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 !?!?

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

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