I have a large dataset of a couple of categorical (nominal) variables and a number of continuous variables. Most of the continuous variables have missing data.
I have been using the mice package (pmm and rf) to impute the missing data, however, I realised that the method is ignoring the categorical data. The categorical data could be useful for prediction.
Therefore, I am looking for a multiple imputation code (ideally Random Forest because there is a large share of missing data) in R which allows considers both continuous and categorical predictors to impute multiple continuous variables.
事实证明我需要将我的分类变量转换为向量
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