The half-normal plot is one of the tools designed to evaluate the goodness of fit of a statistical models. It is a graphical method for comparing two probability distributions by plotting their quantiles against each other. Points on the plot correspond to ordered absolute values of model diagnostic (i.e. standardized residuals) plotted against theoretical order statistics from a half-normal distribution.

plot_halfnormal(object, ..., quantiles = FALSE, sim = 99)

plotHalfNormal(object, ..., quantiles = FALSE, sim = 99)

## Arguments

object An object of class 'auditor_model_halfnormal' created with model_halfnormal function. Other 'auditor_model_halfnormal' objects. If TRUE values on axis are on quantile scale. Number of residuals to simulate.

## Value

A ggplot object.

model_halfnormal

score_halfnormal

## 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
exp_lm <- DALEX::explain(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  :  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
library(auditor)
hn_lm <- model_halfnormal(exp_lm)#> Gaussian model (lm object)
# plot results
plot_halfnormal(hn_lm)plot(hn_lm)