Merge pull request #44 from dpc10ster/cran2-fix

Cran2 fixed # Reason for revised submission * This is an update to CRAN version 1.3.1 which installed with no errors, warnings, or notes (2020-01-13) on all platforms. * Recently it was brought to my attention (by Kurt Hornik <[email protected]>) that the package was generating errors on `R-devel` (>r77865) running on several Linux platforms. * The errors were identified as likely due to the new default behavior for option `stringsAsFactors = FALSE` in functions `data.frame()` and `read.table()`. * This update (V1.3.2) corrects those errors.
dpc10ster · Mar 6, 2020 · b95aa57 · b95aa57
2 parents f1779aa + 5631115
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: RJafroc
 Type: Package
 Title: Analyzing Diagnostic Observer Performance Studies
-Version: 1.3.1
-Date: 2020-01-31
+Version: 1.3.2
+Date: 2020-03-06
 Authors@R: c(person("Dev", "Chakraborty", role = c("cre","aut","cph"), email = "[email protected]"),
              person("Peter", "Philips", role = c("aut"), email = "[email protected]"),
              person("Xuetong", "Zhai", role = c("aut"), email = "[email protected]"),

diff --git a/NAMESPACE b/NAMESPACE
@@ -17,7 +17,7 @@ export(DfReadLrocDataFile)
 export(DfSaveDataFile)
 export(FitBinormalRoc)
 export(FitCbmRoc)
-export(FitCorCbm)
+export(FitCorCbmRoc)
 export(FitRsmRoc)
 export(PlotBinormalFit)
 export(PlotCbmFit)

diff --git a/NEWS.md b/NEWS.md
@@ -1,6 +1,22 @@
-# RJafroc 1.3.1
-
-## After initial comments from CRAN
+# RJafroc 1.3.2
+
+## After email from Kurt Hornik <[email protected]>
+* Created new branch off `cran2` 1.3.1 called `cran2-fix`
+* Bumped version to 1.3.2
+* RJafroc failing on Linux
+    + r-devel-linux-x86_64-debian-clang 
+    + r-devel-linux-x86_64-debian-gcc
+    + r-devel-linux-x86_64-fedora-clang (this showed up post email)
+    + r-devel-linux-x86_64-fedora-gcc (this showed up post email)
+* Has to do with new default (R 4.0.0) for `options(stringsAsFactors = FALSE)`
+* To recreate this problem in `R CMD check` I set `options(stringsAsFactor=FALSE)` near beginning of each plotting function (3 functions) using `data.frame()` and `levels()` to convert strings to factor levels
+* To make problem go away I explicitly specified `stringsAsFactor=TRUE` in each call to `data.frame()` where necessary.
+* Removed examples from FitCorCbmRoc() as they were generating excessive CPU time NOTES. Will need to add these to vignettes, later.
+* Ran `R CMD check` successfully
+* Ran all checks in ScriptsForCranSubmission.R
+* Submitted to CRAN
+
+## After initial comments from CRAN upon submission of version 1.3.0
 * Changes to DESCRIPTION file (removed emphasis characters and url before <)
 * Added a sentence on improved sample size routines
 * Bumped version to 1.3.1

diff --git a/R/DfCreateCorCbmDataset.R b/R/DfCreateCorCbmDataset.R
@@ -1,4 +1,4 @@
-#' Create paired dataset for testing \code{\link{FitCorCbm}}
+#' Create paired dataset for testing \code{\link{FitCorCbmRoc}}
 #' 
 #' @description The paired dataset is generated using bivariate sampling; 
 #' details are in referenced publication 
@@ -21,7 +21,7 @@
 #' @details The ROC data is bined to 5 bins in each condition. 
 #'    
 #'     
-#' @return The return value is the desired dataset, suitable for testing \code{\link{FitCorCbm}}.
+#' @return The return value is the desired dataset, suitable for testing \code{\link{FitCorCbmRoc}}.
 #' 
 #' 
 #' 

diff --git a/R/DfExtractCorCbmDataset.R b/R/DfExtractCorCbmDataset.R
@@ -21,7 +21,7 @@
 #' is that implied by the first treatement
 #' and the second reader, which is one arm, and the other arm is that implied by the second
 #' treatment paired with the first
-#' reader. Using this method any allowed pairing can be extracted and analyzed by \code{\link{FitCorCbm}}.
+#' reader. Using this method any allowed pairing can be extracted and analyzed by \code{\link{FitCorCbmRoc}}.
 #' The utility of this software is
 #' in designing a ratings simulator that is statistically matched to a real dataset.
 #'

