Plot of predicted response vs observed or variable Values.

plotPrediction(object, ..., variable = NULL, smooth = FALSE,
abline = FALSE)

## Arguments

object An object of class modelAudit or modelResiduals. Other modelAudit or modelResiduals objects to be plotted together. Only for modelAudit objects. Name of model variable to order residuals. If value is NULL the data is ordered by a vector of actual response (y parameter passed to the audit function). One can also pass any name of any other variable in the data set. If variable = "" is set, unordered observations are presented. Logical, indicates whenever smooth line should be added. Logical, indicates whenever function y = x shoul be added. Works only with variable = NULL which is a default option.

plot.modelAudit
library(car)
lm_au <- audit(lm_model, data = Prestige, y = Prestige$prestige) plotPrediction(lm_au, variable = "prestige", abline = TRUE) library(randomForest) rf_model <- randomForest(prestige~education + women + income, data = Prestige) rf_au <- audit(rf_model, data = Prestige, y = Prestige$prestige)