I'm unable to parse your question in the time I have right now, but have you worked the examples with ?lme in library(nlme), extending them with "intervals" and "VarCorr"? If you do that, I suspect you should find the answer to your question. Also, have you consulted Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer)? This book made the difference between failure and success in my attempts to understand and use lme.

    fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
    fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
    summary(fm1)
    summary(fm2)
intervals(fm1)
intervals(fm2)
anova(fm1, fm2)
VarCorr(fm1)
VarCorr(fm2)

hope this helps. spencer graves

[EMAIL PROTECTED] wrote:

Dear R users!

I used lme to fit a mixed model with random intercept and spatial Gaussian
correlation i.e. I fitted a model of the following form:

Y = X*beta + error

and

error = U + W(t) + Z

where U is the random intercept (normally distributed), W(t) the stationary
Gaussian process and Z also a normally distributed (the residual) rv. Each of
these three random variables have a variance which I am not sure to which output
in lme they belong to.
VarCorr gives the intercept and residual variance which I assume belong to U and
Z respectively. The output of lme gives another estimate called "range" which I
assume belongs to the parameter estimate needed for the Gaussian correlation.

Are my assumptions correct? And where can I get the variance for the W(t) from?

Thanks for any answers...and happy new year...

Hadassa

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-- Spencer Graves, PhD, Senior Development Engineer O: (408)938-4420; mobile: (408)655-4567

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