sim_hmm

<code>sim_hmm</code> generate <code>n</code> action sequences from HMM based on given parameters.
The lengths of the generated sequences are simulated from a Poission distribution with
mean <code>mean_len</code> and at least <code>min_len</code>.

Provides functions for simulating from and fitting the latent hidden
Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>.
It also includes functions for simulating from and fitting ordinary hidden Markov models.

Xueying Tang

proclhmm

Latent Hidden Markov Models for Response Process Data

sim_hmm function

<dl><dt>n</dt>
<dd>number of action sequences to be generated</dd>
<dt>paras</dt>
<dd>a list containing specified HMM parameters: state-transition probability matrix (<code>P</code>),
state-action probability matrix (<code>Q</code>), and initial state probability (<code>P1</code>).</dd>
<dt>min_len</dt>
<dd>minimum length of generated sequences</dd>
<dt>mean_len</dt>
<dd>mean length of generated sequences</dd>
<dt>return_state</dt>
<dd>logical. Whether generated hidden state sequences should be returned or not.</dd></dl>

Arguments

Simulating action sequences using HMM — sim_hmm

<dl>

<dt>n</dt>
<dd>number of action sequences to be generated</dd>


<dt>paras</dt>
<dd>a list containing specified HMM parameters: state-transition probability matrix (<code>P</code>),
state-action probability matrix (<code>Q</code>), and initial state probability (<code>P1</code>).</dd>


<dt>min_len</dt>
<dd>minimum length of generated sequences</dd>


<dt>mean_len</dt>
<dd>mean length of generated sequences</dd>


<dt>return_state</dt>
<dd>logical. Whether generated hidden state sequences should be returned or not.</dd>

</dl>

sim_hmm: Simulating action sequences using HMM

Description

Usage

Value

Arguments

Examples