"Liaw, Andy" wrote:
>
> I looked at rqss() in nprq, as Prof. Koenker suggested, but that doesn't
> have a predict() method, so I don't know how you'd get the smooth at values
> other than the observed...
>
> The criteria (CV, GCV, etc.) could have multiple local minima for some data,
> as Prof. Ripley and Prof. Koenker pointed out, so relying on those
> `automatic' selection procedure may not be the best thing to do.
> Theoretically as spar (lambda) goes to 0, smooth.spline should linearly
> interpolate the data. I guess the routine could run into numerical problems
> before that.
>
> Here's yet another thing to try (thanks to Martin for the `lokern' package):
>
> library(lokerns)
> par(mfrow=c(2,4))
> for (i in 1:4) {
> plot(dat[[i]]$p, dat[[i]]$t);
> lines(lokerns(dat[[i]]$p, dat[[i]]$t, x.out=seq(25,1000,25)))
> plot(dat[[i]]$p, dat[[i]]$s)
> lines(lokerns(dat[[i]]$p, dat[[i]]$s, x.out=seq(25,1000,25)))
> }
>
> Best,
> Andy
Thanks for still another suggestion. I'm keeping our system manager
busy loading packages. Is there a way he can get them all at once?
Also, do you know whether any of these packages come with a manual ---
other than the help pages?
Regards,
Carlisle
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