@@ -3,8 +3,8 @@ Type: Package

 Title: A framework for cross-validated classification problems, with

        applications to differential variability and differential

        distribution testing

-Version: 3.1.25

-Date: 2022-10-18

+Version: 3.1.26

+Date: 2022-10-19

 Author: Dario Strbenac, Ellis Patrick, John Ormerod, Graham Mann, Jean Yang

 Maintainer: Dario Strbenac <dario.strbenac@sydney.edu.au>

 VignetteBuilder: knitr

@@ -193,7 +193,7 @@ setMethod("crossValidate", "DataFrame",

                   # The below loops over different combinations of assays and merges them together.

                   # This allows someone to answer which combinations of the assays might be most useful.

-                  if(!is.list(assayCombinations) && assayCombinations == "all") assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

+                  if(!is.list(assayCombinations) && assayCombinations[1] == "all") assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

                   result <- sapply(assayCombinations, function(assayIndex){

                       CV(measurements = measurements[, mcols(measurements)[["assay"]] %in% assayIndex],

@@ -221,14 +221,14 @@ setMethod("crossValidate", "DataFrame",

                   # This allows someone to answer which combinations of the assays might be most useful.

-                  if(!is.list(assayCombinations) && assayCombinations == "all")

+                  if(!is.list(assayCombinations) && assayCombinations[1] == "all")

                   {

                       assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

                       assayCombinations <- assayCombinations[sapply(assayCombinations, function(combination) "clinical" %in% combination, simplify = TRUE)]

                       if(length(assayCombinations) == 0) stop("No assayCombinations with \"clinical\" data")

                   }

-                  result <- sapply(assayCombinations[2], function(assayIndex){

+                  result <- sapply(assayCombinations, function(assayIndex){

                       CV(measurements = measurements[, mcols(measurements)[["assay"]] %in% assayIndex],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

@@ -255,7 +255,7 @@ setMethod("crossValidate", "DataFrame",

                   # This allows someone to answer which combinations of the assays might be most useful.

-                  if(!is.list(assayCombinations) && assayCombinations == "all"){

+                  if(!is.list(assayCombinations) && assayCombinations[1] == "all"){

                       assayCombinations <- do.call("c", sapply(seq_along(assayIDs),function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

                       assayCombinations <- assayCombinations[sapply(assayCombinations, function(combination) "clinical" %in% combination, simplify = TRUE)]

                       if(length(assayCombinations) == 0) stop("No assayCombinations with \"clinical\" data")

@@ -14,6 +14,7 @@ classifyInterface <- function(countsTrain, classesTrain, countsTest, ...,

   classPredictions <- predicted[["ytehat"]]

   classScores <- predicted[["discriminant"]]

   colnames(classScores) <- levels(classesTrain)

+  rownames(classScores) <- names(classPredictions) <- rownames(countsTest)

   switch(returnType, class = classPredictions, # Factor vector.

          score = classScores, # Numeric matrix.

          both = data.frame(class = classPredictions, classScores, check.names = FALSE))

@@ -39,6 +39,6 @@ coxnetPredictInterface <- function(model, measurementsTest, survivalTest = NULL,

   model$offset <- TRUE

   survScores <- predict(model, testMatrix, s = lambda, type = "response", newoffset = offset)

-  

+  rownames(survScores) <- rownames(measurementsTest)

   survScores[, 1]

 }

\ No newline at end of file

@@ -31,15 +31,6 @@ extractPrevalidation = function(assayPreval){ #}, startingCol) {

     vec

 }

-# Use to pull out the names of features in a ClassifyR model - XYZ: Could Ditch if we really wanted to

-featurePuller = function(classifyObject) {

-    if ("selectResult" %in% slotNames(classifyObject)) {

-        features = classifyObject@selectResult@chosenFeatures

-    } else{

-        features = classifyObject@originalFeatures

-    }

-}

-

 setClass("prevalModel", slots = list(fullModel = "list"))

 prevalTrainInterface <- function(measurements, classes, params, ...)

@@ -23,6 +23,7 @@ randomForestPredictInterface <- function(forest, measurementsTest, ..., returnTy

   classPredictions <- predict(forest, measurementsTest)$predictions

   classScores <- predict(forest, measurementsTest, predict.all = TRUE)[[1]]

   classScores <- t(apply(classScores, 1, function(sampleRow) table(factor(classes[sampleRow], levels = classes)) / forest$forest$num.trees))

+  rownames(classScores) <- names(classPredictions) <- rownames(measurementsTest)

   switch(returnType, class = classPredictions,

          score = classScores,

          both = data.frame(class = classPredictions, classScores, check.names = FALSE))

@@ -38,7 +38,7 @@ SVMpredictInterface <- function(model, measurementsTest, returnType = c("both",

   # e1071 uses attributes to pass back probabilities. Make them a standalone variable.

   classScores <- attr(classPredictions, "probabilities")[, model[["levels"]], drop = FALSE]

   attr(classPredictions, "probabilities") <- NULL

-  

+  rownames(classScores) <- names(classPredictions) <- rownames(measurementsTest)

   switch(returnType, class = classPredictions, score = classScores,

          both = data.frame(class = classPredictions, classScores, check.names = FALSE))

 }

\ No newline at end of file

@@ -46,14 +46,17 @@ extremeGradientBoostingPredictInterface <- function(booster, measurementsTest, .

   if(verbose == 3)

     message("Predicting using boosted random forest.")  

   measurementsTest <- as(measurementsTest, "data.frame")

+  measurementsTest <- measurementsTest[, attr(booster, "featureNames")]

   # Convert to one-hot encoding as xgboost doesn't understand factors. Need to get rid of intercept afterwards.

   measurementsTest <- MatrixModels::model.Matrix(~ 0 + ., data = measurementsTest, sparse = TRUE)

+  

   scores <- predict(booster, measurementsTest, reshape = TRUE)

   colnames(scores) <- attr(booster, "classes")

   if(!is.null(attr(booster, "classes"))) # It is a classification task.

   {

     classPredictions <- attr(booster, "classes")[apply(scores, 1, function(sampleRow) which.max(sampleRow)[1])]

     classPredictions <- factor(classPredictions, levels = attr(booster, "classes"))

+    rownames(scores) <- names(classPredictions) <- rownames(measurementsTest)

     result <- switch(returnType, class = classPredictions,

                      score = scores,

                      both = data.frame(class = classPredictions, scores, check.names = FALSE))