evaluate_imputation_error: Evaluate the imputation error when true values are known.

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

Evaluate the imputation error when true values are known.

Description

Evaluate the imputation error when true values are known. Useful when missing values are simulated and true values are known (the errors are calculated as distances from the true values). For continuous variables, MSE (mean square error) and NMSE (normalized mean square error) are returned. For categorical variables, MER (misclassification error rate) is returned.

Usage

evaluate_imputation_error(ximp, xmis, xtrue, all = FALSE)

Arguments

ximp	imputed dataframe.
xmis	original dataframe with missing values.
xtrue	true dataframe with no missing values.
all	calculate error on all observations (TRUE) or only on missing observations (FALSE). Default is FALSE.

Details

For details check the advanced vignette on convergence criteria and error monitoring.

Value

Dataframe with variables in rows and performance measures in columns.

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