On Sat, 13 Jan 2024 16:54:01 -0800
Bert Gunter <bgunter.4...@gmail.com> wrote:
> Well, this would seem to work:
>
> e <- data.frame(Score = Score
> , Country = factor(Country)
> , Time = Time)
>
> ncountry <- nlevels(e$Country)
> func= function(dat,idx) {
> if(length(unique(dat[idx,'Country'])) < ncountry) NA
> else coef(lm(Score~ Time + Country,data = dat[idx,]))
> }
> B <- boot(e, func, R=1000)
>
> boot.ci(B, index=2, type="perc")
>
> Caveats:
> 1) boot.ci handles the NA's by omitting them, which of course gives a
> smaller resample and longer CI's than the value of R specified in the
> call to boot().
>
> 2) I do not know if the *nice* statistical properties of the
> nonparametric bootstrap, e.g. asymptotic correctness, hold when
> bootstrap samples are produced in this way. I leave that to wiser
> heads than me.
<SNIP>
It seems to me that my shaganappi idea causes func() to return a vector
of coefficients with NAs corresponding to any missing levels of the
"Country" factor, whereas your idea causes it to return a scalar NA
whenever one or more of the levels of the "Country" factor is missing.
I have no idea what the implications of this are. As I said before, I
have no idea what I am doing!
cheers,
Rolf
--
Honorary Research Fellow
Department of Statistics
University of Auckland
Stats. Dep't. (secretaries) phone:
+64-9-373-7599 ext. 89622
Home phone: +64-9-480-4619
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