This function plots the aggregated residuals of kfold crossvalidated models against the outcome. This allows to evaluate how the model performs according over or underestimation of the outcome.
sjp.kfold_cv(data, formula, k = 5, fit)
data  A data frame, used to split the data into 

formula  A model formula, used to fit linear models ( 
k  Number of folds. 
fit  Model object, which will be used to compute cross validation. If

This function, first, generates k
crossvalidated testtraining
pairs (using the crossv_kfold
function) and
fits the same model, specified in the formula
 or fit

argument, over all training data sets.
Then, the test data is used to predict the outcome from all
models that have been fit on the training data, and the residuals
from all test data is plotted against the observed values (outcome)
from the test data (note: for poisson or negative binomial models, the
deviance residuals are calculated). This plot can be used to validate the model
and see, whether it over (residuals > 0) or underestimates
(residuals < 0) the model's outcome.
Currently, only linear, poisson and negative binomial regression models are supported.
data(efc) sjp.kfold_cv(efc, neg_c_7 ~ e42dep + c172code + c12hour)sjp.kfold_cv(mtcars, mpg ~.)# for poisson models. need to fit a model and use 'fit'argument fit < glm(tot_sc_e ~ neg_c_7 + c172code, data = efc, family = poisson) sjp.kfold_cv(efc, fit = fit)# and for negative binomial models fit < MASS::glm.nb(tot_sc_e ~ neg_c_7 + c172code, data = efc) sjp.kfold_cv(efc, fit = fit)