ematrix.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",
ci = c("delta", "normal", "bootstrap", "none"),
cl = 0.95,
B = 1000,
cores = NULL
)
x |
A fitted multi-state 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
multinomial-logit scale by 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 natural scale at arbitrary covariate values. Confidence intervals are
estimated by assuming normality on the multinomial-logit 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@mrc-bsu.cam.ac.uk
qmatrix.msm
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