Nonparametric Missing Value Imputation using Random Forest
Extract Variable Types from a Dataframe
Nonparametric Missing Value Imputation using Random Forest
Nonparametric Missing Value Imputation using Random Forest
Compute Imputation Error for Mixed-type Data
Normalized Root Mean Squared Error
Introduce Missing Values Completely at Random
The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.