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Choosing the right distributional family for regression models is essential to get more accurate estimates and standard errors. This function may help to check a models' distributional family and see if the model-family probably should be reconsidered. Since it is difficult to exactly predict the correct model family, consider this function as somewhat experimental.





Typically, a model (that should response to residuals()). May also be a numeric vector.


This function uses an internal random forest model to classify the distribution from a model-family. Currently, following distributions are trained (i.e. results of check_distribution() may be one of the following): "bernoulli", "beta", "beta-binomial", "binomial", "chi", "exponential", "F", "gamma", "lognormal", "normal", "negative binomial", "negative binomial (zero-inflated)", "pareto", "poisson", "poisson (zero-inflated)", "uniform" and "weibull".

Note the similarity between certain distributions according to shape, skewness, etc. Thus, the predicted distribution may not be perfectly representing the distributional family of the underlying fitted model, or the response value.

There is a plot() method, which shows the probabilities of all predicted distributions, however, only if the probability is greater than zero.


This function is somewhat experimental and might be improved in future releases. The final decision on the model-family should also be based on theoretical aspects and other information about the data and the model.

There is also a plot()-method implemented in the see-package.


data(sleepstudy, package = "lme4")
model <<- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
#> # Distribution of Model Family
#> Predicted Distribution of Residuals
#>  Distribution Probability
#>        cauchy         94%
#>     lognormal          3%
#>       weibull          3%
#> Predicted Distribution of Response
#>   Distribution Probability
#>      lognormal         47%
#>          gamma         44%
#>  beta-binomial          3%