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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