Dear List,

The problem I encountered was resolved, there was a problem in read.gal ().
If a problem of this nature occurs, following are some hints to dig out the
problem (as per Prof. Bivand's suggestion):

Important points:
Step 1. Check the same model in both GEODA (OLS model) and R (regular linear
model).  Does result agree? if  so you are using same dataset. So there is
not a problem on the data.

Step 2. Check if the summary numbers for the neighbors agree, if not, the
GAL files are not being represented in the same way.

Step 3. See if the weights agree in the both system (GEODA and R), by
computing an univariate moran for a variable, supposedly NOT have
autocorrelation. MY problem was on this step basically in defining read.gal().
It might depend on your original data file type:
i) if it is a "csv" file, then tatagal<-read.gal(tata, region.id=tata$CID),
suppose the data file is: tata<-read.csv(".csv" ,header=TRUE) and say,CID,
is the ID variable used to link datafile with shape file. Instad, i used
tatagal<-read.gal(tata, override.id=TRUE).

ii) BUT, in my experience, if your data file is a TEXT file, then reading
the data in R as: tata<-read.csv(".txt",header=TRUE, skip=1) and defining
the gal file as: tatagal<-read.gal(tata, override.id=TRUE) also work.

So how you define read.gal() is important .

Sometimes, the different results in R and GEODA might be due to collinearity
of model variables. It is wise to check the multicollinearity by
library(car) and checking the variance inflation factor (vif). VIF more than
4 needs attention.
Suppose, you have a model:
lm1<-y~x1+x2+x3
summary(lm1)
library(car)
vif(lm1)


-- 
Ram Pandit
1449 Richland Road
Apartment 3M
Auburn, AL 36832

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