msm (version 1.2)

pnext.msm: 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("delta","normal","bootstrap","none"), cl = 0.95,
           B=1000)

Arguments

x

A fitted multi-state model, as returned by msm.

covariates

The covariate values at which to estimate the intensities. 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 s

ci

If "delta" (the default) then confidence intervals are calculated by the delta method. If "normal", then calculate a confidence interval by simulating B random vectors from the asymptotic multiv

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.

Value

• The matrix of probabilities that the next move of a process in state $r$ (rows) is to state $s$ (columns).

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).

The model is fully parameterised 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.

See Also

qmatrix.msm,pmatrix.msm,qratio.msm