Create Halfnormal Explanation

Creates auditor_model_halfnormal object that can be used for plotting halfnormal plot.

model_halfnormal(object, quant = FALSE, ...)

modelFit(object, quant = FALSE, ...)

Arguments

object	An object of class explainer created with function explain from the DALEX package.
quant	if TRUE values on axis are on quantile scale.
...	other parameters passed do hnp function.

Value

An object of the class auditor_model_halfnormal.

References

Moral, R., Hinde, J., & Demétrio, C. (2017). Half-Normal Plots and Overdispersed Models in R: The hnp Package.doi:http://dx.doi.org/10.18637/jss.v081.i10

Examples

data(titanic_imputed, package = "DALEX")

# fit a model
model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)

glm_audit <- audit(model_glm,
                   data = titanic_imputed,
                   y = titanic_imputed$survived)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  2207  rows  8  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.008128381 , mean =  0.3221568 , max =  0.9731431  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.9628583 , mean =  -2.569729e-10 , max =  0.9663346  
#>   A new explainer has been created!  

# validate a model with auditor
mh <- model_halfnormal(glm_audit)
#> Binomial model 
mh
#> Model label:  lm 
#> Quantiles of Residuals:
#>         0%        10%        20%        30%        40%        50%        60% 
#> -2.5663263 -0.8822712 -0.7406192 -0.6594877 -0.5668733 -0.4933954 -0.3793048 
#>        70%        80%        90%       100% 
#>  0.3573521  0.8379465  1.5982982  2.6043364 

plot(mh)