Cluster sequences using Mixed Markov Models

Fits a mixture of Markov chains to sequence data and returns the fitted net_mmm clustering object. The fit retains assignments, posterior probabilities, mixing proportions, information criteria, and the fitted component models.

Usage

cluster_mmm(
  data,
  k = 2L,
  n_starts = 50L,
  max_iter = 200L,
  tol = 1e-06,
  smooth = 0.01,
  seed = NULL,
  covariates = NULL,
  covariate_effect = c("em", "posthoc"),
  estimator = c("auto", "firth", "multinom", "chisq"),
  cluster_by = "mmm",
  ...
)

Arguments

data

A data.frame (wide format), netobject, cograph_network, or tna model. For tna and cograph_network objects the stored (integer-encoded) data is extracted and decoded to state labels.

k

Integer. Whole finite number of mixture components, >= 2. Default: 2.

n_starts

Integer. Positive whole finite number of random restarts. Default: 50.

max_iter

Integer. Positive whole finite maximum EM iterations per start. Default: 200.

tol

Numeric. Finite positive convergence tolerance. Default: 1e-6.

smooth

Numeric. Finite non-negative 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 a string, character vector, formula, or data.frame (same forms as build_clusters). For netobject or cograph_network input, names are resolved against $metadata first, so a typical call is build_mmm(net, k = 3, covariates = "session_label"). Unlike the post-hoc analysis in build_clusters(), these covariates directly influence cluster membership during EM estimation (see covariate_effect).

covariate_effect

How covariates enter the model. "em" (default) folds them into the EM as covariate-dependent mixing proportions, so they shape the cluster fit itself (and rows with missing covariates are dropped before fitting). "posthoc" fits a plain mixture on every sequence and uses the covariates only for the after-fit multinomial logit, so covariate values — and their missingness — never change which clusters are found. Ignored when covariates is NULL.

estimator

Multinomial fitter for the post-hoc covariate analysis (does not affect EM): "auto" (default) inspects the cluster x covariate cross-tab and falls back to "firth" only when any cell has fewer than 5 observations (separation risk), otherwise the much faster "multinom"; "firth" forces Firth's penalised likelihood via brglm2::brmultinom (finite under separation); "multinom" forces nnet::multinom (warns about separation risk); "chisq" runs descriptive tests (no logit). See build_clusters for full details.

cluster_by

Character. Accepted only as "mmm" (the default). Present so cluster_mmm() and cluster_network() share the same call shape; any other value raises an error pointing at cluster_network.

...

Unsupported. Supplying unused arguments raises an error.

Value

A fitted net_mmm clustering object. This is the same object contract returned by build_mmm. For HTNA input, its preserved actor partition is restored when the fit is materialized with build_network or Nestimate::as_htna().

Details

To materialize one network per fitted cluster, pass the result to build_network or use cluster_network(..., cluster_by = "mmm") for fitting and network construction in one call.

See also

build_mmm, build_network, and cluster_network for fitting and immediately materializing per-cluster networks

Examples

seqs <- data.frame(V1 = sample(c("A","B","C"), 30, TRUE),
                   V2 = sample(c("A","B","C"), 30, TRUE))
fit <- cluster_mmm(seqs, k = 2, n_starts = 1, max_iter = 10, seed = 1)
fit
#> Mixed Markov Model
#>   Sequences: 30  |  Clusters: 2  |  States: 3
#>   ICs: LL = -59.844  |  BIC = 177.508  |  AIC = 153.688  |  ICL = 179.621
#>   Quality: AvePP = 0.966  |  Entropy = 0.202  |  Class.Err = 0.0%
#>   Status: did not converge in 10 iterations
#> 
#>   Cluster  N           Mix%   AvePP
#>   1        26 (86.7%)  84.8%  0.969
#>   2        4 (13.3%)   15.2%  0.943
cluster_diagnostics(fit)
#> Cluster Diagnostics (mmm) [k = 2]
#>   Sequences: 30  |  Clusters: 2  |  States: 3
#>   Quality: AvePP = 0.966  |  Entropy = 0.202  |  Class.Err = 0.0%
#>   ICs: LL = -59.844  |  BIC = 177.508  |  AIC = 153.688  |  ICL = 179.621
#> 
#>   Cluster  N           Mix%   AvePP  Class.Err%
#>   1        26 (86.7%)  84.8%  0.969   0.0%
#>   2        4 (13.3%)   15.2%  0.943   0.0%
# \donttest{
# Visualise with sequence_plot
seqs <- data.frame(
  V1 = sample(LETTERS[1:3], 40, TRUE),
  V2 = sample(LETTERS[1:3], 40, TRUE),
  V3 = sample(LETTERS[1:3], 40, TRUE)
)
fit <- cluster_mmm(seqs, k = 2)
sequence_plot(fit, type = "index")

# }