The function uses regression trees for imputation implemented in mice.
The principle is the following:
For each observation it is calculated at which leave it would end.
Then one (randomly selected) observation of the other observations found on this leave
functions as a donor.
imp_cat_single(y_imp, X_imp, pvalue = 0.2, k = Inf)A Vector with the variable to impute.
A data.frame with the fixed effects variables.
A numeric between 0 and 1 denoting the threshold of p-values a variable in the imputation model should not exceed. If they do, they are excluded from the imputation model.
An integer defining the allowed maximum of levels in a factor covariate.
A n x 1 data.frame with the original and imputed values.