I like to bootstrap regression models, saving the entire set of bootstrapped
regression coefficients for later use so that I can get confidence limits
for a whole set of contrasts derived from the coefficients. I'm finding
that ordinary bootstrap percentile confidence limits can provide poor
coverage for odds ratios for binary logistic models with small N. So I'm
exploring BCa confidence intervals.
Because the matrix of bootstrapped regression coefficients is generated
outside of the boot packages' boot() function, I need to see if it is
possible to compute BCa intervals using boot's boot.ci() function after
constructing a suitable boot object. The function below almost works, but
it seems to return bootstrap percentile confidence limits for BCa limits.
The failure is probably due to my being new to BCa limits and not
understanding the inner workings. Has anyone successfully done something
similar to this?
Thanks very much,
Frank
bootBCa <- function(estimate, estimates, n, conf.int=0.95) {
require(boot)
if(!exists('.Random.seed')) runif(1)
w <- list(sim= 'ordinary',
stype = 'i',
t0 = estimate,
t = as.matrix(estimates),
R = length(estimates),
data = 1:n,
strata = rep(1, n),
weights = rep(1/n, n),
seed = .Random.seed,
statistic = function(...) 1e10,
call = list('rigged', weights='junk'))
np <- boot.ci(w, type='perc', conf=conf.int)$percent
m <- length(np)
np <- np[c(m-1, m)]
bca <- tryCatch(boot.ci(w, type='bca', conf=conf.int),
error=function(...) list(fail=TRUE))
if(length(bca$fail) && bca$fail) {
bca <- NULL
warning('could not obtain BCa bootstrap confidence interval')
} else {
bca <- bca$bca
m <- length(bca)
bca <- bca[c(m-1, m)]
}
list(np=np, bca=bca)
}
-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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