CRAN Package Check Results for Package doBy

Last updated on 2016-02-11 21:46:41.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-gcc 4.5-14 3.30 50.14 53.44 OK
r-devel-linux-x86_64-fedora-clang 4.5-14 103.02 OK
r-devel-linux-x86_64-fedora-gcc 4.5-14 107.64 OK
r-devel-osx-x86_64-clang 4.5-14 119.79 OK
r-devel-windows-ix86+x86_64 4.5-14 13.00 90.00 103.00 OK
r-patched-linux-x86_64 4.5-14 3.45 58.77 62.22 OK
r-patched-solaris-sparc 4.5-14 713.90 OK
r-patched-solaris-x86 4.5-14 150.70 OK
r-release-linux-x86_64 4.5-14 3.69 58.47 62.16 OK
r-release-osx-x86_64-mavericks 4.5-14 OK
r-release-windows-ix86+x86_64 4.5-14 13.00 99.00 112.00 OK
r-oldrel-windows-ix86+x86_64 4.5-14 17.00 84.00 101.00 ERROR

Check Details

Version: 4.5-14
Check: examples
Result: ERROR
    Running examples in 'doBy-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: LSmeans
    > ### Title: Compute linear estimates, including LS-means (aka population
    > ### means or marginal means)
    > ### Aliases: LSmeans LSmeans.default LSmeans.lmerMod popMeans
    > ### popMeans.default popMeans.lmerMod
    > ### Keywords: utilities
    >
    > ### ** Examples
    >
    >
    > ## Two way anova:
    >
    > data(warpbreaks)
    >
    > m0 <- lm(breaks ~ wool + tension, data=warpbreaks)
    > m1 <- lm(breaks ~ wool * tension, data=warpbreaks)
    > LSmeans(m0)
     estimate se df t.stat p.value
    estimate 28.14815 1.580892 50 17.80524 2.739005e-23
    > LSmeans(m1)
     estimate se df t.stat p.value
    estimate 28.14815 1.488784 48 18.9068 6.984096e-24
    >
    > ## same as:
    > K <- LSmatrix(m0);K
     (Intercept) woolB tensionM tensionH
    [1,] 1 0.5 0.3333333 0.3333333
    > linest(m0, K)
     estimate se df t.stat p.value
    estimate 28.14815 1.580892 50 17.80524 2.739005e-23
    > K <- LSmatrix(m1);K
     (Intercept) woolB tensionM tensionH woolB:tensionM woolB:tensionH
    [1,] 1 0.5 0.3333333 0.3333333 0.1666667 0.1666667
    > linest(m1, K)
     estimate se df t.stat p.value
    estimate 28.14815 1.488784 48 18.9068 6.984096e-24
    >
    > LSmatrix(m0, effect="wool")
     (Intercept) woolB tensionM tensionH
    [1,] 1 0 0.3333333 0.3333333
    [2,] 1 1 0.3333333 0.3333333
    > LSmeans(m0, effect="wool")
     estimate se df t.stat p.value wool
    1 31.03704 2.235718 50 13.88236 8.807471e-19 A
    2 25.25926 2.235718 50 11.29805 2.260530e-15 B
    >
    > LSmatrix(m1, effect="wool")
     (Intercept) woolB tensionM tensionH woolB:tensionM woolB:tensionH
    [1,] 1 0 0.3333333 0.3333333 0.0000000 0.0000000
    [2,] 1 1 0.3333333 0.3333333 0.3333333 0.3333333
    > LSmeans(m1, effect="wool")
     estimate se df t.stat p.value wool
    1 31.03704 2.105459 48 14.74122 1.907428e-19 A
    2 25.25926 2.105459 48 11.99703 4.711145e-16 B
    >
    > LSmatrix(m0, effect=c("wool","tension"))
     (Intercept) woolB tensionM tensionH
    [1,] 1 0 0 0
    [2,] 1 1 0 0
    [3,] 1 0 1 0
    [4,] 1 1 1 0
    [5,] 1 0 0 1
    [6,] 1 1 0 1
    > LSmeans(m0, effect=c("wool","tension"))
     estimate se df t.stat p.value wool tension
    1 39.27778 3.161783 50 12.422667 6.681866e-17 A L
    2 33.50000 3.161783 50 10.595287 2.216084e-14 B L
    3 29.27778 3.161783 50 9.259894 2.001692e-12 A M
    4 23.50000 3.161783 50 7.432515 1.266105e-09 B M
    5 24.55556 3.161783 50 7.766363 3.827873e-10 A H
    6 18.77778 3.161783 50 5.938984 2.722531e-07 B H
    >
    > LSmatrix(m1, effect=c("wool","tension"))
     (Intercept) woolB tensionM tensionH woolB:tensionM woolB:tensionH
    [1,] 1 0 0 0 0 0
    [2,] 1 1 0 0 0 0
    [3,] 1 0 1 0 0 0
    [4,] 1 1 1 0 1 0
    [5,] 1 0 0 1 0 0
    [6,] 1 1 0 1 0 1
    > LSmeans(m1, effect=c("wool","tension"))
     estimate se df t.stat p.value wool tension
    1 44.55556 3.646761 48 12.217842 2.425903e-16 A L
    2 28.22222 3.646761 48 7.738982 5.472902e-10 B L
    3 24.00000 3.646761 48 6.581182 3.228357e-08 A M
    4 28.77778 3.646761 48 7.891325 3.215188e-10 B M
    5 24.55556 3.646761 48 6.733524 1.884531e-08 A H
    6 18.77778 3.646761 48 5.149166 4.841542e-06 B H
    >
    >
