Hi,

I am working on a computer 64-bit OS, with 7.8 GB usable memory (RAM). The allocated quota by my administrator is 12 GB.

Now, I use R's 'spdep' package to run an hedonic pricing model, using the function errorsarlm and the following data:


1)      A spatial weights matrix, converted from a .gwt file to a listw (by means of the nb2listw function; of 1.7 mb). It is in fact a k=4 nearest neighbor matrix for 85684 regions (# of obervations):



Characteristics of weights list object:

Neighbour list object:

Number of regions: 85684

Number of nonzero links: 342736

Percentage nonzero weights: 0.004668316

Average number of links: 4

Non-symmetric neighbours list

Link number distribution:

    4

85684



Weights style: W

Weights constants summary:

      n         nn    S0       S1       S2

W 85684 7341747856 85684 34664.44 377277.6


2)      A CSV data set of 246.3 mb, containing all my variables. Of the 177 variables in this data set, I use 80 variables in the errorsarlm model. Each variable has 85684 observations.

When I run a simple linear regression (lm) based on the 80 variables, I have no problems. But, when I run the errorsalm model I immediately get the following message:


'Error in matrix(0, nrow = n, ncol = n) : too many elements specified'



What I don't know is whether the matrix is sparse (weights of 0.25 0.25 0.25 0.25 for 4 neighbors, and zeros for the remaining 85680 observations) or not.

If errorsarlm works with a sparse matrix, then I understand that I would need much more memory. In that case, is there a way around it? A quick trial with the packages 'ff' and 'biglm' don't resolve anything and the 'bigmemory' package is not available for my R version (the most recent one).



Some direction would be highly appreciated.



Regards,

Diana







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