msm.object: Fitted msm model objects

Description

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.

Arguments

Value

callThe original call to msm, as returned by match.call.
QmatricesA 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.
QmatricesSEThe standard error matrices corresponding to Qmatrices.
QmatricesL,QmatricesUCorresponding lower and upper symmetric confidence limits, of width 0.95 unless specified otherwise by the cl argument.
EmatricesA 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.
EmatricesSEThe standard error matrices corresponding to Ematrices.
EmatricesL,EmatricesUCorresponding lower and upper symmetric confidence limits, of width 0.95 unless specified otherwise by the cl argument.
minus2loglikMinus twice the maximised log-likelihood.
derivDerivatives of the minus twice log-likelihood at its maximum.
estimatesVector 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.tVector of transformed maximum likelihood estimates with intensities and probabilities on their natural scales.
fixedparsIndices of estimates which were fixed during the maximum likelihood estimation.
centerIndicator for whether the estimation was performed with covariates centered on their means in the data.
covmatCovariance matrix corresponding to estimates.
ciMatrix of confidence intervals corresponding to estimates.t
optReturn value from the optimisation routine (such as optim or nlm), giving information about the results of the optimisation.
foundseLogical 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.
dataA 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.
qmodelA list of objects representing the transition matrix structure and options for likelihood calculation. See qmodel.object for documentation of the components.
emodelA list of objects representing the misclassification model structure, for models specified using the ematrix argument to msm. See emodel.object.
qcmodelA list of objects representing the model for covariates on transition intensities. See qcmodel.object.
ecmodelA list of objects representing the model for covariates on transition intensities. See ecmodel.object.
hmodelA list of objects representing the hidden Markov model structure. See hmodel.object.
cmodelA list giving information about censored states. See cmodel.object.
pciCut points for time-varying intensities, as supplied to msm, but excluding any that are outside the times observed in the data.
paramdataA list giving information about the parameters of the multi-state model. See paramdata.object.
clConfidence interval width, as supplied to msm.
covariatesFormula for covariates on intensities, as supplied to msm.
misccovariatesFormula for covariates on misclassification probabilities, as supplied to msm.
hcovariatesFormula for covariates on hidden Markov model outcomes, as supplied to msm.
initcovariatesFormula for covariates on initial state occupancy probabilities in hidden Markov models, as supplied to msm.
sojournA 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.