build_mmm: Fit a Mixed Markov Model

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Fit a Mixed Markov Model

Description

Discovers latent subgroups with different transition dynamics using Expectation-Maximization. Each mixture component has its own transition matrix. Sequences are probabilistically assigned to components.

Usage

build_mmm(
  data,
  k = 2L,
  n_starts = 50L,
  max_iter = 200L,
  tol = 1e-06,
  smooth = 0.01,
  seed = NULL,
  covariates = NULL
)

Arguments

data	A data.frame (wide format), netobject, or tna model. For tna objects, extracts the stored data.
k	Integer. Number of mixture components. Default: 2.
n_starts	Integer. Number of random restarts. Default: 50.
max_iter	Integer. Maximum EM iterations per start. Default: 200.
tol	Numeric. Convergence tolerance. Default: 1e-6.
smooth	Numeric. Laplace smoothing constant. Default: 0.01.
seed	Integer or NULL. Random seed.
covariates	Optional. Covariates integrated into the EM algorithm to model covariate-dependent mixing proportions. Accepts formula, character vector, string, or data.frame (same forms as build_clusters). Unlike the post-hoc analysis in build_clusters(), these covariates directly influence cluster membership during estimation. Requires the nnet package.

Value

An object of class net_mmm with components:

models

List of netobjects, one per component.

k

Number of components.

mixing

Numeric vector of mixing proportions.

posterior

N x k matrix of posterior probabilities.

assignments

Integer vector of hard assignments (1..k).

quality

List: avepp (per-class), avepp_overall, entropy, relative_entropy, classification_error.

log_likelihood, BIC, AIC, ICL

Model fit statistics.

states

Character vector of state names.

See Also

compare_mmm, build_network

Examples

seqs <- data.frame(V1 = sample(c("A","B","C"), 30, TRUE),
                   V2 = sample(c("A","B","C"), 30, TRUE))
mmm <- build_mmm(seqs, k = 2, n_starts = 1, max_iter = 10, seed = 1)
mmm

seqs <- data.frame(
  V1 = sample(LETTERS[1:3], 30, TRUE), V2 = sample(LETTERS[1:3], 30, TRUE),
  V3 = sample(LETTERS[1:3], 30, TRUE), V4 = sample(LETTERS[1:3], 30, TRUE)
)
mmm <- build_mmm(seqs, k = 2, seed = 42)
print(mmm)
summary(mmm)

Nestimate documentation built on April 20, 2026, 5:06 p.m.