plot_pca: Principal Component Analysis of models

In ModelOriented/auditor: Model Audit - Verification, Validation, and Error Analysis

Principal Component Analysis of models

Description

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

Usage

plot_pca(object, ..., scale = TRUE, arrow_size = 2)

plotModelPCA(object, ..., scale = TRUE)

Arguments

object	An object of class auditor_model_residual created with model_residual function.
...	Other auditor_model_residual objects to be plotted together.
scale	A logical value indicating whether the models residuals should be scaled before the analysis.
arrow_size	Width of the arrows.

Value

A ggplot object.

Examples

dragons <- DALEX::dragons[1:100, ]

# fit a model
model_lm <- lm(life_length ~ ., data = dragons)

lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length)

# validate a model with auditor
mr_lm <- model_residual(lm_audit)

library(randomForest)
model_rf <- randomForest(life_length~., data = dragons)
rf_audit <- audit(model_rf, data = dragons, y = dragons$life_length)
mr_rf <- model_residual(rf_audit)

# plot results
plot_pca(mr_lm, mr_rf)

ModelOriented/auditor documentation built on Oct. 31, 2023, 8:38 a.m.