Specifically, I tried the following:
DF <- data.frame(x=rep(letters[1:2], 2), y=rep(1:2, 2)+0.01*(1:4)) fit <- lme(y~1, random=~1|x, data=DF) VC <- VarCorr(fit) VC VC[1,2]
When I did this in library(nlme), I got the following:
> VC
x = pdLogChol(1)
Variance StdDev (Intercept) 0.5101563004 0.71425227
Residual 0.0001999596 0.01414071
> VC[1,2]
[1] "0.71425227"
However, when I did it in library(lme4), I got something different:
> VC Groups Name Variance Std.Dev. x (Intercept) 0.50995 0.71411 Residual 2e-04 0.014142 > VC[1,2] Error in VC[1, 2] : incorrect number of dimensions
I was concerned by the differences in the estimates in this case, so I ported this problem to S-Plus 6.2. There I got the "lme4" answers from both "lme" and "varcomp".
hope this helps. spencer graves
Stephen Ellner wrote:
Spencer Graves wrote:
Have you considered "VarCorr"? I've used it with "lme", and the documentation in package lme4 suggests it should work with GLMM, which might also do what you want from glmmPQL.
Thanks for the pointer (I was not aware that nlme and lme4 had different
versions of lme), but I'm still stuck at the same place using lme4:> VarCorr(fit)
Groups Name Variance Std.Dev.
yeart (Intercept) 0.040896 0.20223 Residual 0.091125 0.30187
The number I need to extract and store is the .20223, but all the components I can find in VarCorr(fit) are something else.
u=VarCorr(fit); slotNames(u)
[1] "scale" "reSumry" "useScale"
[EMAIL PROTECTED][1] 0.3018693
[EMAIL PROTECTED]$yeart
An object of class "corrmatrix"
(Intercept)
(Intercept) 1
Slot "stdDev":
(Intercept) 0.6699156
[EMAIL PROTECTED]
[1] TRUE
In glmmML the estimate is returned as the $sigma component
of the model, but I also need the same info from 'family=gaussian' models.
Stephen P. Ellner ([EMAIL PROTECTED]) Department of Ecology and Evolutionary Biology Corson Hall, Cornell University, Ithaca NY 14853-2701 Phone (607) 254-4221 FAX (607) 255-8088
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