@@ -124,16 +124,8 @@ setMethod("crossValidate", "DataFrame",

                                           measurements = measurements)

               selectionMethod <- cleanSelectionMethod(selectionMethod = selectionMethod,

                                                       measurements = measurements)

-              if(any(nFeatures == 1) && classifier == "elasticNetGLM")

-              {

-                  options(warn = 1)

-                  warning("Elastic Net GLM requires two or more features as input but there is only one.

-Using an ordinary GLM instead.")

-                  classifier <- "GLM"

-              }

-              

               classifier <- cleanClassifier(classifier = classifier,

-                                            measurements = measurements)

+                                            measurements = measurements, nFeatures = nFeatures)

               ##!!!!! Do something with data combinations

@@ -236,7 +228,7 @@ Using an ordinary GLM instead.")

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

                   }

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

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

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

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

@@ -471,9 +463,9 @@ cleanNFeatures <- function(nFeatures, measurements){

       obsFeatures <- unlist(as.list(table(mcols(measurements)[, "assay"])))

     else obsFeatures <- ncol(measurements)

     if(is.null(nFeatures) || length(nFeatures) == 1 && nFeatures == "all") nFeatures <- as.list(obsFeatures)

-    if(is.null(names(nFeatures)) & length(nFeatures) == 1) nFeatures <- as.list(pmin(obsFeatures, nFeatures))

-    if(is.null(names(nFeatures)) & length(nFeatures) > 1) nFeatures <- sapply(obsFeatures, function(x)pmin(x, nFeatures), simplify = FALSE)

-    #if(is.null(names(nFeatures)) & length(nFeatures) > 1) stop("nFeatures needs to be a named numeric vector or list with the same names as the assays.")

+    if(is.null(names(nFeatures)) && length(nFeatures) == 1) nFeatures <- as.list(pmin(obsFeatures, nFeatures))

+    if(is.null(names(nFeatures)) && length(nFeatures) > 1) nFeatures <- sapply(obsFeatures, function(x)pmin(x, nFeatures), simplify = FALSE)

+    #if(is.null(names(nFeatures)) && length(nFeatures) > 1) stop("nFeatures needs to be a named numeric vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(nFeatures))) stop("nFeatures needs to be a named numeric vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(nFeatures)) & is(nFeatures, "numeric")) nFeatures <- as.list(pmin(obsFeatures, nFeatures[names(obsFeatures)]))

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(nFeatures)) & is(nFeatures, "list")) nFeatures <- mapply(pmin, nFeatures[names(obsFeatures)], obsFeatures, SIMPLIFY = FALSE)

@@ -498,7 +490,7 @@ cleanSelectionMethod <- function(selectionMethod, measurements){

 ######################################

 ######################################

-cleanClassifier <- function(classifier, measurements){

+cleanClassifier <- function(classifier, measurements, nFeatures){

     #### Clean up

     if(!is.null(mcols(measurements)$assay))

       obsFeatures <- unlist(as.list(table(mcols(measurements)[, "assay"])))

@@ -509,10 +501,20 @@ cleanClassifier <- function(classifier, measurements){

     #if(is.null(names(classifier)) & length(classifier) > 1) stop("classifier needs to be a named character vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(classifier))) stop("classifier needs to be a named character vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(classifier)) & is(classifier, "character")) classifier <- as.list(classifier[names(obsFeatures)])

+    

+    nFeatures <- nFeatures[names(classifier)]

+    checkENs <- which(classifier == "elasticNetGLM")

+    if(length(checkENs) > 0)

+    {

+      replacements <- sapply(checkENs, function(checkEN) ifelse(any(nFeatures[[checkEN]] == 1), "GLM", "elasticNetGLM"))

+      classifier[checkENs] <- replacements

+      if(any(replacements) == "GLM")    

+        warning("Elastic Net GLM requires two or more features as input but there is only one.

+Using an ordinary GLM instead.")

+    }

     classifier

 }

-

 ######################################

 ######################################

 #' A function to generate a CrossValParams object

@@ -808,14 +810,6 @@ CV <- function(measurements = NULL,

                characteristicsLabel = NULL)

 {

-    # Check that other variables are in the right format and fix

-    nFeatures <- cleanNFeatures(nFeatures = nFeatures,

-                                measurements = measurements)

-    selectionMethod <- cleanSelectionMethod(selectionMethod = selectionMethod,

-                                            measurements = measurements)

-    classifier <- cleanClassifier(classifier = classifier,

-                                  measurements = measurements)

-

     # Which data-types or data-views are present?

     if(is.null(characteristicsLabel)) characteristicsLabel <- "none"

@@ -841,7 +835,7 @@ CV <- function(measurements = NULL,

     characteristics <- S4Vectors::DataFrame(characteristic = c(if(!is.null(assayText)) "Assay Name" else NULL, "Classifier Name", "Selection Name", "multiViewMethod", "characteristicsLabel"), value = c(if(!is.null(assayText)) paste(assayText, collapse = ", ") else NULL, paste(classifier, collapse = ", "),  paste(selectionMethod, collapse = ", "), multiViewMethod, characteristicsLabel))

     if(!is.null(measurements))

-    { # Cross-validation.        

+    { # Cross-validation.

       classifyResults <- runTests(measurements, outcome, crossValParams = crossValParams, modellingParams = modellingParams, characteristics = characteristics)

       fullResult <- runTest(measurements, outcome, measurements, outcome, crossValParams = crossValParams, modellingParams = modellingParams, characteristics = characteristics, .iteration = 1)

     } else { # Independent training and testing.

@@ -49,6 +49,7 @@ extremeGradientBoostingPredictInterface <- function(booster, measurementsTest, .

   # 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])]