R: Developer documentation: hidden Markov model structure object

hmodel.object {msm} | R Documentation

## 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`.

[Package msm version 1.6.8 Index]