missForestPredict: Imputes a new dataframe based on the missForest models

In missForestPredict: Missing Value Imputation using Random Forest for Prediction Settings

Imputes a new dataframe based on the missForest models

Description

Imputes a new dataframe based on the missForest models. The same number of iterations as in missForest are used.

Usage

missForestPredict(missForestObj, newdata, x_init = NULL)

Arguments

missForestObj	missForest object as returned by the missForest function.
newdata	new data to impute. The column names should be the same as in the imputation model.
x_init	initialization dataframe in case custom initialization mode has been used. It needs to be complete dataframe (with no missing values). See vignette for a full example.

Details

A new observation is initialized in the same manner as passed through the initialization parameter to the missForest function. Then, variables are imputed in the same sequence and for the same number of iterations using the random models saved for each iteration. This ensures that a new observation is imputed in the same manner as the training set (imputed by the function missForest). Re-imputing the training set with the missForestPredict will yield the same result as the original imputation returned by the missForest function.

Value

an imputed dataframe

Examples

data(iris)
# split train / test and create missing values
id_test <- sample(1:nrow(iris), floor(nrow(iris)/3))

iris_train <- iris[-id_test,]
iris_test <- iris[id_test,]

iris_train_miss <- produce_NA(iris_train, proportion = 0.1)
iris_test_miss <- produce_NA(iris_test, proportion = 0.1)

# impute train and learn imputation models
iris_train_imp_obj <- missForest(iris_train_miss, save_models = TRUE, num.threads = 2)

# impute test
iris_test_imp_new <- missForestPredict(iris_train_imp_obj, newdata = iris_test_miss)
head(iris_test_imp_new)

missForestPredict documentation built on May 29, 2024, 7:26 a.m.