hmodel.object: Developer documentation: hidden Markov model structure object

In msm: Multi-State Markov and Hidden Markov Models in Continuous Time

Developer documentation: hidden Markov model structure object

Description

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 rth 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 rth 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 ith element is the state corresponding to the ith 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.

msm documentation built on Oct. 5, 2024, 1:07 a.m.