ChooseK: Cluster Number Selection

In MGMM: Missingness Aware Gaussian Mixture Models

Cluster Number Selection

Description

Function to choose the number of clusters k. Examines cluster numbers between k0 and k1. For each cluster number, generates boot bootstrap data sets, fits the Gaussian Mixture Model (FitGMM), and calculates quality metrics (ClustQual). For each metric, determines the optimal cluster number k_opt, and the k_1SE, the smallest cluster number whose quality is within 1 SE of the optimum.

Usage

ChooseK(
  data,
  k0 = 2,
  k1 = NULL,
  boot = 100,
  init_means = NULL,
  fix_means = FALSE,
  init_covs = NULL,
  init_props = NULL,
  maxit = 10,
  eps = 1e-04,
  report = TRUE
)

Arguments

data	Numeric data matrix.
k0	Minimum number of clusters.
k1	Maximum number of clusters.
boot	Bootstrap replicates.
init_means	Optional list of initial mean vectors.
fix_means	Fix the means to their starting value? Must provide initial values.
init_covs	Optional list of initial covariance matrices.
init_props	Optional vector of initial cluster proportions.
maxit	Maximum number of EM iterations.
eps	Minimum acceptable increment in the EM objective.
report	Report bootstrap progress?

Value

List containing Choices, the recommended number of clusters according to each quality metric, and Results, the mean and standard error of the quality metrics at each cluster number evaluated.

See Also

See ClustQual for evaluating cluster quality, and FitGMM for estimating the GMM with a specified cluster number.

Examples

set.seed(100)
mean_list <- list(c(2, 2), c(2, -2), c(-2, 2), c(-2, -2))
data <- rGMM(n = 500, d = 2, k = 4, means = mean_list)
choose_k <- ChooseK(data, k0 = 2, k1 = 6, boot = 10)
choose_k$Choices

MGMM documentation built on Sept. 30, 2023, 5:06 p.m.