qmatrix.msm(x, covariates="mean", sojourn=FALSE,
ci=c("delta","normal","bootstrap","none"), cl=0.95, B=1000)
msm
"mean"
, denoting the means of the covariates in
the data (this is the default),
the number 0
, indicating that all the covaria"delta"
(the default) then confidence intervals are
calculated by the delta method, or by simple transformation of the
Hessian in the very simplest cases.
If "normal"
, then calculate a confidence interval by
ci="none"
, then qmatrix.msm
just returns the
estimated transition intensity matrix.
If sojourn
is TRUE
, extra components called
sojourn
, sojournSE
, sojournL
and sojournU
are included, containing the
estimates, standard errors and confidence limits, respectively, of the
mean sojourn times in each transient state. The default print method for objects returned by
qmatrix.msm
presents estimates and confidence limits. To
present estimates and standard errors, do something like
qmatrix.msm(x)[c("estimates","SE")]
msm
. A covariance matrix is estimated from the
Hessian of the maximised log-likelihood. The delta method is used to
obtain from these the standard error of the intensities on the natural
scale at arbitrary covariate values. Confidence limits are calculated
by assuming normality on the log scale.pmatrix.msm
, sojourn.msm
,
deltamethod
, ematrix.msm