Chris,
If you only want to investigate if certain "area" (clusters of cells) have
high numbers of fish than others.
You can try to use a cluster analysis. See the packages "cluster" or
"FactoMineR"
Arnaud
-----------------------------------------------------------------
Date: Mon, 12 Dec 2011 10:07:58 -0000
From: "Chris Mcowen" <chrismco...@gmail.com>
To: <r-sig-geo@r-project.org>
Subject: [R-sig-Geo] Removing spatial autocorrelation - Memory limits
Message-ID: <000c01ccb8b5$edfacdd0$
c9f06970$@com>
Content-Type: text/plain
Dear List,
I am trying to model variation is fisheries catch, I have a large data set
(36574
) cells , with knowledge of the tonnes of fish caught in each "Cell" ( .5
degree) global cells.
At present I am simply wanting to investigate if certain "area" (clusters of
cells) have high numbers of fish than others
Due to the grid nature of the data set I have significant spatial
autocorrelation in the data set.
I have tried:
REALM_gls <- gls(tonnesperkm_log~REALM, correlation = corGaus(form =~Lat +
Lon), data = Cells)
coords<-cbind(Cells$Lat,Cells$Lon)
coords<-as.matrix(coords)
nb1.5<-dnearneigh(coords,0,1.5)
nb1.5.w<-nb2listw(nb1.5, glist=NULL, style="W", zero.policy=TRUE)
ols_lm<-lm(Cells$tonnesperkm_log~Cells$REALM)
ols_lm_error_REALM<-errorsarlm(ols_lm, listw=nb1.5.w, na.action = na.omit,
zero.policy = T, data = Cells)
llk1 <- knn2nb(knearneigh(coords, k=1, longlat=FALSE))
col.nb.0.all <- dnearneigh(coords, 0, llk1)
col.nb.0.all
summary(col.nb.0.all)
ols_error_REALM2<-errorsarlm(ols_lm, listw=col.nb.0.all, na.action =
na.omit, zero.policy = T, data = Cells)
However the memory required is large - 16GB.
This won't run on my computer, I therefore had two questions:
First, is the process I am doing correct - or can it be done in a more
efficient way?
Second, can I take a subsample of the data i.e every other cell and run the
analysis? OR find a way of subsetting and "re-joining"?
Thanks in advance,
Chris
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