Influence of observations Plot in D3 with r2d3 package.

Plot of Cook’s distances used for estimate the influence of an single observation.

plotD3_cooksdistance(
  object,
  ...,
  nlabel = 3,
  single_plot = FALSE,
  scale_plot = FALSE,
  background = FALSE
)

plotD3CooksDistance(
  object,
  ...,
  nlabel = 3,
  single_plot = FALSE,
  scale_plot = FALSE,
  background = FALSE
)

Arguments

object	An object of class 'auditor_model_cooksdistance' created with model_cooksdistance function.
...	Other objects of class 'auditor_model_cooksdistance'.
nlabel	Number of observations with the biggest Cook's distances to be labeled.
single_plot	Logical, indicates whenever single or facets should be plotted. By default it's FALSE.
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

Details

Cook’s distance is a tool for identifying observations that may negatively affect the model. They may be also used for indicating regions of the design space where it would be good to obtain more observations. Data points indicated by Cook’s distances are worth checking for validity.

Cook’s Distances are calculated by removing the i-th observation from the data and recalculating the model. It shows how much all the values in the model change when the i-th observation is removed.

For model classes other than lm and glm the distances are computed directly from the definition.

References

Cook, R. Dennis (1977). "Detection of Influential Observations in Linear Regression". doi:10.2307/1268249.

See also

plot_cooksdistance

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
cd_lm <- model_cooksdistance(lm_audit)

# plot results
plotD3_cooksdistance(cd_lm, nlabel = 5)