pnext.msm {msm} | R Documentation |
Probability of each state being next
Description
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 continuous-time Markov model.
Usage
pnext.msm(x, covariates = "mean",
ci=c("normal","bootstrap","delta","none"), cl = 0.95,
B=1000, cores=NULL)
Arguments
x |
A fitted multi-state 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 |
Details
For a continuous-time 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 continuous-time 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 continuous-time, not discrete-time
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
.
Value
The matrix of probabilities that the next move of a process in state
r
(rows) is to state s
(columns).
Author(s)
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk
See Also
qmatrix.msm
,pmatrix.msm
,qratio.msm