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)
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