mc_to_sc.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mc_to_sc.R
\name{mc_to_sc}
\alias{mc_to_sc}
\title{Transform a Multichannel Hidden Markov Model into a Single Channel Representation}
\usage{
mc_to_sc(model, combine_missing = TRUE, all_combinations = FALSE, cpal)
}
\arguments{
\item{model}{An object of class \code{hmm} or \code{mhmm}.}

\item{combine_missing}{Controls whether combined states of observations 
at time \eqn{t} are coded missing (coded with \eqn{*} in \code{stslist}s) 
if one or more of the channels include missing information at time \eqn{t}. 
Defaults to \code{TRUE}. \code{FALSE} keeps missing states
as they are, producing more states in data; e.g. \eqn{single/childless/*} 
where the observation in channel 3 is missing.}

\item{all_combinations}{Controls whether all possible combinations of
observed states are included in the single channel representation or only
combinations that are found in the data. Defaults to \code{FALSE}, i.e.
only actual observations are included.}

\item{cpal}{The color palette used for the new combined symbols. Optional in 
a case where the number of symbols is less or equal to 200 (in which case 
the \code{seqHMM::colorpalette} is used).}
}
\description{
Transforms data and parameters of a multichannel model into a single 
channel model. Observed states (symbols) are combined and parameters 
multiplied across channels.
}
\details{
Note that in case of no missing observations, the log-likelihood of 
the original and transformed models are identical but the AIC and BIC 
can be different as the model attribute \code{df} is recomputed based 
on the single channel representation.
}
\examples{
# Loading a hidden Markov model of the biofam data (hmm object)
data("hmm_biofam")

# Convert the multichannel model to a single-channel model
sc <- mc_to_sc(hmm_biofam)

# Likelihoods of the single-channel and the multichannel model are the same
# (Might not be true if there are missing observations)
logLik(sc)
logLik(hmm_biofam)
}
\seealso{
\code{\link{build_hmm}} and \code{\link{fit_model}} for building and
  fitting Hidden Markov models; and \code{\link{hmm_biofam}} for information on
  the model used in the example.
}