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 "mean" , denoting the means of the covariates in
the data (this is the default),the number 0 , indicating that all the covariates should be
set to zero,or a list of values, with optional names. For example list (60, 1)
where the order of the list follows the order of the covariates originally given in the model formula, or a named list, list (age = 60, sex = 1)
For time-inhomogeneous models fitted using the pci option to
msm , the covariate timeperiod will
the time periods according to the time interval given |
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 "bootstrap" then calculate a confidence interval by
non-parametric bootstrap refitting. This is 1-2 orders of magnitude
slower than the "normal" method, but is expected to be more
accurate. See boot.msm for more details of
bootstrapping in msm.
If "none" (the default) then no confidence interval is
calculated. |
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 |
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).
For non-homogeneous processes, where covariates and hence the
transition intensity matrix are time-dependent, but are
piecewise-constant within the time interval [u,
u+t]
, the function pmatrix.piecewise.msm
can be used.
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