Principal Component Analysis of models residuals. PCA can be used to assess the similarity of the models.

plotModelPCA(object, ..., scale = TRUE, invisible = "none")

Arguments

object

An object of class modelAudit or modelResiduals,

...

Other modelAudit or modelResiduals objects to be plotted together.

scale

A logical value indicating whether the models residuals should be scaled before the analysis.

invisible

A text specifying the elements to be hidden on the plot. Default value is "none". Allowed values are "model", "observ".

Value

ggplot object

See also

Examples

library(car) lm_model <- lm(prestige~education + women + income, data = Prestige) lm_au <- audit(lm_model, data = Prestige, y = Prestige$prestige) library(randomForest) rf_model <- randomForest(prestige~education + women + income, data = Prestige) rf_au <- audit(rf_model, data = Prestige, y = Prestige$prestige) plotModelPCA(lm_au, rf_au)