Fix jasp-stats/jasp-issues#3183

Author: koenderks

NAMESPACE

@@ -1,14 +1,4 @@
 import(jaspBase)
-S3method(.decodeJaspMLobject,kknn)
-S3method(.decodeJaspMLobject,lda)
-S3method(.decodeJaspMLobject,lm)
-S3method(.decodeJaspMLobject,gbm)
-S3method(.decodeJaspMLobject,randomForest)
-S3method(.decodeJaspMLobject,cv.glmnet)
-S3method(.decodeJaspMLobject,nn)
-S3method(.decodeJaspMLobject,rpart)
-S3method(.decodeJaspMLobject,svm)
-S3method(.decodeJaspMLobject,naiveBayes)
 S3method(.mlPredictionGetModelType,kknn)
 S3method(.mlPredictionGetModelType,lda)
 S3method(.mlPredictionGetModelType,lm)

R/mlPrediction.R

@@ -249,7 +249,7 @@ is.jaspMachineLearning <- function(x) {
       dataset <- .scaleNumericData(dataset)
     }
     # Select only the predictors in the model to prevent accidental double column names
-    dataset <- dataset[, which(decodeColNames(colnames(dataset)) %in% model[["jaspVars"]][["decoded"]]$predictors)]
+    dataset <- dataset[, which(decodeColNames(colnames(dataset)) %in% model[["jaspVars"]][["decoded"]]$predictors), drop = FALSE]
     # Ensure the column names in the dataset match those in the training data
     colnames(dataset) <- .matchDecodedNames(colnames(dataset), model)
     # Retrieve the training set