ematrix.msm {msm} | R Documentation |
Extract the estimated misclassification probability matrix, and corresponding confidence intervals, from a fitted multi-state model at a given set of covariate values.
ematrix.msm(x, covariates="mean", cl=0.95)
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. |
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.
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")]
C. H. Jackson chris.jackson@imperial.ac.uk