diff --git a/R/FitCorCbm.R → R/FitCorCbmRoc.R b/R/FitCorCbm.R → R/FitCorCbmRoc.R
@@ -23,7 +23,7 @@
 #'    treatment, the same reader interpreting images in different treatments, or 
 #'    different readers interpreting images in 2 different treatments. Function 
 #'    \code{\link{DfExtractCorCbmDataset}} can be used to construct a dataset suitable for 
-#'    \code{FitCorCbm}. With reference to the returned values, and assuming R bins 
+#'    \code{FitCorCbmRoc}. With reference to the returned values, and assuming R bins 
 #'    in condition X and L bins in conditon Y, 
 #'    \code{FPCounts} is the R x L matrix containing the counts for non-diseased cases, 
 #'    \code{TPCounts} is the R x L matrix containing the counts for diseased cases; 
@@ -37,22 +37,22 @@
 #'    has one less dimension (along each edge) for each parameter that is held constant.
 #'    The indices of the parameters held fixed are in \code{fitCorCbmRet$fixParam}.
 #'
-#' @examples
-#' \dontrun{
-#' ## this takes 85 sec to execute on OSX
-#' dataset <- DfExtractCorCbmDataset(dataset05, trts = 1, rdrs = c(4,7))
-#' ret <- FitCorCbm(dataset)
-#' print(ret$fitCorCbmRet)
-#' print(ret$stats)
-#' print(ret$fittedPlot)
-#' 
-#' ## this takes very long to execute
-#' ret <- FitCorCbm(datasetBinned123)
-#' print(ret$fitCorCbmRet)
-#' print(ret$stats)
-#' print(ret$fittedPlot)
-#' ## Also try two other datasets ending with 124 and 125
-#' }
+## following examples generate excessive CPU time NOTES on devtools::check_win_xx() 
+## but it is instructive to execute them independently
+## see also the publication referenced below
+##
+## dataset <- DfExtractCorCbmDataset(dataset05, trts = 1, rdrs = c(4,7))
+## ret <- FitCorCbmRoc(dataset)
+## print(ret$fitCorCbmRet)
+## print(ret$stats)
+## print(ret$fittedPlot)
+## 
+## 
+## ret <- FitCorCbmRoc(datasetBinned123)
+## print(ret$fitCorCbmRet)
+## print(ret$stats)
+## print(ret$fittedPlot)
+## Also try two other datasets ending with 124 and 125
 #'
 #'
 #' @references
@@ -68,7 +68,9 @@
 #' @export
 #'
 #'
-FitCorCbm <- function(dataset){
+FitCorCbmRoc <- function(dataset){
+  options(stringsAsFactors = FALSE) # check compatibility with new default for R 4.0.0
+
   if (dataset$dataType != "ROC") {
     stop("This program requires an ROC dataset")
   }
@@ -510,6 +512,7 @@ PlotCorCbmFit <- function(retFitCorCBM){
   plotOpPnts <- NULL
   FPFX <- 1 - pnorm(plotZeta)
   TPFX <- (1 - alphaX) * (1 - pnorm(plotZeta)) + alphaX * (1 - pnorm(plotZeta, mean = muX))
+
   plotCBM <- rbind(plotCBM, data.frame(FPF = FPFX, TPF = TPFX, Condition = "X"))
   FPFX <- cumsum(rev(rowSums(FPCounts)))/K1
   TPFX <- cumsum(rev(rowSums(TPCounts)))/K2

diff --git a/R/PlotBinormalFit.R b/R/PlotBinormalFit.R
@@ -28,6 +28,7 @@ PlotBinormalFit <- function(a, b){
     FPF <- c(1, FPF, 0)
     TPF <- pnorm(a[i] - b[i] * plotZeta)  
     TPF <- c(1, TPF, 0)
+
     plotBM <- rbind(plotBM, data.frame(FPF = FPF, TPF = TPF, Treatment = as.character(i)))
   }
   bmROCCurve <- with(plotBM,{