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

 

 


	[[alternative HTML version deleted]]

