RMEP_mice: Multiple imputation methods for multivariate missing data

In Yangyonghust/RMEP: Calculating Evapotranspiration and Energy Fluxes by Maximum Entropy Production Model

Description

Deal with the missing data of the inputs for the maximum entropy production(MEP) model. The function creates multiple imputations (replacement values) for multivariate missing data. The imputation methods are established based on the mice package:Generates Multivariate Imputations by Chained Equations (MICE). There are mainly six univariate imputation methods available including:Predictive mean matching("pmm"), Weighted predictive mean matching("midastouch"), Classification and regression trees ("cart"), Unconditional mean imputation ("mean"), Imputation of quadratic terms("quadratic") and Linear regression, predicted values ("norm.predict"). Users can select method for imputations and the default is Predictive mean matching("pmm").

Usage

RMEP_mice(data, method = "pmm")

Arguments

data	A dataframe of variables but contains missing data for MEP inputs
method	Methods for imputation, including six methods selected

Value

A dataframe with no missing values

Examples

RMEP_mice(airquality,method= 'pmm')

Yangyonghust/RMEP documentation built on Nov. 30, 2021, 11:20 a.m.