[R-sig-Geo] error message when running errorsarlm

[Roger Bivand]

On Wed, 21 May 2008, evans324 at umn.edu wrote:

> On May 21 2008, Roger Bivand wrote:
>
>> On Wed, 21 May 2008, evans324 at umn.edu wrote:
>> 
>> >  Thanks. I upgraded and updated everything and got a better result.
>> 
>> Does that mean that you get a sensible lambda for your model now - the line 
>> search leads somewhere other than a boundary of the interval?
>
> I apologize for being unclear. I actually upgraded R and updated packages, 
> then ran errorsarlm with method="Matrix" and got the same error messages I'd 
> had previously (i.e., the search led to the boundary of the interval). I then 
> tried your other suggestion and used method="spam" and got a result with no 
> error messages.

But we do not know why the two are not the same (they should be), so I 
would still not trust the outcome. I would be interested in off-list 
access to the data being used - I think that there is some issue with the 
scaling of the variable values. Do you see the same difference using 
spautolm(), which is effectively the same as errorsarlm(), but with a 
different internal structure?

> 
>> >  However, I'm not 100% sure that I'm using the correct command to 
>> >  accomplish what I need to accomplish. My OLS model has significant 
>> >  spatial autocorrelation (RLMlag is not significant and RLMerror is) and 
>> >  heteroscedasticity. I had hoped to use errorsarlm then run White's 
>> >  standard errors to address this, but I find that hccm(car) requires a 
>> >  .lm object. Looking through old threads, I found one that suggests using 
>> >  spautolm is such situations. Does spautolm address both spatial 
>> >  autocorrelation and heteroscedasticity?
>> 
>> There are different traditions. Econometricians and some others in social 
>> science try to trick the standard errors by "magic", while epidemiologists 
>> (and crime people) typically use case weights - that is model the 
>> heteroscedasticity directly. spautolm() can include such case weights. I 
>> don't think that there is any substantive and reliable theory for adjusting 
>> the SE, that is theory that doesn't appeal to assumptions we already know 
>> don't hold. Sampling from the posterior gives a handle on this, but is not 
>> simple, and doesn't really suit 10K observations.
>> 
> Can you explain "magic" a little further? I'm running this for a professor 
> who is a bit nervous about black box techniques and I'd like to be able to 
> offer him a good explanation. I think he'll just have me calculate White's 
> standard errors and ignore spatial autocorrelation if I can't be clearer.
>

If this is all your "professor" can manage, please replace/educate! The 
model is fundamentally misspecified, and neither "magicing" the standard 
errors, nor just fitting a simultaneous autoregressive error model will 
let you make fair decisions on the "significance" or otherwise of the 
right-hand side variables, which I suppose is the object of the exercise?

(Looking at Johnston & DiNardo (1997), pp. 164-166, it looks as if White's 
SE only help asymptotically (in Prof. Ripley's well-known remark, 
asymptotics are a foreign country with spatial data), and not in finite 
samples, and their performance is unknown if the residuals are 
autocorrelated, which is the case here).

The vast number of observations is no help either, because they certainly 
introduce heterogeneity that has not been controlled for. Is this a grid 
of global species occurrence data, by any chance? Which RHS variables are 
covering for differences in environmental drivers? Or is there a better 
reason for using many observations (instead of careful data collection) 
than just their being available?

More observations do not mean more information if meaningful differences 
across the observations are not captured by included variables (with the 
correct functional form). Have you tried GAM with flexible functional 
forms on the RHS variables and s(x,y) on the (point) locations of the 
observations?

You are not alone in your plight, but if the inferences matter, then it's 
better to be cautious, irrespective of the "professor".

Roger

> Thanks again.
>
> Heather
>
>

-- 
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no