Plot Residuals vs Observed, Fitted or Variable Values

A plot of residuals against fitted values, observed values or any variable.

plot_residual(
  object,
  ...,
  variable = "_y_",
  smooth = FALSE,
  std_residuals = FALSE,
  nlabel = 0
)

plotResidual(
  object,
  ...,
  variable = NULL,
  smooth = FALSE,
  std_residuals = FALSE,
  nlabel = 0
)

Arguments

object	An object of class auditor_model_residual created with model_residual function.
...	Other auditor_model_residual objects to be plotted together.
variable	Name of variable to order residuals on a plot. If variable="_y_", the data is ordered by a vector of actual response (y parameter passed to the explain function). If variable = "_y_hat_" the data on the plot will be ordered by predicted response. If variable = NULL, unordered observations are presented.
smooth	Logical, indicates whenever smoothed lines should be added. By default it's FALSE.
std_residuals	Logical, indicates whenever standardized residuals should be used.
nlabel	Number of observations with the biggest absolute values of residuals to be labeled.

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)
#> 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  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task regression (  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
mr_lm <- model_residual(lm_audit)

# plot results
plot_residual(mr_lm)
plot(mr_lm, type = "residual")

library(randomForest)
model_rf <- randomForest(life_length~., data = dragons)
rf_audit <- audit(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  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  751.9421 , mean =  1341.146 , max =  2459.921  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -187.2486 , mean =  6.640559 , max =  438.4994  
#>   A new explainer has been created!  
mr_rf <- model_residual(rf_audit)
plot_residual(mr_lm, mr_rf)
plot(mr_rf, mr_rf, type = "residual")