Fit a Mixed Markov Model

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 cluster_data). Unlike the post-hoc analysis in cluster_data(), 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

# \donttest{
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)
#> Mixed Markov Model
#>   k = 2 | 30 sequences | 3 states
#>   LL = -122.4 | BIC = 302.7 | ICL = 302.8
#> 
#>   Cluster  Size  Mix%%   AvePP
#>   ------------------------------
#>         1    24  80.0%  0.998
#>         2     6  20.0%  0.995
#> 
#>   Overall AvePP = 0.998 | Entropy = 0.019 | Class.Err = 0.0%
summary(mmm)
#> Mixed Markov Model
#>   k = 2 | 30 sequences | 3 states
#>   LL = -122.4 | BIC = 302.7 | ICL = 302.8
#> 
#>   Cluster  Size  Mix%%   AvePP
#>   ------------------------------
#>         1    24  80.0%  0.998
#>         2     6  20.0%  0.995
#> 
#>   Overall AvePP = 0.998 | Entropy = 0.019 | Class.Err = 0.0%
#> 
#> --- Cluster 1 (80.0%, n=24) ---
#>       A     B     C
#> A 0.250 0.250 0.500
#> B 0.302 0.333 0.364
#> C 0.334 0.466 0.200
#> 
#> --- Cluster 2 (20.0%, n=6) ---
#>       A     B     C
#> A 0.002 0.798 0.200
#> B 0.746 0.251 0.003
#> C 0.333 0.444 0.223
#> 
# }