Prediction for logit regression

Author: koenderks

R/mlClassificationLogisticMultinomial.R

@@ -236,7 +236,7 @@ mlClassificationLogisticMultinomial <- function(jaspResults, dataset, options, .
 
 .mlClassificationLogisticPredictions <- function(trainingSet, options, probabilities) {
   categories <- levels(trainingSet[[options[["target"]]]])
-  predicted_categories <- categories[round(probabilities, 0) + 1]
+  predicted_categories <- factor(categories[round(probabilities, 0) + 1], levels = levels(trainingSet[[options[["target"]]]]))
   return(predicted_categories)
 }
 
@@ -251,6 +251,6 @@ mlClassificationLogisticMultinomial <- function(jaspResults, dataset, options, .
   probabilities <- probabilities / row_sums
   predicted_columns <- apply(probabilities, 1, which.max)
   categories <- levels(trainingSet[[options[["target"]]]])
-  predicted_categories <- categories[predicted_columns]
+  predicted_categories <- factor(categories[predicted_columns], levels = levels(trainingSet[[options[["target"]]]]))
   return(predicted_categories)
 }

R/mlPrediction.R

@@ -142,7 +142,7 @@ is.jaspMachineLearning <- function(x) {
   as.character(e1071:::predict.naiveBayes(model, newdata = dataset, type = "class"))
 }
 .mlPredictionGetPredictions.glm <- function(model, dataset) {
-  # TODO
+  as.character(levels(as.factor(model$model[, 1]))[round(predict(model, newdata = dataset, type = "response"), 0) + 1])
 }
 .mlPredictionGetPredictions.vglm <- function(model, dataset) {
   # TODO
@@ -248,7 +248,8 @@ is.jaspMachineLearning <- function(x) {
   return(model)
 }
 .decodeJaspMLobject.glm <- function(model) {
-  # TODO
+  formula <- formula(paste(decodeColNames(as.character(model$terms)[2]), "~", paste0(decodeColNames(strsplit(as.character(model$terms)[3], split = " + ", fixed = TRUE)[[1]]), collapse = " + ")))
+  model$terms <- stats::terms(formula)
   return(model)
 }
 .decodeJaspMLobject.vglm <- function(model) {
@@ -352,8 +353,6 @@ is.jaspMachineLearning <- function(x) {
     table$addColumnInfo(name = "mtry", title = gettext("Features per split"), type = "integer")
   } else if (inherits(model, "cv.glmnet")) {
     table$addColumnInfo(name = "lambda", title = "\u03BB", type = "number")
-  } else if (inherits(model, "glm") || inherits(model, "vglm")) {
-    table$addColumnInfo(name = "family", title = gettext("Family"), type = "string")
   }
   table$addColumnInfo(name = "ntrain", title = gettext("n(Train)"), type = "integer")
   table$addColumnInfo(name = "nnew", title = gettext("n(New)"), type = "integer")
@@ -372,10 +371,6 @@ is.jaspMachineLearning <- function(x) {
     row[["mtry"]] <- model[["mtry"]]
   } else if (inherits(model, "cv.glmnet")) {
     row[["lambda"]] <- model[["lambda.min"]]
-  } else if (inherits(model, "glm")) {
-    row[["family"]] <- gettext("binomial")
-  } else if (inherits(model, "vglm")) {
-    row[["family"]] <- gettext("multinomial")
   }
   if (length(presentVars) > 0) {
     row[["nnew"]] <- nrow(dataset)