[R-sig-eco] is 1 hour long enough to assume independance?

Fri Jul 23 23:14:07 CEST 2010

hi chris,

I think you are not quite giving us enough information to assess this 
situation. Otherwise, I´d think that any data coming from ONE dingo 
(i.e. one radiocollar) will never be independent, the 1 hour is not the 
problem. Or can you tell otherwise?

gab

On 7/21/10 3:52 AM, Chris Howden wrote:
> Morning All,
>
> I'm doing a Resource Selection Function Analysis on dingos and we are
> having a bit of a debate on independence.
>
> We're using a landscape unit of 40x40m (from a GIS) and have radio
> collared data every 1 hour. So we can put a dingo in a specific 40x40 grid
> very hour.
>
> I'm concerned about the independence of the data since its only 1 hour
> apart.
>
> As such I'm proposing we split each day up into 4 periods (dawn, dusk,
> night and day)  and randomly sample 1 fix from each. I feel that this data
> will be independent. There is also evidence that dingos act differently in
> these 4 periods, which further increases the chance of independence.
>
> I was wondering what people thought?
>
> Is 1 hour far enough apart to assume independence? Is splitting the day
> into 4 periods and randomly sampling far enough apart to assume
> independence? Or is even that too close, and should it be further apart,
> like 1 day.
>
>
> Chris Howden
> Founding Partner
> Tricky Solutions
> Tricky Solutions 4 Tricky Problems
> Evidence Based Strategic Development, IP development, Data Analysis,
> Modelling, and Training
> (mobile) 0410 689 945
> (fax / office) (+618) 8952 7878
> chris at trickysolutions.com.au
>
> -----Original Message-----
> From: r-sig-ecology-bounces at r-project.org
> [mailto:r-sig-ecology-bounces at r-project.org] On Behalf Of Kingsford Jones
> Sent: Monday, 19 July 2010 4:40 AM
> To: lgj200306
> Cc: r-sig-ecology at r-project.org
> Subject: Re: [R-sig-eco] A question about PCNM analysis
>
> lgj200306,
>
> You didn't tell us, but since the problem was 'all the same' on both
> machines I'm guessing both instances used a 32bit build of R under
> Windows.  If so, you'll be able to access, at most, about 3.5Gb of RAM
> (see RW-FAQ 2.9).  The best solution is to upgrade to a 64bit build
> (IMO preferrably Linux, but a 64bit windows port is now on CRAN).  You
> can also manage memory more carefully.  E.g., the error indicates
> there's no contiguous block of memory to hold an object of size
> 190.7Mb at the time the error's thrown.  That may be because all RAM
> is allocated, or because of fragmentation.  R holds everything in
> memory so when working w/ large objects in a restricted setting you'll
> want to write unneeded objects to disc, clean up, and reload when
> needed (see ?save, ?load, ?rm, and ?gc).  More info can be found at
> ?Memory and by Googling: R memory mangagement.  Also, for some cases
> there are R packages that facilitate memory management: ff, bigmemory,
> biglars, bigtabulate, biganalytics, biglm,...
>
>
> Kingsford Jones
>
> On Sun, Jul 18, 2010 at 4:14 AM, lgj200306<lgj200306 at 163.com>  wrote:
>    
>> Hi, all
>>    I want to do PCNM analysis using vegan and PCNM packages,my R code as
>>      
> follow:
>    
>>    >  bci10m=data.frame(x=rep(1:100,each=50),y=rep(1:50,times=100))
>>    >  bci10m.d=dist(bci10m)
>>    >  library(PCNM)
>>    >  pcnms10m.analysis1=pcnm(bci10m.d)#code 1##using function of pcnm
>>      
> contained in vegan package
>    
>>    >  pcnms10m.analysis2=PCNM(bci10m.d) #code 2##using function of PCNM
>>      
> contained in PCNM package
>    
>>    >  bci20m=data.frame(x=rep(1:50,each=25),y=rep(1:25,times=50))
>>    >  bci20m.d=dist(bci20m)
>>    >  pcnms20m.analysis1=pcnm(bci20m.d)#code 3
>>    >  pcnms20m.analysis2=PCNM(bci20m.d)#code 4
>>
>>
>>    The result shows that code 1,3,4 are all ok, I can get what I want
>>      
> using these three commands. However, code4 can't be carried out. Error
> message shows:"cannot allocate vector of size 190.7 Mb ". I have asked a
> professor about this question, he told me that maybe my computer's memory
> was not enough and suggested me closing the calculation of Moran_I. Then I
> recalculated these codes using another computer that had high capability.
> The problem was all the same. I don't know the reason.
>    
>>    Another question, if I want to know how many pcnm eigenvectors'
>>      
> Moran_I are higher than expected Moran_I after using code1, how can I
> achive it in R?
>    
>>    Thanks for your attention!
>> 2010-07-18
>>
>>
>>
>> lgj200306
>>
>>         [[alternative HTML version deleted]]
>>
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>    

-- 
Dr. Gabriel Singer
Department of Limnology - University of Vienna
and Wassercluster Lunz Biologische Station GmbH
+43-(0)664-1266747
gabriel.singer at univie.ac.at