CRAN Package Check Results for Package lcmm

Last updated on 2014-12-20 12:47:28.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.6.6 35.53 78.80 114.33 OK
r-devel-linux-x86_64-debian-gcc 1.6.6 34.41 78.58 112.98 OK
r-devel-linux-x86_64-fedora-clang 1.6.6 203.40 OK
r-devel-linux-x86_64-fedora-gcc 1.6.6 185.86 OK
r-devel-osx-x86_64-clang 1.6.6 184.42 OK
r-devel-windows-ix86+x86_64 1.6.6 98.00 249.00 347.00 OK
r-patched-linux-x86_64 1.6.6 34.44 82.89 117.33 OK
r-patched-solaris-sparc 1.6.6 1661.80 OK
r-patched-solaris-x86 1.6.6 335.00 ERROR
r-release-linux-ix86 1.6.6 49.60 102.66 152.26 OK
r-release-linux-x86_64 1.6.6 34.57 79.54 114.11 OK
r-release-osx-x86_64-mavericks 1.6.6 WARN
r-release-osx-x86_64-snowleopard 1.6.6 OK
r-release-windows-ix86+x86_64 1.6.6 75.00 229.00 304.00 OK
r-oldrel-windows-ix86+x86_64 1.6.6 60.00 164.00 224.00 OK

Check Details

Version: 1.6.6
Check: examples
Result: ERROR
    Running examples in ‘lcmm-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: Diffepoce
    > ### Title: Computation of the difference of expected prognostic
    > ### cross-entropy (EPOCE) estimators and its 95% tracking interval
    > ### between two joint latent class models estimated with 'Jointlcmm'
    > ### Aliases: Diffepoce
    >
    > ### ** Examples
    >
    > #### estimation with 2 latent classes (ng=2)
    > data(data_Jointlcmm)
    > m2 <- Jointlcmm(fixed= Ydep1~Time*X1,random=~Time,mixture=~Time,subject='ID'
    + ,survival = Surv(Tevent,Event)~ X1+X2 ,hazard="Weibull"
    + ,hazardtype="PH",ng=2,data=data_Jointlcmm,
    + B=c( 0.7608, -9.4974, 1.0242, 1.4331, 0.1063 , 0.6714, 10.4679, 11.3178,
    + -2.5671, -0.5386, 1.4616, -0.0605, 0.9489, 0.1020, 0.2079, 1.5045),logscale=TRUE)
    Be patient, Jointlcmm is running ...
    The program took 1.63 seconds
    > m1 <- Jointlcmm(fixed= Ydep1~Time*X1,random=~Time,subject='ID'
    + ,survival = Surv(Tevent,Event)~ X1+X2 ,hazard="Weibull"
    + ,hazardtype="PH",ng=1,data=data_Jointlcmm,
    + B=c(-7.6634, 0.9136, 0.1002, 0.6641, 10.5675, -1.6589, 1.4767, -0.0806,
    + 0.9240,0.5643, 1.2277, 1.5004))
    Be patient, Jointlcmm is running ...
    The program took 5.29 seconds
    >
    > ## EPOCE computation for predictions times from 1 to 6 on the dataset used
    > ## for estimation of m.
    > VecTime <- c(1,3,5,7,9,11,13,15)
    > cvpol1 <- epoce(m1,var.time="Time",pred.times=VecTime)
    Be patient, epoce function is running ...
    The program took 0.92 seconds
    > cvpol1
    Expected Prognostic Observed Cross-Entropy (EPOCE) of the joint latent class model:
    
    Jointlcmm(fixed = Ydep1 ~ Time * X1, random = ~Time, subject = "ID",
     ng = 1, survival = Surv(Tevent, Event) ~ X1 + X2, hazard = "Weibull",
     hazardtype = "PH", data = data_Jointlcmm)
    
    EPOCE estimators on data used for estimation:
     Mean Prognostic Observed Log-likelihood (MPOL)
     and Cross-validated Prognostic Observed Log-likelihood (CVPOL)
     (CVPOL is the bias-corrected MPOL obtained by approximated cross-validation)
    
     pred. times N at risk N events MPOL CVPOL
     1 300 150 1.892619 .
     3 299 150 1.889431 .
     5 291 149 1.899210 .
     7 258 127 1.785964 .
     9 205 107 1.850733 .
     11 158 81 1.793531 .
     13 129 68 1.772987 .
     15 99 49 1.656587 .
    
    > cvpol2 <- epoce(m2,var.time="Time",pred.times=VecTime)
    Be patient, epoce function is running ...
    The program took 1.72 seconds
    > cvpol2
    Expected Prognostic Observed Cross-Entropy (EPOCE) of the joint latent class model:
    
    Jointlcmm(fixed = Ydep1 ~ Time * X1, mixture = ~Time, random = ~Time,
     subject = "ID", ng = 2, survival = Surv(Tevent, Event) ~
     X1 + X2, hazard = "Weibull", hazardtype = "PH", data = data_Jointlcmm,
     logscale = TRUE)
    
    EPOCE estimators on data used for estimation:
     Mean Prognostic Observed Log-likelihood (MPOL)
     and Cross-validated Prognostic Observed Log-likelihood (CVPOL)
     (CVPOL is the bias-corrected MPOL obtained by approximated cross-validation)
    
     pred. times N at risk N events MPOL CVPOL
     1 300 150 1.869272 2.092841
     3 299 150 1.840027 2.045497
     5 291 149 1.853149 2.056496
     7 258 127 1.735359 1.948203
     9 205 107 1.773111 2.084853
     11 158 81 1.672144 1.969925
     13 129 68 1.628349 1.924389
     15 99 49 1.463446 1.806864
    
    > DeltaEPOCE <- Diffepoce(cvpol1,cvpol2)
    > summary(DeltaEPOCE)
    Difference in Expected Prognostic Observed Cross-Entropy (EPOCE) estimates
     from the two following joint latent class models:
    
    Jointlcmm(fixed = Ydep1 ~ Time * X1, random = ~Time, subject = "ID",
     ng = 1, survival = Surv(Tevent, Event) ~ X1 + X2, hazard = "Weibull",
     hazardtype = "PH", data = data_Jointlcmm)
    Jointlcmm(fixed = Ydep1 ~ Time * X1, mixture = ~Time, random = ~Time,
     subject = "ID", ng = 2, survival = Surv(Tevent, Event) ~
     X1 + X2, hazard = "Weibull", hazardtype = "PH", data = data_Jointlcmm,
     logscale = TRUE)
    
    Difference in the Cross-Validated Prognostic Observed Log-likelihood (CVPOL)
     and its 95% tracking interval
    
     pred. times Diff CVPOL 95%TIinf 95%TIsup
     1 . . .
     3 . . .
     5 . . .
     7 . . .
     9 . . .
     11 . . .
     13 . . .
     15 . . .
    
    > plot(DeltaEPOCE,bty="l")
    Error in plot.Diffepoce(DeltaEPOCE, bty = "l") :
     can't produce the plot with missing differences in EPOCE
    Calls: plot -> plot.Diffepoce
    Execution halted
Flavor: r-patched-solaris-x86

Version: 1.6.6
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     Warning: Possible change of value in conversion from REAL(8) to INTEGER(4) at (1)
Flavor: r-release-osx-x86_64-mavericks