@@ -162,7 +162,7 @@ setMethod("crossValidate", "DataFrame",

                                   # Loop over classifiers

                                   set.seed(seed)

                                   CV(

-                                      measurements = measurements[, mcols(measurements)$dataset == dataIndex],

+                                      measurements = measurements[, mcols(measurements)$dataset == dataIndex, drop = FALSE],

                                       classes = classes,

                                       nFeatures = nFeatures[dataIndex],

                                       selectionMethod = selectionIndex,

@@ -440,11 +440,14 @@

   tuneChosen <- NULL

   if(!is.null(modellingParams@trainParams@tuneParams) && is.null(modellingParams@selectParams@tuneParams))

   {

+    performanceType <- modellingParams@trainParams@tuneParams[["performanceType"]]

+    modellingParams@trainParams@tuneParams <- modellingParams@trainParams@tuneParams[-match("performanceType", names(modellingParams@trainParams@tuneParams))]

     tuneCombos <- expand.grid(modellingParams@trainParams@tuneParams, stringsAsFactors = FALSE)

     modellingParams@trainParams@tuneParams <- NULL

     performances <- sapply(1:nrow(tuneCombos), function(rowIndex)

     {

+      modellingParams@trainParams@otherParams <- c(modellingParams@trainParams@otherParams, as.list(tuneCombos[rowIndex, ]))

       if(crossValParams@tuneMode == "Resubstitution")

       {

         result <- runTest(measurementsTrain, outcomesTrain, measurementsTest, outcomesTest,

@@ -456,13 +459,13 @@

           predictedOutcomes <- predictions[, "outcome"]

         else

           predictedOutcomes <- predictions

-        calcExternalPerformance(outcomesTest, predictedOutcomes, performanceName)

+        calcExternalPerformance(outcomesTest, predictedOutcomes, performanceType)

       } else {

         result <- runTests(measurementsTrain, outcomesTrain,

                            crossValParams, modellingParams,

                            verbose = verbose, .iteration = "internal")

-        result <- calcCVperformance(result, performanceName)

-        median(predictions(result)["performanceType"])

+        result <- calcCVperformance(result, performanceType)

+        median(performances(result)[[performanceType]])

       }

     })

     betterValues <- .ClassifyRenvir[["performanceInfoTable"]][.ClassifyRenvir[["performanceInfoTable"]][, "type"] == performanceType, "better"]