PMMI {DLMRMV} | R Documentation |
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)
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) |
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 |
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)
[Package DLMRMV version 0.2.0 Index]