CRAN Package Check Results for Package LTRCtrees

Last updated on 2018-10-17 07:51:02 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.0 1.76 55.37 57.13 OK
r-devel-linux-x86_64-debian-gcc 1.1.0 1.23 41.18 42.41 OK
r-devel-linux-x86_64-fedora-clang 1.1.0 66.54 OK
r-devel-linux-x86_64-fedora-gcc 1.1.0 64.88 OK
r-devel-windows-ix86+x86_64 1.1.0 5.00 89.00 94.00 ERROR
r-patched-linux-x86_64 1.1.0 1.68 51.30 52.98 OK
r-patched-solaris-x86 1.1.0 82.70 OK
r-release-linux-x86_64 1.1.0 1.27 51.93 53.20 OK
r-release-windows-ix86+x86_64 1.1.0 5.00 84.00 89.00 OK
r-release-osx-x86_64 1.1.0 OK
r-oldrel-windows-ix86+x86_64 1.1.0 6.00 96.00 102.00 OK
r-oldrel-osx-x86_64 1.1.0 OK

Check Details

Version: 1.1.0
Check: examples
Result: ERROR
    Running examples in 'LTRCtrees-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: Pred.rpart
    > ### Title: Prediction function for rpart.object
    > ### Aliases: Pred.rpart
    >
    > ### ** Examples
    >
    > ## The Assay of serum free light chain data in survival package
    > ## Adjust data & clean data
    > library(survival)
    > library(LTRCtrees)
    > Data <- flchain
    > Data <- Data[!is.na(Data$creatinine),]
    > Data$End <- Data$age + Data$futime/365
    > DATA <- Data[Data$End > Data$age,]
    > names(DATA)[6] <- "FLC"
    >
    > ## Setup training set and test set
    > Train = DATA[1:500,]
    > Test = DATA[1000:1020,]
    >
    > ## Predict median survival time and Kaplan Meier survival curve
    > ## on test data using Pred.rpart
    > LTRCART.pred <- Pred.rpart(Surv(age, End, death) ~ sex + FLC + creatinine, Train, Test)
    > LTRCART.pred$KMcurves ## list of predicted KM curves
    [[1]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[2]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[3]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[4]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[5]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[6]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     144.0 56.0 0.0 131.0 83.8 81.0 86.7
    
    [[7]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[8]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[9]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[10]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[11]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[12]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[13]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     144.0 56.0 0.0 131.0 83.8 81.0 86.7
    
    [[14]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[15]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     86.0 47.0 0.0 75.0 87.0 84.2 89.3
    
    [[16]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[17]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[18]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[19]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     248.0 144.0 0.0 189.0 90.5 88.4 91.4
    
    [[20]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     144.0 56.0 0.0 131.0 83.8 81.0 86.7
    
    [[21]]
    Call: survfit(formula = Formula, data = subset)
    
    records n.max n.start events median 0.95LCL 0.95UCL
     144.0 56.0 0.0 131.0 83.8 81.0 86.7
    
    > LTRCART.pred$Medians ## vector of predicted median survival time
     [1] 90.5 90.5 90.5 90.5 90.5 83.8 90.5 90.5 90.5 90.5 90.5 90.5 83.8 90.5 87.0
    [16] 90.5 90.5 90.5 90.5 83.8 83.8
    >
    >
    >
    >
    > ### * <FOOTER>
    > ###
    > cleanEx()
    
    detaching 'package:survival'
    
    Error: connections left open:
     utils::capture.output(KM) (textConnection)
     utils::capture.output(KM) (textConnection)
     utils::capture.output(KM) (textConnection)
     utils::capture.output(KM) (textConnection)
    Execution halted
Flavor: r-devel-windows-ix86+x86_64