The `msm`

function returns a list with the following components.
These are intended for developers and confident users. To extract results
from fitted model objects, functions such as `qmatrix.msm`

or
`print.msm`

should be used instead.

## Value

- call
The original call to

`msm`

, as returned by`match.call`

.- Qmatrices
A list of matrices. The first component, labelled

`logbaseline`

, is a matrix containing the estimated transition intensities on the log scale with any covariates fixed at their means in the data (or at zero, if`center=FALSE`

). The component labelled`baseline`

is the equivalent on the untransformed scale. Each remaining component is a matrix giving the linear effects of the labelled covariate on the matrix of log intensities. To extract an estimated intensity matrix on the natural scale, at an arbitrary combination of covariate values, use the function`qmatrix.msm`

.- QmatricesSE
The standard error matrices corresponding to

`Qmatrices`

.- QmatricesL,QmatricesU
Corresponding lower and upper symmetric confidence limits, of width 0.95 unless specified otherwise by the

`cl`

argument.- Ematrices
A list of matrices. The first component, labelled

`logitbaseline`

, is the estimated misclassification probability matrix (expressed as as log odds relative to the probability of the true state) with any covariates fixed at their means in the data (or at zero, if`center=FALSE`

). The component labelled`baseline`

is the equivalent on the untransformed scale. Each remaining component is a matrix giving the linear effects of the labelled covariate on the matrix of logit misclassification probabilities. To extract an estimated misclassification probability matrix on the natural scale, at an arbitrary combination of covariate values, use the function`ematrix.msm`

.- EmatricesSE
The standard error matrices corresponding to

`Ematrices`

.- EmatricesL,EmatricesU
Corresponding lower and upper symmetric confidence limits, of width 0.95 unless specified otherwise by the

`cl`

argument.- minus2loglik
Minus twice the maximised log-likelihood.

- deriv
Derivatives of the minus twice log-likelihood at its maximum.

- estimates
Vector of untransformed maximum likelihood estimates returned from

`optim`

. Transition intensities are on the log scale and misclassification probabilities are given as log odds relative to the probability of the true state.- estimates.t
Vector of transformed maximum likelihood estimates with intensities and probabilities on their natural scales.

- fixedpars
Indices of

`estimates`

which were fixed during the maximum likelihood estimation.- center
Indicator for whether the estimation was performed with covariates centered on their means in the data.

- covmat
Covariance matrix corresponding to

`estimates`

.- ci
Matrix of confidence intervals corresponding to

`estimates.t`

- opt
Return value from the optimisation routine (such as

`optim`

or`nlm`

), giving information about the results of the optimisation.- foundse
Logical value indicating whether the Hessian was positive-definite at the supposed maximum of the likelihood. If not, the covariance matrix of the parameters is unavailable. In these cases the optimisation has probably not converged to a maximum.

- data
A list giving the data used for the model fit, for use in post-processing. To extract it, use the methods

`model.frame.msm`

or`model.matrix.msm`

.The format of this element changed in version 1.4 of msm, so that it now contains a

`model.frame`

object`mf`

with all the variables used in the model. The previous format (an ad-hoc list of vectors and matrices) can be obtained with the function`recreate.olddata(msmobject)`

, where`msmobject`

is the object returned by`msm`

.- qmodel
A list of objects representing the transition matrix structure and options for likelihood calculation. See

`qmodel.object`

for documentation of the components.- emodel
A list of objects representing the misclassification model structure, for models specified using the

`ematrix`

argument to`msm`

. See`emodel.object`

.- qcmodel
A list of objects representing the model for covariates on transition intensities. See

`qcmodel.object`

.- ecmodel
A list of objects representing the model for covariates on transition intensities. See

`ecmodel.object`

.- hmodel
A list of objects representing the hidden Markov model structure. See

`hmodel.object`

.- cmodel
A list giving information about censored states. See

`cmodel.object`

.- pci
Cut points for time-varying intensities, as supplied to

`msm`

, but excluding any that are outside the times observed in the data.- paramdata
A list giving information about the parameters of the multi-state model. See

`paramdata.object`

.- cl
Confidence interval width, as supplied to

`msm`

.- covariates
Formula for covariates on intensities, as supplied to

`msm`

.- misccovariates
Formula for covariates on misclassification probabilities, as supplied to

`msm`

.- hcovariates
Formula for covariates on hidden Markov model outcomes, as supplied to

`msm`

.- initcovariates
Formula for covariates on initial state occupancy probabilities in hidden Markov models, as supplied to

`msm`

.- sojourn
A list as returned by

`sojourn.msm`

, with components:`mean`

= estimated mean sojourn times in the transient states, with covariates fixed at their means (if center=TRUE) or at zero (if center=FALSE).`se`

= corresponding standard errors.