CRAN Package Check Results for Package mlquantify

Last updated on 2021-04-06 06:52:08 CEST.

Additional issues

M1mac

Check Details

Version: 0.1.4
Check: examples
Result: ERROR
    Running examples in 'mlquantify-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: PACC
    > ### Title: Probabilistic Adjusted Classify and Count
    > ### Aliases: PACC
    >
    > ### ** Examples
    >
    > library(randomForest)
    randomForest 4.6-14
    Type rfNews() to see new features/changes/bug fixes.
    > library(caret)
    Loading required package: lattice
    Loading required package: ggplot2
    
    Attaching package: 'ggplot2'
    
    The following object is masked from 'package:randomForest':
    
     margin
    
    > cv <- createFolds(aeAegypti$class, 3)
    > tr <- aeAegypti[cv$Fold1,]
    > validation <- aeAegypti[cv$Fold2,]
    > ts <- aeAegypti[cv$Fold3,]
    >
    > # -- Getting a sample from ts with 80 positive and 20 negative instances --
    > ts_sample <- rbind(ts[sample(which(ts$class==1),80),],
    + ts[sample(which(ts$class==2),20),])
    > scorer <- randomForest(class~., data=tr, ntree=500)
    > scores <- cbind(predict(scorer, validation, type = c("prob")), validation$class)
    > TprFpr <- getTPRandFPRbyThreshold(scores)
    > test.scores <- predict(scorer, ts_sample, type = c("prob"))[,1]
    >
    > # -- PACC requires calibrated scores. Be aware of doing this before using PACC --
    > # -- You can make it using calibrate function from the CORElearn package --
    > if(requireNamespace("CORElearn")){
    + cal_tr <- CORElearn::calibrate(as.factor(scores[,3]), scores[,1], class1=1,
    + method="isoReg",assumeProbabilities=TRUE)
    + test.scores <- CORElearn::applyCalibration(test.scores, cal_tr)
    + }
    Loading required namespace: CORElearn
    > PACC(test = test.scores, TprFpr = TprFpr)
    Error in if (result < 0) result <- 0 :
     missing value where TRUE/FALSE needed
    Calls: PACC -> PCC
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 0.1.4
Check: dependencies in R code
Result: NOTE
    Namespaces in Imports field not imported from:
     ‘caret’ ‘randomForest’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64-gcc10-UCRT, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64

Version: 0.1.4
Check: examples
Result: ERROR
    Running examples in ‘mlquantify-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: PACC
    > ### Title: Probabilistic Adjusted Classify and Count
    > ### Aliases: PACC
    >
    > ### ** Examples
    >
    > library(randomForest)
    randomForest 4.6-14
    Type rfNews() to see new features/changes/bug fixes.
    > library(caret)
    Loading required package: lattice
    Loading required package: ggplot2
    
    Attaching package: ‘ggplot2’
    
    The following object is masked from ‘package:randomForest’:
    
     margin
    
    > cv <- createFolds(aeAegypti$class, 3)
    > tr <- aeAegypti[cv$Fold1,]
    > validation <- aeAegypti[cv$Fold2,]
    > ts <- aeAegypti[cv$Fold3,]
    >
    > # -- Getting a sample from ts with 80 positive and 20 negative instances --
    > ts_sample <- rbind(ts[sample(which(ts$class==1),80),],
    + ts[sample(which(ts$class==2),20),])
    > scorer <- randomForest(class~., data=tr, ntree=500)
    > scores <- cbind(predict(scorer, validation, type = c("prob")), validation$class)
    > TprFpr <- getTPRandFPRbyThreshold(scores)
    > test.scores <- predict(scorer, ts_sample, type = c("prob"))[,1]
    >
    > # -- PACC requires calibrated scores. Be aware of doing this before using PACC --
    > # -- You can make it using calibrate function from the CORElearn package --
    > if(requireNamespace("CORElearn")){
    + cal_tr <- CORElearn::calibrate(as.factor(scores[,3]), scores[,1], class1=1,
    + method="isoReg",assumeProbabilities=TRUE)
    + test.scores <- CORElearn::applyCalibration(test.scores, cal_tr)
    + }
    Loading required namespace: CORElearn
    > PACC(test = test.scores, TprFpr = TprFpr)
    Error in if (result < 0) result <- 0 :
     missing value where TRUE/FALSE needed
    Calls: PACC -> PCC
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-x86_64-gcc10-UCRT, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64