Hello,

If you can, you probably should upgrade for R version 3.0.1.

Regards,
Pascal



2013/8/27 Peter Maclean <pmaclean2011@yahoo.com>

> I would like to store a big spatial weight matrix in R memory to do more
> calculation. I know there are memory issue for 32 bit computer and I have
> tried increasing the memory to maximum without success. I am using R
> version 2.15.2 and window vista. The data has about 15,000 observations and
> I would like to create and store a distance based Spatial Weight Matrix in
> R memory. A reproducible codes are as follows and for both codes
> produce out of memory error. The codes works to up 5000 observations.
>
> Based on these codes, (for those with experiece in big data)is it possible
> to use ffbase or bigmemory packages to achieve the objective (keep the
> matrix in memory)? I will appreciate any help.
>
> #Remove all objects
> rm(list=ls())
> library(spdep)
> library(maptools)
> library(GeoXp)
> library(ffbase)
> library(bigmemory)
> ##########################################################################
> #Testing using fake data
> n = 15000
> data = data.frame(n1=1:n)
> data$SEQN  <- seq(1, n,1)
> data$LAT   <- runif(n,  29.0000, 32.0000)
> data$LON   <- runif(n, -100.0000, -89.0000)
> ######################################################################3
> #Create distance based spatial weight matrix using dist function
> #using euclidean method
> coords  <- cbind(data$LON, data$LAT)
> SWM <- as.matrix(dist(coords, method = "euclidean", upper=TRUE))
>
> #Other data management
> data1 <- cbind(data, SWM)
>
>
> #Alternatively
> #Create spatial weight matrix based on distance between Points
> #using the spDistsN1 function and produce the same results as above
> #Create coordinates
> coordinates(data) <- ~LON+LAT
> #Function to calculate the distance matrix
> DistMatrix = function(obj, longlat = FALSE) {
>   return(sapply(1:length(obj[[1]]), function(x)
> spDistsN1(coordinates(obj), coordinates(obj[x,]), longlat)))
> }
> SWMD <- big.matrix(DistMatrix(data))
>
> data2 <- cbind(data, SWMD)
>
> With thanks
>
> Peter Maclean
> Department of Economics
> UDSM
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>
>
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> and provide commented, minimal, self-contained, reproducible code.
>
>

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