DLMRMV (version 1.0.0)

PMMI: Predictive Mean Matching with Multiple Imputation

Description

Implements PMM algorithm for handling missing data in linear regression models. Uses chained equations approach to generate multiple imputed datasets and pools results using Rubin's rules.

Usage

PMMI(data, k = 5, m = 5)

Value

List containing:

Y

Original response vector with NAs

Yhat

Final imputed response vector (averaged across imputations)

betahat

Pooled regression coefficients

imputations

List of m completed datasets

m

Number of imputations performed

k

Number of neighbors used

Arguments

data

Dataframe with response variable in 1st column and predictors in others

k

Number of nearest neighbors for matching (default=5)

m

Number of imputations (default=5)

Examples

# Create dataset with 30% missing values
data <- data.frame(Y=c(rnorm(70),rep(NA,30)), X1=rnorm(100))
results <- PMMI(data, k=5, m=5)