    > ## Regression; two parallel regression lines:
    >
    > data(Puromycin)
    >
    > m0 <- lm(rate ~ state + log(conc), data=Puromycin)
    > ## Can not use LSmeans / LSmatrix here because of
    > ## the log-transformation. Instead we must do:
    > Puromycin$lconc <- log( Puromycin$conc )
    > m1 <- lm(rate ~ state + lconc, data=Puromycin)
    >
    > LSmatrix(m1)
     (Intercept) stateuntreated lconc
    [1,] 1 0.5 -1.905247
    > LSmeans(m1)
     estimate se df t.stat p.value
    estimate 126.2787 2.373818 20 53.19645 5.122112e-23
    >
    > LSmatrix(m1, effect="state")
     (Intercept) stateuntreated lconc
    [1,] 1 0 -1.905247
    [2,] 1 1 -1.905247
    > LSmeans(m1, effect="state")
     estimate se df t.stat p.value state lconc
    1 138.8689 3.286766 20 42.25093 4.936118e-21 treated -1.905247
    2 113.6884 3.433246 20 33.11397 6.041425e-19 untreated -1.905247
    >
    > LSmatrix(m1, effect="state", at=list(lconc=3))
     (Intercept) stateuntreated lconc
    [1,] 1 0 3
    [2,] 1 1 3
    > LSmeans(m1, effect="state", at=list(lconc=3))
     estimate se df t.stat p.value state lconc
    1 298.6019 9.349911 20 31.93634 1.230524e-18 treated 3
    2 273.4214 9.697498 20 28.19505 1.411991e-17 untreated 3
    >
    > ## Non estimable contrasts
    >
    > ## ## Make balanced dataset
    > dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3),
    + CC=factor(1:3)))
    > dat.bal$y <- rnorm(nrow(dat.bal))
    >
    > ## ## Make unbalanced dataset
    > # 'BB' is nested within 'CC' so BB=1 is only found when CC=1
    > # and BB=2,3 are found in each CC=2,3,4
    > dat.nst <- dat.bal
    > dat.nst$CC <-factor(c(1,1,2,2,2,2,1,1,3,3,3,3,1,1,4,4,4,4))
    >
    > mod.bal <- lm(y ~ AA + BB*CC, data=dat.bal)
    > mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst)
    >
    > LSmeans(mod.bal, effect=c("BB", "CC"))
     estimate se df t.stat p.value BB CC
    1 -0.2214052 0.6749357 8 -0.3280390 0.75130293 1 1
    2 0.3798261 0.6749357 8 0.5627589 0.58901767 2 1
    3 -0.2454803 0.6749357 8 -0.3637092 0.72549942 3 1
    4 0.6128769 0.6749357 8 0.9080522 0.39038424 1 2
    5 0.1351965 0.6749357 8 0.2003102 0.84623645 2 2
    6 0.9508122 0.6749357 8 1.4087448 0.19657044 3 2
    7 -1.4179702 0.6749357 8 -2.1008967 0.06884143 1 3
    8 0.5399987 0.6749357 8 0.8000742 0.44677275 2 3
    9 0.4638230 0.6749357 8 0.6872106 0.51137651 3 3
    > LSmeans(mod.nst, effect=c("BB", "CC"))
     estimate se df t.stat p.value BB CC
    1 -0.3421662 0.4369726 10 -0.7830382 0.4517503 1 1
    2 NA NA NA NA NA 2 1
    3 NA NA NA NA NA 3 1
    4 NA NA NA NA NA 1 2
    5 0.3798261 0.7568587 10 0.5018455 0.6266404 2 2
    6 -0.2454803 0.7568587 10 -0.3243410 0.7523623 3 2
    7 NA NA NA NA NA 1 3
    8 0.1351965 0.7568587 10 0.1786284 0.8617965 2 3
    9 0.9508122 0.7568587 10 1.2562612 0.2375761 3 3
    10 NA NA NA NA NA 1 4
    11 0.5399987 0.7568587 10 0.7134736 0.4918678 2 4
    12 0.4638230 0.7568587 10 0.6128264 0.5536716 3 4
    > LSmeans(mod.nst, at=list(BB=1, CC=1))
     estimate se df t.stat p.value BB CC
    estimate -0.3421662 0.4369726 10 -0.7830382 0.4517503 1 1
    >
    > LSmeans(mod.nst, at=list(BB=1, CC=2))
     estimate se df t.stat p.value BB CC
    estimate NA NA NA NA NA 1 2
    > ## Above: NA's are correct; not an estimable function
    >
    > if( require( lme4 )){
    + warp.mm <- lmer(breaks ~ -1 + tension + (1|wool), data=warpbreaks)
    + LSmeans(warp.mm, effect="tension")
    + class(warp.mm)
    + fixef(warp.mm)
    + coef(summary(warp.mm))
    + vcov(warp.mm)
    + if (require(pbkrtest))
    + vcovAdj(warp.mm)
    + }
    Loading required package: lme4
    Loading required package: Matrix
    
    Attaching package: 'Matrix'
    
    The following objects are masked from 'package:base':
    
     crossprod, tcrossprod
    
    Error in cbind(do.call(cbind, SigmaG$G), X) :
     number of rows of matrices must match (see arg 2)
    Calls: LSmeans ... <Anonymous> -> vcovAdj.lmerMod -> vcovAdj16_internal -> cbind
    Execution halted
Flavor: r-oldrel-windows-ix86+x86_64