is 1 hour long enough to assume independance?

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

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