pmatrix.msm {msm} | R Documentation |
Extract the estimated transition probability matrix from a fitted multi-state model for a given time interval, at a given set of covariate values.
pmatrix.msm(x, t=1, t1=0, covariates="mean", ci=c("none","normal","bootstrap"), cl=0.95, B=1000, ...)
x |
A fitted multi-state model, as returned by msm . |
t |
The time interval to estimate the transition probabilities for, by default one unit. |
t1 |
The starting time of
the interval. Used for models x with piecewise-constant intensities fitted
using the pci option to msm . The probabilities will be computed on the interval [t1, t1+t]. |
covariates |
The covariate values at which to estimate the transition
probabilities. 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,
For time-inhomogeneous models fitted using the
For time-inhomogeneous models fitted "by hand" by using a
time-dependent covariate in the |
ci |
If "normal" , then calculate a confidence interval for
the transition probabilities by simulating B random vectors
from the asymptotic multivariate normal distribution implied by the
maximum likelihood estimates (and covariance matrix) of the log
transition intensities and covariate effects, then calculating the
resulting transition probability matrix for each replicate.
If
If |
cl |
Width of the symmetric confidence interval, relative to 1. |
B |
Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs |
... |
Optional arguments to be passed to MatrixExp to
control the method of computing the matrix exponential. |
For a continuous-time homogeneous Markov process with transition intensity matrix Q, the probability of occupying state s at time u + t conditionally on occupying state r at time u is given by the (r,s) entry of the matrix P(t) = exp(tQ), where exp() is the matrix exponential.
For non-homogeneous processes, where covariates and hence the
transition intensity matrix Q are piecewise-constant in time,
the transition probability matrix is calculated as
a product of matrices over a series of intervals, as explained in
pmatrix.piecewise.msm
.
The pmatrix.piecewise.msm
function is only necessary for models fitted using a
time-dependent covariate in the covariates
argument to
msm
. For time-inhomogeneous models fitted using "pci",
pmatrix.msm
can be used, with arguments t
and t1
,
to calculate transition probabilities over any time period.
The matrix of estimated transition probabilities P(t) in the given time. Rows correspond to "from-state" and columns to "to-state".
Or if ci="normal"
or ci="bootstrap"
, pmatrix.msm
returns a list with
components estimates
and ci
, where estimates
is
the matrix of estimated transition probabilities, and ci
is a
list of two matrices containing the upper and lower confidence
limits.
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk.
qmatrix.msm
, pmatrix.piecewise.msm
, boot.msm