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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.
The original call to msm, as returned by match.call.
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.
The standard error matrices corresponding to
Qmatrices.
Corresponding lower and upper symmetric
confidence limits, of width 0.95 unless specified otherwise by the
cl argument.
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.
The standard error matrices corresponding to Ematrices.
Corresponding lower and upper symmetric
confidence limits, of width 0.95 unless specified otherwise by the
cl argument.
Minus twice the maximised log-likelihood.
Derivatives of the minus twice log-likelihood at its maximum.
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.
Vector of transformed maximum likelihood estimates with intensities and probabilities on their natural scales.
Indices of estimates which were fixed during
the maximum likelihood estimation.
Indicator for whether the estimation was performed with covariates centered on their means in the data.
Covariance matrix corresponding to estimates.
Matrix of confidence intervals corresponding to estimates.t
Return value from the optimisation routine (such as
optim or nlm), giving information about
the results of the optimisation.
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.
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.
A list of objects representing the transition matrix
structure and options for likelihood calculation. See
qmodel.object for documentation of the components.
A list of objects representing the misclassification model
structure, for models specified using the ematrix argument to
msm. See emodel.object.
A list of objects representing the model for covariates
on transition intensities. See qcmodel.object.
A list of objects representing the model for covariates
on transition intensities. See ecmodel.object.
A list of objects representing the hidden Markov model
structure. See hmodel.object.
A list giving information about censored states. See
cmodel.object.
Cut points for time-varying intensities, as supplied to
msm, but excluding any that are outside the times
observed in the data.
A list giving information about the parameters of the multi-state
model. See paramdata.object.
Confidence interval width, as supplied to msm.
Formula for covariates on intensities, as supplied to msm.
Formula for covariates on misclassification probabilities, as supplied to msm.
Formula for covariates on hidden Markov model outcomes, as supplied to msm.
Formula for covariates on initial state occupancy
probabilities in hidden Markov models, as supplied to
msm.
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.