msm objects.Extract the data from a multi-state model fitted with msm.
# S3 method for msm
model.frame(formula, agg=FALSE, …)
# S3 method for msm
model.matrix(object, model="intens", state=1, …)A fitted multi-state model object, as returned by
msm.
Return the model frame in the efficient aggregated form
used to calculate the likelihood internally for non-hidden Markov
models. This has one row for each unique combination of from-state,
to-state, time lag, covariate value and observation type. The
variable named "(nocc)" counts how many observations of that
combination there are in the original data.
A fitted multi-state model object, as returned by
msm.
"intens" to return the design matrix for covariates on
intensities, "misc" for misclassification probabilities, "hmm" for a
general hidden Markov model, and "inits" for initial state
probabilities in hidden Markov models.
State corresponding to the required covariate design matrix in a hidden Markov model.
Further arguments (not used).
model.frame returns a data frame with all the original
variables used for the model fit, with any missing data removed (see
na.action in msm). The state, time, subject,
obstype and obstrue variables are named
"(state)", "(time)", "(subject)",
"(obstype)" and "(obstrue)" respectively (note the
brackets). A variable called "(obs)" is the observation number
from the original data before any missing data were dropped.
The variable "(pcomb)" is used for computing the likelihood for hidden
Markov models, and identifies
which distinct time difference, obstype and covariate values
(thus which distinct interval transition probability matrix) each observation
corresponds to.
The model frame object has some other useful attributes, including
"usernames" giving the user's original names for these
variables (used for model refitting, e.g. in bootstrapping or cross
validation) and "covnames" identifying which ones are covariates.
model.matrix returns a design matrix for a part of the
model that includes covariates. The required part is indicated by the
"model" argument.
For time-inhomogeneous models fitted with "pci", these datasets
will have imputed observations at each time change point, indicated
where the variable "(pci.imp)" in the model frame is 1. The
model matrix for intensities will have factor contrasts for the
timeperiod covariate.