ematrix.msm {msm}R Documentation

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 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)

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. The delta method is used to obtain from these the standard error of the probabilities on the natural scale at arbitrary covariate values.

Value

A list with components:

estimate Estimated misclassification probability matrix.
SE Corresponding approximate standard errors.

Author(s)

C. H. Jackson chris.jackson@imperial.ac.uk

See Also

qmatrix.msm

Examples






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