Extract the estimated misclassification probability matrix, and corresponding confidence intervals, from a fitted multistate model at a given set of covariate values.
1 2  ematrix.msm(x, covariates="mean", ci=c("delta","normal","bootstrap","none"),
cl=0.95, B=1000, cores=NULL)

x 
A fitted multistate model, as returned by 
.
covariates 
The covariate values for which to estimate the misclassification
probability matrix. This can either be: the string the number or a list of values, with optional names. For example
where the order of the list follows the order of the covariates originally given in the model formula, or a named list,

ci 
If If If 
cl 
Width of the symmetric confidence interval to present. Defaults to 0.95. 
B 
Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs 
cores 
Number of cores to use for bootstrapping using parallel
processing. See 
Misclassification probabilities and covariate effects are estimated on
the multinomiallogit scale by msm
. A covariance matrix
is estimated from the Hessian of the maximised loglikelihood. From
these, the delta method can be 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
multinomiallogit scale.
A list with components:
estimate 
Estimated misclassification probability matrix. The rows correspond to true states, and columns observed states. 
SE 
Corresponding approximate standard errors. 
L 
Lower confidence limits. 
U 
Upper confidence limits. 
Or if 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")]
C. H. Jackson chris.jackson@mrcbsu.cam.ac.uk
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