Plot Residuals vs Observed, Fitted or Variable Values in D3 with r2d3 package.

Source: R/plotD3_residual.R

Function plotD3_residual plots residual values vs fitted, observed or variable values in the model.

plotD3_residual(
  object,
  ...,
  variable = "_y_",
  points = TRUE,
  smooth = FALSE,
  std_residuals = FALSE,
  nlabel = 0,
  point_count = NULL,
  single_plot = TRUE,
  scale_plot = FALSE,
  background = FALSE
)

plotD3Residual(
  object,
  ...,
  variable = NULL,
  points = TRUE,
  smooth = FALSE,
  std_residuals = FALSE,
  point_count = NULL,
  single_plot = TRUE,
  scale_plot = FALSE,
  background = FALSE
)

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.
points

Logical, indicates whenever observations should be added as points. By default it's TRUE.
smooth

Logical, indicates whenever smoothed lines should be added. By default it's FALSE.
std_residuals

Logical, indicates whenever standardized residuals should be used. By default it's FALSE.
nlabel

Number of observations with the biggest residuals to be labeled.
point_count

Number of points to be plotted per model. Points will be chosen randomly. By default plot all of them.
single_plot

Logical, indicates whenever single or facets should be plotted. By default it's TRUE.
scale_plot

Logical, indicates whenever the plot should scale with height. By default it's FALSE.
background

Logical, available only if single_plot = FALSE. Indicates whenever background plots should be plotted. By default it's FALSE.

Value

a r2d3 object

See also

plot_residual

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
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
plotD3_residual(mr_lm)

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 =  754.92 , mean =  1340.849 , max =  2470.237  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -202.9642 , mean =  6.938273 , max =  428.1842  
#>   A new explainer has been created!  
mr_rf <- model_residual(rf_audit)
plotD3_residual(mr_lm, mr_rf)