Extract the data from a multi-state model fitted with msm.
Usage
# S3 method for class 'msm'
model.frame(formula, agg = FALSE, ...)
# S3 method for class 'msm'
model.matrix(object, model = "intens", state = 1, ...)Arguments
- formula
A fitted multi-state model object, as returned by
msm.- agg
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.- ...
Further arguments (not used).
- object
A fitted multi-state model object, as returned by
msm.- model
"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
State corresponding to the required covariate design matrix in a hidden Markov model.
Value
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.
Author
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk