First thanks for your answer.
Now I try to explain better:

I have species in the rows and morphological attributes in the  
columns coded by numbers (qualitative variables; nominal and ordinal).
In one table for the male plants of every species and in the other  
table for the female plants of every species. The variables contain  
every possible occurrence in this species and this gender.
I would like to compare every variable between male and female plants  
for example using a ChiSquare Test.
The Null-hypothesis could be: Variable male is equal to variable Female.

The question behind all is, if male and female plants in this species  
are significantly different and which attributes are responsible for  
this difference.

I really hope that this is better understandable. If not please ask.

Thanks a million in advance.


Greetings

Birgit






Am 20.09.2007 um 20:24 schrieb bbolker:

>
>
>
> Birgit Lemcke wrote:
>>
>>
>> Perhaps you haven´t understood my question in the mail yesterday. So
>> I will try to describe my problem in a different way
>>
>> You see the tables. I would like to test the variables between the
>> tables.
>>
>
> I'm afraid that even before we start to deal with the ambiguities
> your question is not clear.   What do you want to know, and before
> you sat down at the computer what statistical test did you intend
> to use?  (For better or worse, most of the documentation of R
> _assumes_ you know what you want to test and how you want to
> do it.)   I'm supposing you want to do some kind of comparison
> across communities (tables 1 and 2), but I don't know what kind.
> Comparing a single cell of the table to another just asks if
> the leaf form is the same in the two communities.  Do you just
> want to ask if leaf forms of a given species are significantly  
> different
> in different communities?  I'm not sure what the null hypothesis
> would be here.  What are the rows and columns?  Can we use
> them to develop a hypothesis?
>
> If you can say precisely what your question is and how you would
> test it in the _absence_ of ambiguity (i.e., specify a statistical  
> test --
> you don't need to know how to run it in R, that's what the list is
> actually for), then we can help you decide how to handle the
> multiple coding problem.
>
>   good luck
>     Ben Bolker
> -- 
> View this message in context: http://www.nabble.com/Ambiguities-in- 
> vector-tf4485921.html#a12802997
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting- 
> guide.html
> and provide commented, minimal, self-contained, reproducible code.

Birgit Lemcke
Institut für Systematische Botanik
Zollikerstrasse 107
CH-8008 Zürich
Switzerland
Ph: +41 (0)44 634 8351
birgit.lemcke@systbot.uzh.ch






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