msm (version 0.5.2)

qmatrix.msm: Transition intensity matrix

Description

Extract the estimated transition intensity matrix, and the corresponding standard errors, from a fitted multi-state model at a given set of covariate values.

Usage

qmatrix.msm(x, covariates="mean", sojourn=FALSE, cl=0.95)

Arguments

x

A fitted multi-state model, as returned by msm

covariates

The covariate values at which to estimate the intensity matrix. 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 covaria

sojourn

Set to TRUE if the estimated sojourn times and their standard errors should also be returned.

cl

Width of the symmetric confidence interval to present. Defaults to 0.95.

Value

• A list with components:
• estimate
  Estimated transition intensity matrix.
• SE
  Corresponding approximate standard errors.
• L
  Lower confidence limits
• U
  Upper confidence limits
• If sojourn is TRUE, extra components called sojourn and sojournSE are included, containing the estimate and standard errors, 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")]

Details

Transition intensities and covariate effects are estimated on the log scale by 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.

See Also

pmatrix.msm, sojourn.msm, deltamethod, ematrix.msm