imp_cat_single: The function to impute unordered categorical variables

In hmi: Hierarchical Multiple Imputation

Description

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.

Usage

imp_cat_single(y_imp, X_imp, pvalue = 0.2, k = Inf)

Arguments

y_imp	A Vector with the variable to impute.
X_imp	A data.frame with the fixed effects variables.
pvalue	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.
k	An integer defining the allowed maximum of levels in a factor covariate.

Value

A n x 1 data.frame with the original and imputed values.

hmi documentation built on Oct. 23, 2020, 7:31 p.m.