lme function

RSiteSearch("lme spatial correlation", "functions") produced 10 
hits for me just now.  The sixth title on that list was "spatial 
correlation structure" 
(http://finzi.psych.upenn.edu/R/library/nlme/html/corSpher.html).  This 
is the help page for the "corSpher" function.  The Examples section 
there includes references to selected pages in Pinheiro and Bates (2000) 
Mixed-Effects Models in S and S-Plus (Springer), which  for me is 
essential documentation for 'lme' and is the best book I know on 
mixed-effects models generally.  The value of that book is greatly 
enhanced by the availability of script files "ch01.R", "ch02.R", ..., 
"ch06.R", "ch08.R" (in the "~R\library\nlme\scripts" subdirectory of 
your R installation directory).  These contain R code to reproduce all 
the data analyses in the book.  There are a very few cases where the 
syntax is different between R and that documented in the book [e.g., x^2 
must be I(x^2)].  Before I found the script files, I couldn't understand 
why I got substantially different results from the book when just typing 
the commands into R. 

       Hope this helps. 
      Spencer Graves

Hello.

As advised by Mick Crawley in his book on S+, I'm trying to use the lme
function to examine a linear relationship between two variables measured at
60 locations in 12 sites, while taking account of any spatial
autocorrelation (i.e. similarity in variation between the two variables that
is due to site). I am using the function as follows:

model<-lme(yvariable~xvariable,random=~xvariable|site)

If you know your way around this function, I would be very grateful if you
could confirm that this approach is a valid one, or point out why it isn't.
I'd also be very keen to hear any suggestions regarding alternative ways to
address the spatial autocorrelation in my data (I'm hoping to arrive at a
slightly more elegant solution than simply taking site averages for each of
the two variables and running a correlation using these mean values).

Thanks,

Mark