MGMM.R
In MGMM: Missingness-Aware Gaussian Mixture Models

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  echo = TRUE,
  warning = FALSE,
  message = FALSE,
  cache = TRUE
)
library(MGMM)

## ----compact-example----------------------------------------------------------
set.seed(101)
library(MGMM)

# Parameter settings.
mean_list <- list(
  c(1, 1),
  c(-1, -1)
)
cov_list <- list(
  matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  matrix(c(1, 0.5, 0.5, 1), nrow = 2)
)

# Generate data.
data <- rGMM(
  n = 1e3,
  d = 2,
  k = 2,
  miss = 0.1,
  means = mean_list,
  covs = cov_list
)

# Original data.
head(data)

# Choose cluster number.
choose_k <- ChooseK(
  data,
  k0 = 2,
  k1 = 4,
  boot = 10,
  maxit = 10,
  eps = 1e-4,
  report = TRUE
)

# Cluster number recommendations.
show(choose_k$Choices)

# Estimation.
fit <- FitGMM(
  data,
  k = 2,
  maxit = 10
)

# Estimated means.
show(fit@Means)

# Estimated covariances.
show(fit@Covariances)

# Cluster assignments.
head(fit@Assignments)

# Deterministic imputation.
head(fit@Completed)

# Stochastic imputation.
imp <- GenImputation(fit)
head(imp)