Sorry for re-iterating - but are there any suggestions on how I could tackle
this problem?
Thanks,
Rainer
On Tue, Jul 26, 2011 at 2:58 PM, Rainer M Krug <r.m.k...@gmail.com> wrote:
> Hi
>
> this might be a little bit off topic, but here it goes: lets assume I have
> the following:
>
> set.seed(13)
> dat1 <- rnorm(2000, mean=10, sd=10)
> dat2 <- rnorm(100, mean=10, sd=20)
> d.all <- density(dat, n=1024)
> d.co <- density(x[[v]], , from=min(d.all$x), to=max(d.all$x),
> n=1024)
> d.diff <- list(
> x = d.all$x,
> y = d.all$y - d.co$y
> )
>
> ylim <- range(c(d.all$y, d.co$y, d.diff$y))
> plot(
> d.all,
> ylim = ylim
> )
> abline(h=0)
> lines(d.co, col="red")
> lines(d.diff$x, d.diff$y, col="blue")
>
> Now I would like to identify the areas where d.all is significantly larger
> then d.co and where it is significantly smaller.
>
> What is the easiest approach to do this? At the moment I am not doing any
> tests, but I am sure there is a way to determine the ranges statistically?
>
> Thanks,
>
> Rainer
>
> --
> Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology,
> UCT), Dipl. Phys. (Germany)
>
> Centre of Excellence for Invasion Biology
> Stellenbosch University
> South Africa
>
> Tel : +33 - (0)9 53 10 27 44
> Cell: +33 - (0)6 85 62 59 98
> Fax (F): +33 - (0)9 58 10 27 44
>
> Fax (D): +49 - (0)3 21 21 25 22 44
>
> email: rai...@krugs.de
>
> Skype: RMkrug
>
>
--
Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology,
UCT), Dipl. Phys. (Germany)
Centre of Excellence for Invasion Biology
Stellenbosch University
South Africa
Tel : +33 - (0)9 53 10 27 44
Cell: +33 - (0)6 85 62 59 98
Fax (F): +33 - (0)9 58 10 27 44
Fax (D): +49 - (0)3 21 21 25 22 44
email: rai...@krugs.de
Skype: RMkrug
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