This is an attempt to an answer to Geertja van der Heijden's question,
which seems not to have been answered yet.
Your attempt was:
>drought <- read.table("D:/drought080525.txt", header=T)
>regres <- function(x, indices) {
>x <- x[indices,]
>coef(lm(x$AGB ~ x$days, weights=x$weights))
>}
and what you need are the confidence bands for the regression line.
How about generating fitted values out of bootstrap samples the
following way
(untested!):
days <- seq(from=min(x$days), to=max(x$days), length=200)
days.df <- as.data.frame(days)
regres <- function(x, indices) {
x <- x[indices,]
predict(lm(x$AGB ~ x$days), newdata=days.df)
}
predvals <- boot(drought, regres, R=10000, stype="i")
You can then plot the results of boot.ci as lines.