ematrix.msm {msm} | R Documentation |
Extract the estimated misclassification probability matrix, and the corresponding standard errors, from a fitted multi-state model at a given set of covariate values.
ematrix.msm(x, covariates="mean")
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 that all the covariates should be
set to zero,or a list of values, with optional names. For example list (60, 1)
where the order of the list follows the order of the covariates originally given in the model formula, or a named list, list (age = 60, sex = 1)
|
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. The delta method is used to
obtain from these the standard error of the probabilities on the
natural scale at arbitrary covariate values.
A list with components:
|
Estimated misclassification probability matrix. |
|
Corresponding approximate standard errors. |
C. H. Jackson chris.jackson@imperial.ac.uk