
Yong,

Differences in abundance shouldn't matter provided that you have enough 
individuals to describe the patterns effectively (I would hope for at 
least 100 of each type as a minimum). The method describes pattern 
structure, not intensity (i.e. the density of points), which you should 
of course report separately as well.

Instead of using the bivariate K(r) function, use the cross-type pair 
correlation function, which you can calculate through pcfcross() in 
spatstat. K(r) is a cumulative function which makes it harder to 
interpret visually. The pair correlation function is based on the 
derivative of this, controlled for total area sampled at each distance.

If one species is massively more abundant and their patterns differ then 
you might find variation in the results depending on the focal species, 
so make sure that you check and compare both g12(r) and g21(r).

Before going any further though I recommend you to read the excellent 
spatstat course book which can be downloaded from 
http://www.csiro.au/resources/pf16h. There are a number of other factors 
which you might need to take into account before launching into this test.

All the best,

Markus Eichhorn



On 15/08/12 11:00, r-sig-ecology-request@r-project.org wrote:
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> Date: Wed, 15 Aug 2012 15:37:27 +0800 (CST)
> From: "Yong Shen" <winnie56100@163.com>
> To: r-sig-ecology@r-project.org
> Subject: [R-sig-eco] bivariate K function
> Message-ID: <7b065720.bce5.139293734a1.Coremail.winnie56100@163.com>
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> Hi all,
>
>    I want to used bivariate K function ("Kcross", package ??spatstat??) to perform my analysis, but the number of point of type i are much more than the point of type j, will this affect the results? And if so, how does it affect the results? Thanks a lot.
>
> Best,
>
>                                                                                                                                                          Yong
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