Hi:
First my apologies for cross-posting. A few days back I posted my queries ar R-sig-geo but did not get any response. Hence this post.

I am working on two parcel-level housing dataset to estimate the impact of various variables on home sale prices. 

I created the spatial weight metrics in ArcGIS 10 using sale 
year of four nearest houses to assign weights.  Next, I ran LM tests and
 then ran the spatial lag and error models using spdep package. 


I run into five issues. 


Issue 1: When I weight the 10,000-observation first dataset, I get the following message: Non-symmetric neighbors list.   

Is this going to pose problems while running the regression models? If yes, what can I do? 


The code and the results are: 
test1.csv <- read.csv("C:/Article/Housing1/NHspwt.csv") 

class(test1.csv) <- c("spatial.neighbour", class(test1.csv)) 
of <- ordered(test1.csv$OID) 
attr(test1.csv, "region.id") <- levels(of) 
test1.csv$OID <- as.integer(of) 
test1.csv$NID <- as.integer(ordered(test1.csv$NID)) 
attr(test1.csv, "n") <- length(unique(test1.csv$OID)) 

lw_test1.csv <- sn2listw(test1.csv) 
lw_test1.csv$style <- "W" 
lw_test1.csv 

Characteristics of weights list object: 
Neighbour list object: 
Number of regions: 10740 
Number of nonzero links: 42960 
Percentage nonzero weights: 0.03724395 
Average number of links: 4 
Non-symmetric neighbours list 

Weights style: W 
Weights constants summary: 
      n        nn    S0       S1       S2 
W 10740 115347600 10740 3129.831 44853.33 


Issue 2: The spatial lag and error models do not run. I get 
the following message (the models runs on half the data, approx. 5,000 
 observations.  However, I will like to use the entire sample).   

Error: cannot allocate vector of size 880.0 Mb 
In addition: Warning messages: 
1: In t.default(object) : 
  Reached total allocation of 3004Mb: see help(memory.size) 
2: In t.default(object) : 
  Reached total allocation of 3004Mb: see help(memory.size) 
3: In t.default(object) : 
  Reached total allocation of 3004Mb: see help(memory.size) 
4: In t.default(object) : 
  Reached total allocation of 3004Mb: see help(memory.size) 

The code for the lag model is: 
> fmtypecurrentcombinedlag <-lagsarlm(fmtypecurrentcombined, 
data = spssnew, lw_test1.csv, na.action=na.fail, type="lag", 
method="eigen", quiet=TRUE, zero.policy=TRUE, interval = NULL, 
tol.solve=1.0e-20) 

When I am able to read the data file using filehash package. 
 However, I still get the following error message when I run the models:
 Error in matrix(0, nrow = n, ncol = n) : too many elements specified 


Issue 3: For the second dataset that contains approx. 
100,000 observations, I get the following error message when I try to 
run spatial lag or error models. 
Error in matrix(0, nrow = n, ncol = n) : too many elements specified 

The code is: 
> fecurrentcombinedlag <-lagsarlm(fecurrentcombined, data = 
spssall, lw_test2.csv, na.action=na.fail, type="lag", method="eigen", 
quiet=NULL, zero.policy=TRUE, interval = NULL, tol.solve=1.0e-20) 


Issue 5: When I run LM tests I get the test results but with
 the following message: Spatial weights matrix not row standardized. 
 Should I be worried about this considering that I am using the 
4-nearest neighbor rule? 

The code is: 
lm.LMtests(fmtypecurrent, lw_test1.csv, test=c("LMerr", "LMlag", "RLMerr", 
"RLMlag", "SARMA")) 

Thanks 
Shishm 
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