -0.142394, 1.01298, 1.42309, 2.02828, 0.207783, 1.32244,
     + 0.527183, 1.43767),
     + 16, 2), tol = 5e-5)
     + )
     + }
     Error: as.vector(fit.lasso$bhat) and c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501, .... are not equal:
     Mean relative difference: 0.01599114
     Execution halted
    Running the tests in ‘tests/test-multisplit.R’ failed.
    Complete output:
     > #################
     > ## multi-split ##
     > #################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > set.seed(123)
     >
     > x <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
     > y <- x[,1] + x[,2] + rnorm(100)
     >
     > set.seed(3) ; fit.mult <- multi.split(x, y)
     > set.seed(3) ; fit.tmp <- multi.split(x, y, verbose = TRUE)
     ...Split 1
     ...Split 2
     ...Split 3
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     >
     > ## dput(fit.mult$pval.corr)
     > stopifnot(all.equal(fit.mult$pval.corr,c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1)))
     Error: fit.mult$pval.corr and c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1, .... are not equal:
     Mean relative difference: 0.7118953
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1-6
Check: tests
Result: ERROR
     Running ‘ex-clusterGroupBound.R’
     Running ‘ex-lasso.proj.R’
     Running ‘ex-plotClusterGroupBound.R’
     Running ‘groupTsts.R’
     Running ‘test-lasso.R’ [15s/16s]
     Running ‘test-multisplit.R’ [47s/54s]
     Running ‘test-ridge.R’
     Running ‘test-stability.R’
    Running the tests in ‘tests/test-lasso.R’ failed.
    Complete output:
     > #####################################
     > ## Load stuff for testing purposes ##
     > #####################################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > data(riboflavin)
     >
     > x <- riboflavin[,-1]
     > y <- riboflavin[,1]
     >
     > dim(x)
     [1] 71 4088
     > ##- [1] 71 4088
     > length(y)
     [1] 71
     > ##- [1] 71
     >
     > doExtras <- interactive() # i.e., FALSE for routine R CMD check
     >
     > p. <- if(doExtras) 50 else 16 # smaller for speed
     > p.
     [1] 16
     >
     > x.use <- x[,1:p.]
     >
     > ######################
     > ## Lasso projection ##
     > ######################
     >
     > ## set seed because of cv
     > set.seed(3) ; fit.lasso <- lasso.proj(x = x.use, y = y)
     > ## Check standardization, i.e., equivariance :
     > set.seed(3) ; fit.lasso2 <- lasso.proj(x = 2 + 4 * x.use, y = y)
     >
     > ## verbose
     > ncores <- if(.Platform$OS.type == "windows") 1 else getOption("mc.cores", 2L)
     >
     > set.seed(3) ; fit.tmp <- lasso.proj(x = x.use, y = y, verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     > set.seed(3) ; fit.tmp2 <- lasso.proj(x = x.use, y = y,
     + parallel = TRUE, ncores = ncores,
     + verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     >
     > ## confidence intervals
     > ci.lasso <- confint(fit.lasso, level = 0.95)
     > ci.lasso2 <- confint(fit.lasso2, level = 0.95)
     >
     > stopifnot(
     + all.equal(fit.lasso$pval, fit.lasso2$pval)
     + ,
     + all.equal(c(0,0), range(fit.lasso$bhat / fit.lasso2$bhat - 4))
     + ,
     + all.equal(ci.lasso, ci.lasso2 * 4)
     + ,
     + TRUE)
     >
     > if(!doExtras) {
     + stopifnot(
     + all.equal(as.vector(fit.lasso$bhat),
     + c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501,
     + -0.63576549, -0.095448048, 0.40801737, -1.2194818, -0.11113313,
     + 0.3474404, 1.1425587, -0.54460967, 0.45298509, -0.31922868, 0.42184791),
     + tol = 4e-7)# 1e-8
     + ,
     + all.equal(unname(ci.lasso),
     + matrix(c(-0.186587, -1.88574, -1.03822, -0.274364, -0.808168, -1.30643,
     + -0.994648, -0.446016, -2.29657, -1.23525, -0.728214, 0.256838,
     + -1.297, -0.416467, -1.16564, -0.593977, 1.27959, 0.598448,
     + 1.19786, 0.802489, 0.380058, 0.0348979, 0.803752, 1.26205,
     + -0.142394, 1.01298, 1.42309, 2.02828, 0.207783, 1.32244,
     + 0.527183, 1.43767),
     + 16, 2), tol = 5e-5)
     + )
     + }
     Error: as.vector(fit.lasso$bhat) and c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501, .... are not equal:
     Mean relative difference: 0.01599114
     Execution halted
    Running the tests in ‘tests/test-multisplit.R’ failed.
