CombineMIs: Combine Multiple Imputations

In zrmacc/MNMix: Missingness Aware Gaussian Mixture Models

Combine Multiple Imputations

Description

Combines point estimates and standard errors across multiple imputations.

Usage

CombineMIs(points, covs)

Arguments

points	List of point estimates, potentially vector valued.
covs	List of sampling covariances, potentially matrix valued.

Value

List containing the final point estimate ('point') and sampling covariance ('cov').

Examples

set.seed(100)

# Generate data and introduce missingness.
data <- rGMM(n = 25, d = 2, k = 1)
data[1, 1] <- NA
data[2, 2] <- NA
data[3, ] <- NA 

# Fit GMM.
fit <- FitGMM(data)

# Lists to store summary statistics.
points <- list()
covs <- list()

# Perform 50 multiple imputations.
# For each, calculate the marginal mean and its sampling variance.
for (i in seq_len(50)) {
  imputed <- GenImputation(fit)
  points[[i]] <- apply(imputed, 2, mean)
  covs[[i]] <- cov(imputed) / nrow(imputed)
}

# Combine summary statistics across imputations.
results <- CombineMIs(points, covs)

zrmacc/MNMix documentation built on April 30, 2023, 6:37 a.m.