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