Build a Random Mixture Markov Model Object

Construct a synthetic tna_mmm object that mirrors the structure a fitted seqHMM mixed Markov model exposes to tna without depending on seqHMM at runtime. The returned object can be passed to group_model() (dispatching via group_model.tna_mmm) and to mmm_stats() (dispatching via mmm_stats.tna_mmm).

Real seqHMM mhmm objects continue to dispatch via the original *.mhmm methods.

Usage

random_tna_mmm(
  n_clusters = NULL,
  n_states = NULL,
  states = NULL,
  category = NULL,
  alpha = NULL,
  diag_boost = NULL,
  n_sequences = NULL,
  seq_length = NULL,
  n_covariates = 1L,
  seed = NULL
)

Arguments

n_clusters

An integer giving the number of mixture clusters. If NULL (the default), drawn from 2:4 on each call.

n_states

An integer >= 2 giving the number of states. If NULL (the default), a value is drawn from 7:11 on each call.

states

An optional character vector of state labels of length at least n_states. The first n_states are used. If NULL (the default), labels are taken from category or auto-picked from a built-in pool that fits n_states.

category

An optional character string naming a built-in label pool. Available pools are returned by list_random_state_pools(). When NULL (the default), a pool whose size is at least n_states is sampled at random. Ignored when states is supplied.

alpha

A positive numeric Dirichlet concentration parameter. Small values (e.g. 0.3) produce sparse, peaked transitions; large values (e.g. 5) produce near-uniform transitions. If NULL (the default), a value is drawn from Uniform(0.5, 1.0) on each call.

diag_boost

A non-negative numeric added to the diagonal of the transition matrix before re-normalising rows. Larger values make states "stickier" (more self-transitions). If NULL (the default), a value is drawn from Uniform(1.5, 3.0) on each call.

n_sequences

An integer giving the number of sequences to simulate from the true parameters. If NULL (the default), a value is drawn from 500:800 on each call.

seq_length

An integer giving the length of each simulated sequence. If NULL (the default), a value is drawn from 6:20 on each call.

n_covariates

An integer >= 1 giving the number of regression variables (including the intercept) used to predict cluster membership. Default is 1 (intercept only). When > 1, additional rows are added to the coefficient matrix.

seed

An integer random seed for reproducibility, or NULL (the default) for fresh randomness on every call.

Value

An object of class tna_mmm containing fields observations, transition_probs, initial_probs, coefficients, vcov, most_probable_cluster, cluster_names, state_names, n_clusters, n_states, n_sequences, n_covariates.

See also

Other data: import_data(), import_onehot(), list_random_state_pools(), prepare_data(), print.tna_data(), random_group_tna(), random_tna(), simulate.group_tna(), simulate.tna()

Examples

model <- random_tna_mmm(seed = 1)
mmm_stats(model)
#>     cluster    variable  estimate std_error   ci_lower ci_upper  z_value
#> 1 Cluster 2 (Intercept) 0.4825967 0.4679061 -0.4344823 1.399676 1.031397
#>     p_value
#> 1 0.3023549
grp <- group_model(model)