create_predictor_matrix: Creates the default predictor matrix with 0 diagonal and 1...

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

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

Description

Creates a square predictor matrix with number of rows and columns equal to the number of columns of the data. Each row name represents the name of the column to be imputed. Each column name represents a predictor for the imputation model. The values in a row will contain 1 for the variables that should be included as predictors in the imputation model and 0 if the variable should not be included as predictor. The diagonal of the predictor matrix is 0, indicating that variable X will not be a predictor for the imputation model of variable X. All other values are 1, meaning that all other variables will be included in the imputation model. This is the default predictor matrix used.

Usage

create_predictor_matrix(data)

Arguments

data	dataframe to be imputed

Value

predictor matrix that can be used as a start for setting a custom predictor matrix

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