bootci.fmsm | R Documentation |
Calculate a confidence interval for a model output by repeatedly replacing the parameters in a fitted model object with a draw from the multivariate normal distribution of the maximum likelihood estimates, then recalculating the output function.
bootci.fmsm( x, B, fn, cl = 0.95, attrs = NULL, cores = NULL, sample = FALSE, ... )
x |
Output from |
B |
Number of parameter draws to use |
fn |
Function to bootstrap the results of. It must have an argument named 'codex giving a fitted flexsurv model object. This may return a value with any format, e.g. list, matrix or vector, as long as it can be converted to a numeric vector with |
cl |
Width of symmetric confidence interval, by default 0.95 |
attrs |
Any attributes of the value returned from |
cores |
Number of cores to use for parallel processing. |
sample |
If |
... |
Additional arguments to pass to |
A matrix with two rows, giving the upper and lower confidence limits respectively. Each row is a vector of the same length as the unlisted result of the function corresponding to fncall
.
## How to use bootci.msm ## Write a function with one argument called x giving a fitted model, ## and returning some results of the model. The results may be in any form. tmat <- rbind(c(NA,1,2),c(NA,NA,3),c(NA,NA,NA)) bexp <- flexsurvreg(Surv(Tstart, Tstop, status) ~ trans, data=bosms3, dist="exp") summfn <- function(x, t){ resp <- pmatrix.fs(x, trans=tmat, t=t) rest <- totlos.fs(x, trans=tmat, t=t) list(resp, rest) } ## Use bootci.msm to obtain the confidence interval ## The matrix columns are in the order of the unlisted results of the original ## summfn. You will have to rearrange them into the format that you want. ## If summfn has any extra arguments, in this case \code{t}, make sure they are ## passed through via the ... argument to bootci.fmsm bootci.fmsm(bexp, B=3, fn=summfn, t=10) bootci.fmsm(bexp, B=3, fn=summfn, t=5)
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