Cumulative Distribution Function for positive and negative residuals.

plot_tsecdf(object, ..., scale_error = TRUE, outliers = NA,
  residuals = TRUE, reverse_y = FALSE)

plotTwoSidedECDF(object, ..., scale_error = TRUE, outliers = NA,
  residuals = TRUE, reverse_y = FALSE)

Arguments

object

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

...

Other modelAudit objects to be plotted together.

scale_error

A logical value indicating whether ECDF should be scaled by proportions of positive and negative proportions.

outliers

Number of outliers to be marked.

residuals

A logical value indicating whether residuals should be marked.

reverse_y

A logical value indicating whether values on y axis should be reversed.

Value

A ggplot object.

See also

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$life_length)
#> Preparation of a new explainer is initiated #> -> model label : lm (default) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.lm will be used (default) #> -> predicted values : numerical, min = 585.8311 , mean = 1347.787 , max = 2942.307 #> -> residual function : difference between y and yhat (default) #> -> residuals : numerical, min = -88.41755 , mean = -1.489291e-13 , max = 77.92805 #> A new explainer has been created!
# validate a model with auditor library(auditor) mr_lm <- model_residual(exp_lm) plot_tsecdf(mr_lm)
plot(mr_lm, type="tsecdf")
library(randomForest) model_rf <- randomForest(life_length~., data = dragons) exp_rf <- DALEX::explain(model_rf, data = dragons, y = dragons$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 = 761.1137 , mean = 1341.155 , max = 2472.702 #> -> residual function : difference between y and yhat (default) #> -> residuals : numerical, min = -184.3527 , mean = 6.632384 , max = 425.7191 #> A new explainer has been created!
mr_rf <- model_residual(exp_rf) plot_tsecdf(mr_lm, mr_rf, reverse_y = TRUE)