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.
hidden 

nstates 
Number of states, the same as 
fitted 

models 
The outcome distribution for each hidden state. A vector
of length 
labels 
String identifying each distribution in 
npars 
Vector of length 
nipars 
Number of initial state occupancy probabilities being
estimated. This is zero if 
totpars 
Total number of parameters, equal to

pars 
A vector of length 
plabs 
List with the names of the parameters in 
parstate 
A vector of length 
firstpar 
A vector of length 
locpars 
Index in 
initprobs 
Initial state occupancy probabilities, as supplied to

est.initprobs 
Are initial state occupancy probabilities
estimated ( 
ncovs 
Number of covariate effects per parameter in 
coveffect 
Vector of covariate effects, of length 
covlabels 
Labels of these effects. 
coveffstate 
Vector indicating state corresponding to each element of 
ncoveffs 
Number of covariate effects on HMM outcomes, equal to 
nicovs 
Vector of length 
icoveffect 
Vector of length 
nicoveffs 
Number of covariate effects on initial state occupancy
probabilities, equal to 
constr 
Constraints on (baseline) hidden Markov model outcome parameters,
as supplied in the 
covconstr 
Vector of constraints on covariate effects in hidden Markov outcome models,
as supplied in the 
ranges 
Matrix of range restrictions for HMM parameters, including
those given to the 
foundse 

initpmat 
Matrix of initial state occupancy probabilities with one
row for each subject (estimated if 
ci 
Confidence intervals for baseline HMM outcome parameters. 
covci 
Confidence intervals for covariate effects in HMM outcome models. 
msm.object
,qmodel.object
, emodel.object
.
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