[R-sig-Geo] How to compute the response variable from the GMerrorsar output?
Roger Bivand
Roger.Bivand at nhh.no
Wed Apr 13 23:21:05 CEST 2011
On Wed, 13 Apr 2011, Mihail Rosu wrote:
> Thanks Roger for your quick answer.
>
> I'll follow your advise.
>
> Now, assuming that lambda is << 1, could I use
> fitted.values from GMErrorsar output to compute the (average)response
> variable? I think of:
>
> mean(fitted.value) = mean(response -noise) ~= mean(response)
>
> provided that the mean(noise) ~= zero if the mean is computed over more than
> 30 points.
>
> Please be as kind as to advise on that,
For an error model, provided that it is correctly specified, and the
regression coefficients take values that are very similar to the OLS
coefficients (the standard errors may differ), you may do as you would
with OLS. If, however, the model is not well specified, the error SAR
coefficients differ from the OLS coefficients (see the optional Hausman
test in the summary method), indicating that there are missing variables
(or wrong functional forms) correlated with the spatially autocorrelated
error. In that case, you'd need to correct the specification.
Hope this helps,
Roger
>
> Thanks,
>
> Radu
>
> On Wed, Apr 13, 2011 at 4:44 AM, Roger Bivand <Roger.Bivand at nhh.no> wrote:
>
>> On Tue, 12 Apr 2011, Mihail Rosu wrote:
>>
>> Dear list,
>>>
>>> I'm using a 3rd party code to (spatially) analyse the dependence of crops
>>> yields (YLD) on soil types (MUSYM). Consider the model
>>>
>>> model<- YLD ~ MUSYM -1
>>>
>>> The lm() function ouputs as coefficients the average YLD for the various
>>> soils (see below). I'm confused about the interpretation of coefficients
>>> outputed by GMerrorsar(). They are kind of twice smaller than the average
>>> YLD !?!?
>>>
>>
>> Use GM methods with spatial data with great care! Note that the spatial
>> coefficient estimate is outside its range (for your row standardised sptial
>> weights, it should be strictly less than 1). You can try to tune the
>> optimizer used, but in general maximum likelihood is to be prefered. If you
>> use spautolm() or errorsarlm() with method="Matrix", you should get the
>> exact results you need, or try method="MC" or method="Chebyshev" for
>> approximations.
>>
>> Hope this helps,
>>
>> Roger
>>
>>
>>
>>> Please help on "how to compute the predicted YLD from the GMerrorsar()
>>> output". Should I use the "fitted.values" instead of the coefficients?
>>>
>>> much thanks,
>>>
>>> Radu
>>>
>>> diagnostics<-lm(model, data)
>>>> summary(diagnostics)
>>>>
>>>
>>> Call:
>>> lm(formula = model, data = data)
>>>
>>> Residuals:
>>> Min 1Q Median 3Q Max
>>> -44.006 -2.489 2.948 7.258 32.591
>>>
>>> Coefficients:
>>> Estimate Std. Error t value Pr(>|t|)
>>> MUSYMBa 42.1410 0.2279 184.90 <2e-16 ***
>>> MUSYMBe 39.1673 0.3420 114.52 <2e-16 ***
>>> MUSYMBf 19.5921 0.5783 33.88 <2e-16 ***
>>> MUSYMCa 33.1261 0.2935 112.88 <2e-16 ***
>>> MUSYMCh 43.6497 0.1580 276.21 <2e-16 ***
>>> MUSYMCn 41.7622 0.1309 318.98 <2e-16 ***
>>> MUSYMDa 37.1995 0.5189 71.69 <2e-16 ***
>>> MUSYMSb 38.3553 0.2168 176.93 <2e-16 ***
>>> MUSYMTa 44.0064 0.3164 139.10 <2e-16 ***
>>> ---
>>> Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
>>>
>>> Residual standard error: 12.32 on 26679 degrees of freedom
>>> Multiple R-squared: 0.9171, Adjusted R-squared: 0.917
>>> F-statistic: 3.278e+04 on 9 and 26679 DF, p-value: < 2.2e-16
>>>
>>>
>>> dW <- dnearneigh(coords, 0, dist)
>>> dlist <- nbdists(dW, coords)
>>> idlist <- lapply(dlist, function(x) 1/x)
>>> W <- nb2listw(dW, glist=idlist, style="W")
>>>
>>> #Performs spatial error process model with empirically determined spatial
>>> weights matrix
>>>
>>> SEM<-GMerrorsar(model,data=data, W, na.action=na.exclude,
>>> zero.policy=TRUE)
>>>
>>> summary(SEM)
>>>>
>>>
>>> Call:GMerrorsar(formula = model, data = data, listw = W, na.action =
>>> na.exclude, zero.policy = TRUE)
>>>
>>> Residuals:
>>> Min 1Q Median 3Q Max
>>> -46.788453 -2.508823 0.024350 2.486553 37.375018
>>>
>>> Type: GM SAR estimator
>>> Coefficients: (GM standard errors)
>>> Estimate Std. Error z value Pr(>|z|)
>>> MUSYMBa 17.7399 2.3552 7.5322 4.996e-14
>>> MUSYMBe 21.8829 2.3987 9.1229 < 2.2e-16
>>> MUSYMBf 16.4898 2.4502 6.7299 1.698e-11
>>> MUSYMCa 21.3378 2.4094 8.8561 < 2.2e-16
>>> MUSYMCh 18.8470 2.3216 8.1182 4.441e-16
>>> MUSYMCn 18.8399 2.3164 8.1332 4.441e-16
>>> MUSYMDa 19.5054 2.4220 8.0533 8.882e-16
>>> MUSYMSb 19.0423 2.3655 8.0501 8.882e-16
>>> MUSYMTa 19.2016 2.3662 8.1150 4.441e-16
>>>
>>> Lambda: 1.0157
>>> Number of observations: 26688
>>> Number of parameters estimated: 11
>>>
>>> [[alternative HTML version deleted]]
>>>
>>>
>>>
>> --
>> 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
>>
>>
>
--
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
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