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Spatial Regression

5 messages · youngbin, Roger Bivand, Danlin Yu +1 more

Original
youngbin
# 
Hi,

1. While conducting spatial regression models, R does not directly provide
the Rsquared values. Does anybody have an idea how to get the Pseudo
Rsquared values in spatial regression models? 

2. Regarding spatial regression models, how to conduct the general spatial
model which both the lag and error are included?

Thanks 

youngbin
Roger Bivand
#
On Fri, 19 Jun 2009, youngbin wrote:

            

      
      
      
    

        
The models are fitted with maximum likelihood, so R squared is not a very 
suitable measure, although I'm sure you can find various ways of computing 
them. On the other hand, you can also get the AIC and log-likelihood for 
OLS and some other models, and they also provide a way of comparing 
models.

            

      
      
      
    

        
This is not provided, and is not even well understood in spatial 
statistics (there are very complicated interactions between the lag and 
error components). Spatial Durbin models do provide a general structure 
within which both lag and error models nest. If both spatial coefficients 
are significant in a general model, you know with little chance of mistake 
that your model is badly misspecified, I'm afraid. The only possible 
alternative is that you have well-motivated behavioural models for both 
processes and their interactions.

Hope this helps,

Roger

            

      
      
      
    

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        
      

    

    
3 days later

    

      
      

        
      

    

    

      
      

        


  

        

      Roger Bivand
    

    

      
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On Tue, 23 Jun 2009, Adrian Toti wrote:

        

            

      
      
      
    

        
No, the original questioner was thinking of a model like:

y = \rho W y + X \beta + u

u = \lambda W u + e

with both a lag coefficient \rho and an error coefficient \lambda. You end 
up with a messy interaction between the \rho and \lambda terms, something 
like:

(I - \rho W) (I - \lambda W) y = (I - \lambda W) X \beta + e

or

y = (I - \rho W)^{-1} X \beta + (I - \rho W)^{-1} (I - \lambda W)^{-1} e

            

      
      
      
    

        
Yes, they often do, but comparing the AIC values may prefer the lag or 
error models because adding extra variables is penalised.

Roger