ematrix.msm(x, covariates="mean", ci=c("delta","normal","bootstrap","none"), cl=0.95, B=1000)msm"mean", denoting the means of the covariates in
the data (this is the default),
the number 0, indicating"delta" (the default) then confidence intervals are
calculated by the delta method, or by simple transformation of the
Hessian in the very simplest cases.
If "normal", then calculate a confidence interval by ci="none", then ematrix.msm just returns the
estimated misclassification probability matrix.
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")]
msm. A covariance matrix
is estimated from the Hessian of the maximised log-likelihood. From
these, the delta method can be used to obtain standard errors of the
probabilities on the naturalscale at arbitrary covariate values.
Confidence intervals are estimated by assuming normality on the
multinomial-logit scale.qmatrix.msm