Autocorrelation Plot in D3 with r2d3 package.

Plot of i-th residual vs i+1-th residual.

plotD3_autocorrelation(
  object,
  ...,
  variable = NULL,
  points = TRUE,
  smooth = FALSE,
  point_count = NULL,
  single_plot = TRUE,
  scale_plot = FALSE,
  background = FALSE
)

plotD3Autocorrelation(
  object,
  ...,
  variable = NULL,
  points = TRUE,
  smooth = 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).
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.
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

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
plotD3_autocorrelation(mr_lm)
plotD3_autocorrelation(mr_lm, smooth = TRUE)