Nothing
`boot.mlds` <- function(x, nsim, no.warn = TRUE, ...) {
if (no.warn){
old.opt <- options(warn = -1)
on.exit(old.opt)
}
d <- if (x$method == "glm")
as.matrix(x$obj$data[, -1]) else
as.matrix(make.ix.mat(x$data)[, -1])
# as.mlds.df(x$obj$data) else
# x$data
p <- fitted(x)
rsim <- matrix(rbinom(length(p) * nsim, 1, p),
nrow = length(p), ncol = nsim)
bts.samp <- apply(rsim, 2, function(y, dd) {
# dd$resp <- x
# psct <- mlds(dd, ...)$pscale
psct <- glm.fit(dd, y, family = binomial(x$link), ...)$coefficients
names(psct) <- x$stimulus[-1]
c(psct, sigma = 1)/psct[length(psct)]
},
dd = d)
res <- list(boot.samp = bts.samp,
bt.mean = apply(bts.samp, 1, mean),
bt.sd = apply(bts.samp, 1, sd),
N = nsim
)
class(res) <- c("mlds.bt", "list")
res
}
`boot.mlbs` <- function (x, nsim, no.warn = TRUE, ...)
{
if (no.warn){
old.opt <- options(warn = -1)
on.exit(old.opt)
}
d <- as.matrix(x$obj$data[, -1])
p <- fitted(x)
rsim <- matrix(rbinom(length(p) * nsim, 1, p), nrow = length(p),
ncol = nsim)
bts.samp <- apply(rsim, 2, function(y, dd) {
psct <- glm.fit(dd, y, family = binomial(x$link), ...)$coefficients
names(psct) <- x$stimulus[-1]
c(psct, sigma = 1)/psct[length(psct)]
}, dd = d)
res <- list(boot.samp = bts.samp, bt.mean = apply(bts.samp, 1, mean),
bt.sd = apply(bts.samp, 1, sd), N = nsim)
class(res) <- c("mlds.bt", "list")
res
}
`summary.mlds.bt` <- function(object, standard.scale = TRUE, sigma = FALSE, ...) {
n <- nrow(object$boot.samp)
if (sigma && standard.scale)
print(c(object$bt.mean[n],
SD = object$bt.sd[n]))
if (standard.scale){
with(object,
cbind(mean = c(0, bt.mean[-n]),
SD = c(0, bt.sd[-n])))
} else {
unnorm <- with(object,
apply(boot.samp, 2, function(x) x[-n]/x[n]))
cbind(means = c(0, rowMeans(unnorm)),
SD = c(0, apply(unnorm, 1, sd))) # c(0, sd(t(unnorm))))
}
}
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