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.
TRUE for hidden Markov models, FALSE otherwise.
Number of states, the same as qmodel$nstates.
TRUE if the parameter values in pars are the maximum
likelihood estimates, FALSE if they are the initial values.
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.
String identifying each distribution in models.
Vector of length nstates giving the number of
parameters in each outcome distribution, excluding covariate effects.
Number of initial state occupancy probabilities being
estimated. This is zero if est.initprobs=FALSE, otherwise equal to
the number of states.
Total number of parameters, equal to
sum(npars).
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.
List with the names of the parameters in pars.
A vector of length totpars, whose \(i\)th
element is the state corresponding to the \(i\)th parameter.
A vector of length nstates giving the index in
pars of the first parameter for each state.
Index in pars of parameters which can
have covariates on them.
Initial state occupancy probabilities, as supplied to
msm (initial values before estimation, if est.initprobs=TRUE.)
Are initial state occupancy probabilities
estimated (TRUE or FALSE), as supplied in the
est.initprobs argument of msm.
Number of covariate effects per parameter in pars,
with, e.g. factor contrasts expanded.
Vector of covariate effects, of length sum(ncovs).
Labels of these effects.
Vector indicating state corresponding to each element of coveffect.
Number of covariate effects on HMM outcomes, equal to sum(ncovs).
Vector of length nstates-1 giving the number of
covariate effects on each initial state occupancy probability
(log relative to the baseline probability).
Vector of length sum(nicovs) giving covariate effects on initial state occupancy probabilities.
Number of covariate effects on initial state occupancy
probabilities, equal to sum(nicovs).
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.
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.
Matrix of range restrictions for HMM parameters, including
those given to the hranges argument to msm.
TRUE if standard errors are available for the estimates.
Matrix of initial state occupancy probabilities with one
row for each subject (estimated if est.initprobs=TRUE).
Confidence intervals for baseline HMM outcome parameters.
Confidence intervals for covariate effects in HMM outcome models.