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.krug@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:      Rainer@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:      Rainer@krugs.de

Skype:      RMkrug

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