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.