pnext.msm | R Documentation |
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.
pnext.msm(
x,
covariates = "mean",
ci = c("normal", "bootstrap", "delta", "none"),
cl = 0.95,
B = 1000,
cores = NULL
)
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 |
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
.
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@mrc-bsu.cam.ac.uk
qmatrix.msm
,pmatrix.msm
,qratio.msm
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