Developer documentation: hidden Markov model structure object

A list giving information about the models for the outcome data conditionally on the states of a hidden Markov model. Used in internal computations, and returned in a fitted msm model object.

Value

hidden

TRUE for hidden Markov models, FALSE otherwise.

nstates

Number of states, the same as qmodel$nstates.

fitted

TRUE if the parameter values in pars are the maximum likelihood estimates, FALSE if they are the initial values.

models

The outcome distribution for each hidden state. A vector of length nstates whose \(r\)th entry is the index of the state \(r\) outcome distributions in the vector of supported distributions. The vector of supported distributions is given in full by msm:::.msm.HMODELS: the first few are 1 for categorical outcome, 2 for identity, 3 for uniform and 4 for normal.

labels

String identifying each distribution in models.

npars

Vector of length nstates giving the number of parameters in each outcome distribution, excluding covariate effects.

nipars

Number of initial state occupancy probabilities being estimated. This is zero if est.initprobs=FALSE, otherwise equal to the number of states.

totpars

Total number of parameters, equal to sum(npars).

pars

A vector of length totpars, made from concatenating a list of length nstates whose \(r\)th component is vector of the parameters for the state \(r\) outcome distribution.

plabs

List with the names of the parameters in pars.

parstate

A vector of length totpars, whose \(i\)th element is the state corresponding to the \(i\)th parameter.

firstpar

A vector of length nstates giving the index in pars of the first parameter for each state.

locpars

Index in pars of parameters which can have covariates on them.

initprobs

Initial state occupancy probabilities, as supplied to msm (initial values before estimation, if est.initprobs=TRUE.)

est.initprobs

Are initial state occupancy probabilities estimated (TRUE or FALSE), as supplied in the est.initprobs argument of msm.

ncovs

Number of covariate effects per parameter in pars, with, e.g. factor contrasts expanded.

coveffect

Vector of covariate effects, of length sum(ncovs).

covlabels

Labels of these effects.

coveffstate

Vector indicating state corresponding to each element of coveffect.

ncoveffs

Number of covariate effects on HMM outcomes, equal to sum(ncovs).

nicovs

Vector of length nstates-1 giving the number of covariate effects on each initial state occupancy probability (log relative to the baseline probability).

icoveffect

Vector of length sum(nicovs) giving covariate effects on initial state occupancy probabilities.

nicoveffs

Number of covariate effects on initial state occupancy probabilities, equal to sum(nicovs).

constr

Constraints on (baseline) hidden Markov model outcome parameters, as supplied in the hconstraint argument of msm, excluding covariate effects, converted to a vector and mapped to the set 1,2,3,... if necessary.

covconstr

Vector of constraints on covariate effects in hidden Markov outcome models, as supplied in the hconstraint argument of msm, excluding baseline parameters, converted to a vector and mapped to the set 1,2,3,... if necessary.

ranges

Matrix of range restrictions for HMM parameters, including those given to the hranges argument to msm.

foundse

TRUE if standard errors are available for the estimates.

initpmat

Matrix of initial state occupancy probabilities with one row for each subject (estimated if est.initprobs=TRUE).

ci

Confidence intervals for baseline HMM outcome parameters.

covci

Confidence intervals for covariate effects in HMM outcome models.

See also

msm.object,qmodel.object, emodel.object.