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
)A list with components:
Estimated misclassification probability matrix. The rows correspond to true states, and columns observed states.
Corresponding approximate standard errors.
Lower confidence limits.
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")]
A fitted multi-state model, as returned by msm
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)
If "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 simulating
B random vectors from the asymptotic multivariate normal distribution
implied by the maximum likelihood estimates (and covariance matrix) of the
multinomial-logit-transformed misclassification probabilities and covariate
effects, then transforming back.
If "bootstrap" then calculate a confidence interval by non-parametric
bootstrap refitting. This is 1-2 orders of magnitude slower than the
"normal" method, but is expected to be more accurate. See
boot.msm for more details of bootstrapping in msm.
Width of the symmetric confidence interval to present. Defaults to 0.95.
Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs
Number of cores to use for bootstrapping using parallel
processing. See boot.msm for more details.
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk
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.
qmatrix.msm