int_to_prob

<code>int_to_prob</code> transforms a set of Multinomial logit regression
intercepts to the corresponding state transition or categorical emission
observation probabilities. Note that the first state or category is assumed
to be the reference category, hence no intercept is to specified for the
first state or category.

An implementation of the multilevel (also known as mixed or random
effects) hidden Markov model using Bayesian estimation in R. The multilevel
hidden Markov model (HMM) is a generalization of the well-known hidden
Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The
multilevel HMM is tailored to accommodate (intense) longitudinal data of
multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al.
<doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow
for heterogeneity in the model parameters (transition probability matrix and
conditional distribution), while estimating one overall HMM. The model can
be fitted on multivariate data with either a categorical, normal, or Poisson
distribution, and include individual level covariates (allowing for e.g.,
group comparisons on model parameters). Parameters are estimated using
Bayesian estimation utilizing the forward-backward recursion within a hybrid
Metropolis within Gibbs sampler. Missing data (NA) in the dependent
variables is accommodated assuming MAR. The package also includes various
visualization options, a function to simulate data, and a function to obtain
the most likely hidden state sequence for each individual using the Viterbi
algorithm.

Emmeke Aarts

mHMMbayes

Multilevel Hidden Markov Models Using Bayesian Estimation

Sebastian Mildiner Moraga

int_to_prob function

<dl><dt>int_matrix</dt>
<dd>A matrix with (number of states OR categories - 1) columns
and number of rows to be determined by the user. For obtaining the set of
probabilities of the complete transition probability matrix gamma or
categorical emission distribution matrix, the number of rows equals the
number of states <code>m</code>. The first state / category is assumed to be the
reference category, no intercept is to be specified for this first
category.</dd></dl>

Arguments

Transforming a set of Multinomial logit regression intercepts to probabilities — int_to_prob

<dl>

<dt>int_matrix</dt>
<dd>A matrix with (number of states OR categories - 1) columns
and number of rows to be determined by the user. For obtaining the set of
probabilities of the complete transition probability matrix gamma or
categorical emission distribution matrix, the number of rows equals the
number of states <code>m</code>. The first state / category is assumed to be the
reference category, no intercept is to be specified for this first
category.</dd>

</dl>

int_to_prob: Transforming a set of Multinomial logit regression intercepts to probabilities

Description

Usage

Value

Arguments

Details

See Also

Examples