Compute a matrix of the probability of each state s being the
next state of the process after each state r. Together with
the mean sojourn times in each state (sojourn.msm
),
these fully define a continuoustime Markov model.
1 2 3 
x 
A fitted multistate model, as returned by

covariates 
The covariate values at which to estimate the intensities.
This can either be: the string the number or a list of values, with optional names. For example
where the order of the list follows the order of the covariates originally given in the model formula, or a named list,

ci 
If If If 
cl 
Width of the symmetric confidence interval to present. Defaults to 0.95. 
B 
Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs. 
cores 
Number of cores to use for bootstrapping using parallel
processing. See 
For a continuoustime Markov process in state r, the probability
that the next state is s is q_{rs} / q_{rr}, where
q_{rs} is the transition intensity (qmatrix.msm
).
A continuoustime Markov model is fully specified by these probabilities together with the mean sojourn times 1/q_{rr} in each state r. This gives a more intuitively meaningful description of a model than the intensity matrix.
Remember that msm deals with continuoustime, not discretetime
models, so these are not the same as the probability of observing
state s at a fixed time in the future. Those probabilities are
given by pmatrix.msm
.
The matrix of probabilities that the next move of a process in state r (rows) is to state s (columns).
C. H. Jackson chris.jackson@mrcbsu.cam.ac.uk
qmatrix.msm
,pmatrix.msm
,qratio.msm
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