Description Usage Arguments Details Value Author(s) See Also
Estimate the mean sojourn times in the transient states of a multistate model and their confidence limits.
1 2  sojourn.msm(x, covariates="mean", ci=c("delta","normal","bootstrap","none"),
cl=0.95, B=1000)

x 
A fitted multistate model, as returned by 
covariates 
The covariate values at which to estimate the mean sojourn times. This can either be: the string the number a list of values, with optional names. For example,

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 
The mean sojourn time in a transient state r is estimated by  1 / q_{rr}, where q_{rr} is the rth entry on the diagonal of the estimated transition intensity matrix.
A continuoustime Markov model is fully specified by the mean sojourn
times and the probability that each state is next (pnext.msm
).
This is a more intuitively meaningful description of a model than the transition intensity
matrix (qmatrix.msm
).
Time dependent covariates, or timeinhomogeneous models, are not supported. This would require the mean of a piecewise exponential distribution, and the package author is not aware of any general analytic form for that.
A data frame with components:
estimates 
Estimated mean sojourn times in the transient states. 
SE 
Corresponding standard errors. 
L 
Lower confidence limits. 
U 
Upper confidence limits. 
C. H. Jackson [email protected]
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