MGMM (version 1.0.1.3)

CombineMIs: Combine Multiple Imputations

Description

Combines point estimates and their estimated sampling (co)variances across multiple imputations using the usual multiple-imputation combining rules.

Usage

CombineMIs(points, covs)

Value

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

Arguments

points

List of point estimates (each may be a vector or scalar).

covs

List of estimated sampling covariance matrices (or variances for scalar estimates), one per imputation.

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)