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, Thanks, Radu On Wed, Apr 13, 2011 at 4:44 AM, Roger Bivand <[email protected]> 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: [email protected] > > [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
