tna (version 1.3.1)

random_tna_mmm: Build a Random Mixture Markov Model Object

Description

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
)

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.

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.

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)
grp <- group_model(model)