Matrix of plots. Left-down triangle consists of plots of fitted values (aternatively residuals), on the diagonal there are density plots of fitted values (alternatively residuals), in the right-top triangle there are correlations between fitte dvalues (alternatively residuals).

plot_correlation(object, ..., values = "fit")

plotModelCorrelation(object, ..., values = "fit")

Arguments

object

An object of class 'auditor_model_residual' created with model_residual function.

...

Other 'auditor_model_residual' objects to be plotted together.

values

"fit" for model fitted values or "res" for residual values.

Value

Invisibly returns a gtable object.

Examples

dragons <- DALEX::dragons[1:100, ] # fit a model model_lm <- lm(life_length ~ ., data = dragons) # use DALEX package to wrap up a model into explainer exp_lm <- DALEX::explain(model_lm, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated #> -> model label : lm (default) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.lm will be used (default) #> -> predicted values : numerical, min = 585.8311 , mean = 1347.787 , max = 2942.307 #> -> residual function : difference between y and yhat (default) #> -> residuals : numerical, min = -88.41755 , mean = -1.489291e-13 , max = 77.92805 #> A new explainer has been created!
# validate a model with auditor library(auditor) mr_lm <- model_residual(exp_lm) library(randomForest) model_rf <- randomForest(life_length~., data = dragons) exp_rf <- DALEX::explain(model_rf, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated #> -> model label : randomForest (default) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.randomForest will be used (default) #> -> predicted values : numerical, min = 758.3905 , mean = 1340.895 , max = 2455.742 #> -> residual function : difference between y and yhat (default) #> -> residuals : numerical, min = -185.2927 , mean = 6.891513 , max = 442.6785 #> A new explainer has been created!
mr_rf <- model_residual(exp_rf) # plot results plot_correlation(mr_lm, mr_rf)
plot(mr_lm, mr_rf, type = "correlation")