Add plotLIFT and plotCGain

DESCRIPTION

@@ -17,6 +17,7 @@ Imports: car,
     ggplot2,
     hnp,
     plotROC,
+    ROCR,
     tseries
 RoxygenNote: 6.0.1
 Suggests: aods3,

NAMESPACE

@@ -1,11 +1,13 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,modelAudit)
 export(audit)
+export(plot.modelAudit)
 export(plotACF)
 export(plotAutocorrelation)
+export(plotCGains)
 export(plotCook)
 export(plotHalfNormal)
+export(plotLIFT)
 export(plotREC)
 export(plotROC)
 export(plotRROC)
@@ -17,6 +19,8 @@ export(scoreDW)
 export(scoreGQ)
 export(scoreHalfNormal)
 export(scoreRuns)
+import(ROCR)
+import(dplyr)
 import(ggplot2)
 import(plotROC)
 importFrom(car,durbinWatsonTest)

R/plotCGains.R

@@ -0,0 +1,71 @@
+#' @title Cumulative Gains Chart
+#'
+#' @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
+#'
+#' @seealso \code{\link{plot.modelAudit}}
+#'
+#' @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){
+  if(class(object)!="modelAudit") stop("plotCGains requires object class modelAudit.")
+  rpp <- tpr <- label <- NULL
+  df <- getCGainsDF(object, newdata, newy)
+
+  dfl <- list(...)
+  if (length(dfl) > 0) {
+    for (resp in dfl) {
+      if(class(resp)=="modelAudit"){
+        df <- rbind( df, getCGainsDF(resp, newdata, newy) )
+      }
+    }
+  }
+
+  ggplot(df, aes(x = rpp, y = tpr, color = label)) +
+    geom_line() +
+    xlab("Rate of Positive Prediction") +
+    ylab("True Positive Rate") +
+    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
+  }
+
+  pred <- prediction(predictions, y)
+  gain <- performance(pred, "tpr", "rpp")
+
+  res <- data.frame(rpp = gain@x.values[[1]], tpr = gain@y.values[[1]], alpha = gain@alpha.values[[1]],
+                    label = object$label)
+  return(res)
+}

R/plotLift.R

@@ -0,0 +1,84 @@
+#' @title Lift Chart
+#'
+#' @description Lift Chart shows the ratio of a model to a random guess.
+#'
+#' @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 groups number of groups
+#' @param cumulative boolean. If TRUE cumulative lift curve will be plotted.
+#' @param ... other modelAudit objects to be plotted together
+#'
+#' @return ggplot object
+#'
+#' @seealso \code{\link{plot.modelAudit}}
+#'
+#' @import ggplot2
+#' @import dplyr
+#'
+#' @examples
+#' library(auditor)
+#' library(mlbench)
+#' library(randomForest)
+#' data("PimaIndiansDiabetes")
+#'
+#' model_rf <- randomForest(diabetes~., data=PimaIndiansDiabetes)
+#' au_rf <- audit(model_rf, label="rf")
+#' plotLIFT(au_rf)
+#'
+#' model_glm <- glm(diabetes~., family=binomial,	data=PimaIndiansDiabetes)
+#' au_glm <- audit(model_glm)
+#' plotLIFT(au_rf, au_glm)
+#'
+#' @export
+
+
+plotLIFT <- function(object, ..., newdata = NULL, newy, groups = 10, cumulative = TRUE){
+  if(class(object)!="modelAudit") stop("plotCGains requires object class modelAudit.")
+  depth <- lift <- label <- NULL
+  df <- getLIFTDF(object, newdata, newy, groups, cumulative)
+
+  dfl <- list(...)
+  if (length(dfl) > 0) {
+    for (resp in dfl) {
+      if(class(resp)=="modelAudit"){
+        df <- rbind( df, getLIFTDF(resp, newdata, newy, groups, cumulative) )
+      }
+    }
+  }
+
+  ggplot(df, aes(x = depth, y = lift, color = label)) +
+    geom_line() +
+    xlab("Percentage of observations") +
+    ylab("Lift") +
+    theme_light()
+}
+
+getLIFTDF <- function(object, newdata, newy, n.groups, cumulative = TRUE){
+  pred <- NULL
+  if (is.null(newdata)) {
+    predictions <- object$fitted.values
+    y <- as.numeric(as.character(object$y))
+  } else {
+    if(is.null(newy)) stop("newy must be provided.")
+    predictions <- object$predict.function(object$model, newdata)
+    y <- as.numeric(as.character(newy))
+  }
+  df <- data.frame(pred=predictions, y=y)
+  df <- arrange(df, desc(pred))
+
+  group <- ceiling(seq_along(df[,2])/floor(nrow(df)/n.groups))
+
+  cap <- floor(nrow(df)/n.groups) * n.groups
+  df <-  stats::aggregate(df[1:cap,2], by=list(group[1:cap]), mean)
+
+  if (cumulative==TRUE) {
+    df[,2] <- cumsum(df[,2])/seq_along(df[,2])
+  }
+  colnames(df) <- c("depth", "lift")
+  df$lift <- df$lift/mean(y)
+  df$depth <- 100* df$depth / n.groups
+  df$label <- object$label
+  return(df)
+}
+