bootstrap: Bootstrap Sampling of NHMM Coefficients

In seqHMM: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Bootstrap Sampling of NHMM Coefficients

Description

It is possible to parallelize the bootstrap runs using the future package, e.g., by calling future::plan(multisession, workers = 2) before bootstrap_coefs(). See future::plan() for details.

Usage

bootstrap_coefs(model, ...)

## S3 method for class 'nhmm'
bootstrap_coefs(
  model,
  nsim,
  type = c("nonparametric", "parametric"),
  append = FALSE,
  ...
)

## S3 method for class 'mnhmm'
bootstrap_coefs(
  model,
  nsim,
  type = c("nonparametric", "parametric"),
  append = FALSE,
  ...
)

Arguments

model	An nhmm or mnhmm object.
...	Additional arguments to estimate_nhmm() or estimate_mnhmm().
nsim	number of bootstrap samples.
type	Either "nonparametric" (default) or "parametric", to define whether nonparametric or parametric bootstrap should be used. The former samples sequences with replacement, whereas the latter simulates new datasets based on the model.
append	If TRUE, in case the model already contains bootstrap samples, new samples are appended to model$boot. If FALSE (default), old samples are discarded.
method	Estimation method used in bootstrapping. Defaults to "EM-DNM".

Details

bootstrap_coefs() is compatible with progressr package, so you can use progressr::with_progress(bootstrap_coefs(fit)) to track the progress of bootstrapping.

Value

The original model with additional element model$boot.

seqHMM documentation built on June 8, 2025, 10:16 a.m.