    Complete output:
     > #################
     > ## multi-split ##
     > #################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > set.seed(123)
     >
     > x <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
     > y <- x[,1] + x[,2] + rnorm(100)
     >
     > set.seed(3) ; fit.mult <- multi.split(x, y)
     > set.seed(3) ; fit.tmp <- multi.split(x, y, verbose = TRUE)
     ...Split 1
     ...Split 2
     ...Split 3
     ...Split 4
     ...Split 5
     ...Split 6
     ...Split 7
     ...Split 8
     ...Split 9
     ...Split 10
     ...Split 11
     ...Split 12
     ...Split 13
     ...Split 14
     ...Split 15
     ...Split 16
     ...Split 17
     ...Split 18
     ...Split 19
     ...Split 20
     ...Split 21
     ...Split 22
     ...Split 23
     ...Split 24
     ...Split 25
     ...Split 26
     ...Split 27
     ...Split 28
     ...Split 29
     ...Split 30
     ...Split 31
     ...Split 32
     ...Split 33
     ...Split 34
     ...Split 35
     ...Split 36
     ...Split 37
     ...Split 38
     ...Split 39
     ...Split 40
     ...Split 41
     ...Split 42
     ...Split 43
     ...Split 44
     ...Split 45
     ...Split 46
     ...Split 47
     ...Split 48
     ...Split 49
     ...Split 50
     ...Split 51
     ...Split 52
     ...Split 53
     ...Split 54
     ...Split 55
     ...Split 56
     ...Split 57
     ...Split 58
     ...Split 59
     ...Split 60
     ...Split 61
     ...Split 62
     ...Split 63
     ...Split 64
     ...Split 65
     ...Split 66
     ...Split 67
     ...Split 68
     ...Split 69
     ...Split 70
     ...Split 71
     ...Split 72
     ...Split 73
     ...Split 74
     ...Split 75
     ...Split 76
     ...Split 77
     ...Split 78
     ...Split 79
     ...Split 80
     ...Split 81
     ...Split 82
     ...Split 83
     ...Split 84
     ...Split 85
     ...Split 86
     ...Split 87
     ...Split 88
     ...Split 89
     ...Split 90
     ...Split 91
     ...Split 92
     ...Split 93
     ...Split 94
     ...Split 95
     ...Split 96
     ...Split 97
     ...Split 98
     ...Split 99
     ...Split 100
     >
     > ## dput(fit.mult$pval.corr)
     > stopifnot(all.equal(fit.mult$pval.corr,c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1)))
     Error: fit.mult$pval.corr and c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1, .... are not equal:
     Mean relative difference: 0.7118953
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1-6
Check: tests
Result: ERROR
     Running 'ex-clusterGroupBound.R' [16s]
     Running 'ex-lasso.proj.R' [12s]
     Running 'ex-plotClusterGroupBound.R' [16s]
     Running 'groupTsts.R' [3s]
     Running 'test-lasso.R' [17s]
     Running 'test-multisplit.R' [54s]
     Running 'test-ridge.R' [8s]
     Running 'test-stability.R' [7s]
    Running the tests in 'tests/test-lasso.R' failed.
    Complete output:
     > #####################################
     > ## Load stuff for testing purposes ##
     > #####################################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > data(riboflavin)
     >
     > x <- riboflavin[,-1]
     > y <- riboflavin[,1]
     >
     > dim(x)
     [1] 71 4088
     > ##- [1] 71 4088
     > length(y)
     [1] 71
     > ##- [1] 71
     >
     > doExtras <- interactive() # i.e., FALSE for routine R CMD check
     >
     > p. <- if(doExtras) 50 else 16 # smaller for speed
     > p.
     [1] 16
     >
     > x.use <- x[,1:p.]
