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imputations.\n\n\n```r\nCombineMIs(points, covs)\n```\n\n## Arguments\n\n- `points`: List of point estimates, potentially vector valued.\n- `covs`: List of sampling covariances, potentially matrix valued.\n\n## Returns\n\nList containing the final point estimate (`point`) and sampling covariance (`cov`).\n\n\n\n## Examples\n\n```r\nset.seed(100)\n\n# Generate data and introduce missingness.\ndata <- rGMM(n = 25, d = 2, k = 1)\ndata[1, 1] <- NA\ndata[2, 2] <- NA\ndata[3, ] <- NA \n\n# Fit GMM.\nfit <- FitGMM(data)\n\n# Lists to store summary statistics.\npoints <- list()\ncovs <- list()\n\n# Perform 50 multiple imputations.\n# For each, calculate the marginal mean and its sampling variance.\nfor (i in seq_len(50)) {\n imputed <- GenImputation(fit)\n points[[i]] <- apply(imputed, 2, mean)\n covs[[i]] <- cov(imputed) / nrow(imputed)\n}\n\n# Combine summary statistics across imputations.\nresults <- CombineMIs(points, covs)\n```"}]}]}]}]}]}],["$","div",null,{"className":"lg:w-1/3 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