On Wed, Jul 27, 2011 at 2:09 PM, David Winsemius <dwinsemius@comcast.net>wrote:

>
> On Jul 27, 2011, at 5:36 AM, Rainer M Krug wrote:
>
>  Sorry for re-iterating - but are there any suggestions on how I could
>> tackle
>> this problem?
>>
>
> You could start by providing an operational definition for "identity the
> areas where d.all is significantly larger then d.co and where it is
> significantly smaller" when presumably working from a perspective off not
> knowing anything about the parent distributions for the random draws.


Let me give you some background on my data and how it was obtained:
The data is based on monte carlo simulations of different parameter sets,
where the parameter sets are based on a latin hypercube design. The first
dataset (dat1) is one input parameter for the simulations, i.e. more or less
same probability density for a given range (lets say 1 to 100). Now these
simulations predict the population sizes of two species, and the second
dataset (dat2) are the same parameter, but for which the smulation predicts
co-existence. When deternining the pdfs, I have for some ranges higher
densities for dat2 then dat1, i.e. this range favours co-existence, while in
others less, i.e. dis-favours co-existence.

At the moment, I am just plotting the difference, to indicate in which
parameter range co-existence is favoured, but I would like to have a
statistical measure to back this up, to avoid the argument "these
differences are random effects based on sample size".

Eventually, I would be able to draw a ine along the x-axis, which is green
if the difference is significantly positive, and negative if significantly
negative, and whicte if not different.

But is this possible, and how could I do that?


> I had no notion of piecewise significance of differences in densities and
> figured you were the the one advancing the notion, so you should be defining
> what you meant.


As I said, I have no idea if something like this can be done - probably
moving window type analysis?


> I was expecting one of my statistical betters to step in can correct or
> comment on the statistical issues, but that hasn't yet happened, so if you
> have an operational definition, we could try to implement it across the
> range of the two empiric densities.
>

What I was thinking about, is some kind of moving window analysis, and then
using e.g. a ch-square test, to derive a p value to quantify the differences
in this window? But what about the window size?

Hope this clarifies my question,

Rainer



>
> --
> David
>
>>
>> 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
>>
>>        [[alternative HTML version deleted]]
>>
>> ______________________________**________________
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>> PLEASE do read the posting guide http://www.R-project.org/**
>> posting-guide.html <http://www.R-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> David Winsemius, MD
> West Hartford, CT
>
>


-- 
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

	[[alternative HTML version deleted]]

