spatial correlation structure in lme

Thanks for your advice. Let me re-phrase my question with a reproducible
example. Same message was also posted to the mixed-model list. Sorry for
the cross-posting.

The example and data are from UCLA's R FAQ webpage. The link for the
webpage is:  http://www.ats.ucla.edu/stat/r/faq/spatial_regression.htm

First, following the instructions on the webpage, I did the following???

install.packages("nlme")
library(nlme)

spdata <- read.table("http://www.ats.ucla.edu/stat/R/faq/thick.csv", header
= T, sep = ",")
dummy <- rep(1, 75)
spdata <- cbind(spdata, dummy)

### estimated the null model ###

soil.model <- lme(fixed = thick ~ soil, data = spdata, random = ~ 1 |
dummy, method = "ML")
summary(soil.model)
plot(Variogram(soil.model,form=~north+east))

### then updated the model by the spatial correlation structure ####

soil.gaus <- update(soil.model, correlation=corGaus(1,form=~north+east))
summary(soil.gaus)
plot(Variogram(soil.gaus,form=~north+east))

*My first question is*: the variogram of soil.gaus indicates a range of 40,
but the estimated range is 20.43725. Does this mean the model "soil.gaus"
failed?

Next, I define the range and nugget manually as below. The variogram of
soil.model indicates a range at 40 and nugget at about 0.1.

soil.gaus.1 <- update(soil.model,
correlation=corGaus(c(40,0.1),form=~north+east,nugget=T))
summary(soil.gaus.1)
plot(Variogram(soil.gaus.1,form=~north+east))

The estimated range looks right (very close to 40). But the estimated
nugget and variogram plot still look weird. *So my question is*: what I can
do next step to improve the model?

Any advice would be greatly appreciated!

Best
Gary

On Sat, Jun 16, 2012 at 2:23 AM, Edzer Pebesma <
edzer.pebesma at uni-muenster.de> wrote:

It would have helped us when you would have provided an example that was
reproducible for readers of this list.

I tried some things out, along that way, but didn't manage. Maybe you
could try the mixed-models list:

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