ematrix.msm {msm}R Documentation

Misclassification probability matrix

Description

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

Usage

ematrix.msm(x, covariates="mean", cl=0.95)

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

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. Confidence intervals are estimated by assuming normality on the logit scale.

Value

A list with components:

estimate Estimated misclassification probability matrix.
SE Corresponding approximate standard errors.
L Lower confidence limits.
U Upper confidence limits.


The default print method for objects returned by ematrix.msm presents estimates and confidence limits. To present estimates and standard errors, do something like
ematrix.msm(x)[c("estimates","SE")]

Author(s)

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

See Also

qmatrix.msm


[Package msm version 0.6.3 Index]