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missForestPredict (version 1.0)

Missing Value Imputation using Random Forest for Prediction Settings

Description

Missing data imputation based on the 'missForest' algorithm (Stekhoven, Daniel J (2012) ) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data.

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Install

install.packages('missForestPredict')

Monthly Downloads

743

Version

1.0

License

GPL (>= 2)

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Maintainer

Elena Albu

Last Published

December 12th, 2023

Functions in missForestPredict (1.0)

missForest

Imputes a dataframe and returns imputation models to be used on new observations
prop_usable_cases

Calculates variable-wise proportion of usable cases (missing and observed)
check_predictor_matrix

Performs checks on a custom predictor matrix
produce_NA

Produces a dataframe with missing values
evaluate_imputation_error

Evaluate the imputation error when true values are known.
calculate_convergence

Calculates convergence based on NMSE
missForestPredict

Imputes a new dataframe based on the missForest models
create_predictor_matrix

Creates the default predictor matrix with 0 diagonal and 1 elements in the rest.