     >
     > ######################
     > ## Lasso projection ##
     > ######################
     >
     > ## set seed because of cv
     > set.seed(3) ; fit.lasso <- lasso.proj(x = x.use, y = y)
     > ## Check standardization, i.e., equivariance :
     > set.seed(3) ; fit.lasso2 <- lasso.proj(x = 2 + 4 * x.use, y = y)
     >
     > ## verbose
     > ncores <- if(.Platform$OS.type == "windows") 1 else getOption("mc.cores", 2L)
     >
     > set.seed(3) ; fit.tmp <- lasso.proj(x = x.use, y = y, verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     > set.seed(3) ; fit.tmp2 <- lasso.proj(x = x.use, y = y,
     + parallel = TRUE, ncores = ncores,
     + verbose = TRUE)
     The expensive computation is now 25% done
     The expensive computation is now 50% done
     The expensive computation is now 75% done
     The expensive computation is now 100% done
     >
     > ## confidence intervals
     > ci.lasso <- confint(fit.lasso, level = 0.95)
     > ci.lasso2 <- confint(fit.lasso2, level = 0.95)
     >
     > stopifnot(
     + all.equal(fit.lasso$pval, fit.lasso2$pval)
     + ,
     + all.equal(c(0,0), range(fit.lasso$bhat / fit.lasso2$bhat - 4))
     + ,
     + all.equal(ci.lasso, ci.lasso2 * 4)
     + ,
     + TRUE)
     >
     > if(!doExtras) {
     + stopifnot(
     + all.equal(as.vector(fit.lasso$bhat),
     + c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501,
     + -0.63576549, -0.095448048, 0.40801737, -1.2194818, -0.11113313,
     + 0.3474404, 1.1425587, -0.54460967, 0.45298509, -0.31922868, 0.42184791),
     + tol = 4e-7)# 1e-8
     + ,
     + all.equal(unname(ci.lasso),
     + matrix(c(-0.186587, -1.88574, -1.03822, -0.274364, -0.808168, -1.30643,
     + -0.994648, -0.446016, -2.29657, -1.23525, -0.728214, 0.256838,
     + -1.297, -0.416467, -1.16564, -0.593977, 1.27959, 0.598448,
     + 1.19786, 0.802489, 0.380058, 0.0348979, 0.803752, 1.26205,
     + -0.142394, 1.01298, 1.42309, 2.02828, 0.207783, 1.32244,
     + 0.527183, 1.43767),
     + 16, 2), tol = 5e-5)
     + )
     + }
     Error: as.vector(fit.lasso$bhat) and c(0.54650099, -0.64364814, 0.079821945, 0.26406221, -0.21405501, .... are not equal:
     Mean relative difference: 0.01599114
     Execution halted
    Running the tests in 'tests/test-multisplit.R' failed.
    Complete output:
     > #################
     > ## multi-split ##
     > #################
     >
     > library(hdi)
     Loading required package: scalreg
     Loading required package: lars
     Loaded lars 1.2
    
     >
     > set.seed(123)
     >
     > x <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
     > y <- x[,1] + x[,2] + rnorm(100)
     >
     > set.seed(3) ; fit.mult <- multi.split(x, y)
     > set.seed(3) ; fit.tmp <- multi.split(x, y, verbose = TRUE)
     ...Split 1
     ...Split 2
     ...Split 3
     ...Split 4
     ...Split 5
     ...Split 6
     ...Split 7
     ...Split 8
     ...Split 9
     ...Split 10
     ...Split 11
     ...Split 12
     ...Split 13
     ...Split 14
     ...Split 15
     ...Split 16
     ...Split 17
     ...Split 18
     ...Split 19
     ...Split 20
     ...Split 21
     ...Split 22
     ...Split 23
     ...Split 24
     ...Split 25
     ...Split 26
     ...Split 27
     ...Split 28
     ...Split 29
     ...Split 30
     ...Split 31
     ...Split 32
     ...Split 33
     ...Split 34
     ...Split 35
     ...Split 36
     ...Split 37
     ...Split 38
     ...Split 39
     ...Split 40
     ...Split 41
     ...Split 42
     ...Split 43
     ...Split 44
     ...Split 45
     ...Split 46
     ...Split 47
     ...Split 48
     ...Split 49
     ...Split 50
     ...Split 51
     ...Split 52
     ...Split 53
     ...Split 54
     ...Split 55
     ...Split 56
     ...Split 57
     ...Split 58
     ...Split 59
     ...Split 60
     ...Split 61
     ...Split 62
     ...Split 63
     ...Split 64
     ...Split 65
     ...Split 66
     ...Split 67
     ...Split 68
     ...Split 69
     ...Split 70
     ...Split 71
     ...Split 72
     ...Split 73
     ...Split 74
     ...Split 75
     ...Split 76
     ...Split 77
     ...Split 78
     ...Split 79
     ...Split 80
     ...Split 81
     ...Split 82
     ...Split 83
     ...Split 84
     ...Split 85
     ...Split 86
     ...Split 87
     ...Split 88
     ...Split 89
     ...Split 90
     ...Split 91
     ...Split 92
     ...Split 93
     ...Split 94
     ...Split 95
     ...Split 96
     ...Split 97
     ...Split 98
     ...Split 99
     ...Split 100
     >
     > ## dput(fit.mult$pval.corr)
     > stopifnot(all.equal(fit.mult$pval.corr,c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
     + 1, 1, 1, 1, 1, 1, 1, 1, 1)))
     Error: fit.mult$pval.corr and c(2.19211217621905e-10, 2.63511914584751e-08, 1, 1, 1, 1, 1, .... are not equal:
     Mean relative difference: 0.7118953
     Execution halted
Flavor: r-devel-windows-ix86+x86_64