Fix plotFift

R/plotCGains.R

@@ -3,8 +3,6 @@
 #' @description Cumulative Gains Chartis a plot of the rate of positive prediction against true positive rate for the different thresholds.
 #' It is useful for measuring and comparing the accuracy of the classificators.
 #' @param object An object of class ModelAudit
-#' @param newdata optionally, a data frame in which to look for variables with which to plot CGains curve. If omitted, the data used to build model will be used.
-#' @param newy optionally, required if newdata used. Response vector for new data.
 #' @param ... other modelAudit objects to be plotted together
 #'
 #' @return ggplot object
@@ -14,33 +12,20 @@
 #' @import ggplot2
 #' @import ROCR
 #'
-#' @examples
-#' library(auditor)
-#' library(mlbench)
-#' library(randomForest)
-#' data("PimaIndiansDiabetes")
-#'
-#' model_rf <- randomForest(diabetes~., data=PimaIndiansDiabetes)
-#' au_rf <- audit(model_rf, label="rf")
-#' plotCGains(au_rf)
-#'
-#' model_glm <- glm(diabetes~., family=binomial,	data=PimaIndiansDiabetes)
-#' au_glm <- audit(model_glm)
-#' plotCGains(au_rf, au_glm)
 #'
 #' @export
 
 
-plotCGains <- function(object, ..., newdata = NULL, newy){
+plotCGains <- function(object, ...){
   if(class(object)!="modelAudit") stop("plotCGains requires object class modelAudit.")
   rpp <- tpr <- label <- NULL
-  df <- getCGainsDF(object, newdata, newy)
+  df <- getCGainsDF(object)
 
   dfl <- list(...)
   if (length(dfl) > 0) {
     for (resp in dfl) {
       if(class(resp)=="modelAudit"){
-        df <- rbind( df, getCGainsDF(resp, newdata, newy) )
+        df <- rbind( df, getCGainsDF(resp) )
       }
     }
   }
@@ -52,15 +37,10 @@ plotCGains <- function(object, ..., newdata = NULL, newy){
     theme_light()
 }
 
-getCGainsDF <- function(object, newdata, newy){
-  if (is.null(newdata)) {
-    predictions <-   object$fitted.values
-    y <- object$y
-  } else {
-    if(is.null(newy)) stop("newy must be provided.")
-    predictions <- object$predict.function(object$model, newdata)
-    y <- newy
-  }
+getCGainsDF <- function(object){
+
+  predictions <- object$fitted.values
+  y <- as.numeric(as.character(object$y))
 
   pred <- prediction(predictions, y)
   gain <- performance(pred, "tpr", "rpp")

R/plotLift.R

@@ -45,7 +45,7 @@ plotLIFT <- function(object, ..., groups = 10, cumulative = TRUE){
 
 getLIFTDF <- function(object, n.groups, cumulative = TRUE){
   pred <- NULL
-  y = object$y
+  y = as.numeric(as.character(object$y))
   df <- data.frame(pred=object$fitted.values, y=y)
   df <- arrange(df, desc(pred))
 

R/plotModelPCA.R

@@ -29,7 +29,7 @@ plotModelPCA <- function(object, ...){
     }
   }
 
-  res.pca <- prcomp(df, scale = TRUE)
+  res.pca <- prcomp(df, scale = FALSE)
 
   fviz_pca_biplot(res.pca,
                   repel = TRUE,