msm (version 0.4.1)

ematrix.msm: Misclassification probability matrix

Description

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

Usage

ematrix.msm(x, covariates="mean")

Arguments

x

A fitted multi-state model, as returned by msm

covariates

The covariate values for which to estimate the misclassification probability 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

Value

• A list with components:
• estimate
  Estimated misclassification probability matrix.
• SE
  Corresponding approximate standard errors.

Details

Misclassification probabilities and covariate effects are estimated on the logit scale by msm. A covariance matrix is estimated from the Hessian of the maximised log-likelihood. From these, the delta method is used to obtain standard errors of the probabilities on the natural scale at arbitrary covariate values.

See Also

qmatrix.msm