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Cluster sequences using Mixed Markov Models

Source: R/mmm.R
cluster_mmm.Rd

Convenience alias for build_mmm. Fits a mixture of Markov chains to sequence data and returns per-component transition networks with EM-fitted initial state probabilities.

Usage

cluster_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

A net_mmm object. See build_mmm for details.

Details

Use build_network on the result to extract per-cluster networks with any estimation method, or use cluster_network for a one-shot clustering + network call.

See also

build_mmm, cluster_network

Examples

# \donttest{
seqs <- data.frame(
  V1 = sample(LETTERS[1:3], 40, TRUE),
  V2 = sample(LETTERS[1:3], 40, TRUE),
  V3 = sample(LETTERS[1:3], 40, TRUE)
)
mmm <- cluster_mmm(seqs, k = 2)
print(mmm)
#> Mixed Markov Model
#>   k = 2 | 40 sequences | 3 states
#>   LL = -122.9 | BIC = 308.6 | ICL = 314.2
#> 
#>   Cluster  Size  Mix%%   AvePP
#>   ------------------------------
#>         1    23  56.6%  0.944
#>         2    17  43.4%  0.945
#> 
#>   Overall AvePP = 0.944 | Entropy = 0.153 | Class.Err = 0.0%
# }