hmodel.object: 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. - nstatesNumber 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. - modelsThe 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. - labelsString identifying each distribution in
models. - nparsVector of length
nstates giving the number of
parameters in each outcome distribution, excluding covariate effects. - niparsNumber of initial state occupancy probabilities being
estimated. This is zero if
est.initprobs=FALSE, otherwise equal to
the number of states. - totparsTotal number of parameters, equal to
sum(npars). - parsA 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. - plabsList with the names of the parameters in
pars. - parstateA vector of length
totpars, whose $i$th
element is the state corresponding to the $i$th parameter. - firstparA vector of length
nstates giving the index in
pars of the first parameter for each state. - linksLink function used for the outcome for each state.
- locparsIndex in
pars of parameters which can
have covariates on them. - initprobsInitial state occupancy probabilities, as supplied to
msm (initial values before estimation, if est.initprobs=TRUE.) - est.initprobsAre initial state occupancy probabilities
estimated (
TRUE or FALSE), as supplied in the
est.initprobs argument of msm. - ncovsNumber of covariate effects per parameter in
pars,
with, e.g. factor contrasts expanded. - coveffectVector of covariate effects, of length
sum(ncovs). - covlabelsLabels of these effects.
- coveffstateVector indicating state corresponding to each element of
coveffect. - ncoveffsNumber of covariate effects on HMM outcomes, equal to
sum(ncovs). - nicovsVector of length
nstates-1 giving the number of
covariate effects on each initial state occupancy probability
(log relative to the baseline probability). - icoveffectVector of length
sum(nicovs) giving covariate effects on initial state occupancy probabilities. - nicoveffsNumber of covariate effects on initial state occupancy
probabilities, equal to
sum(nicovs). - constrConstraints 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. - covconstrVector 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. - rangesMatrix 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. - initpmatMatrix of initial state occupancy probabilities with one
row for each subject (estimated if
est.initprobs=TRUE). - ciConfidence intervals for baseline HMM outcome parameters.
- covciConfidence intervals for covariate effects in HMM outcome models.