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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