On 16 Sep 2005, at 17:21, Sundar Dorai-Raj wrote:
> My guess is he wants this:
>
> c1 <- factor(c)
> d1 <- factor(d)
> m <- lmer(a ~ b + (1|c1:d1)+(1|c1))
>
> which assumes d1 is nested within c1.
>
> Take a look at Section 3 in the "MlmSoftRev" vignette:
>
> library(mlmRev)
> vignette("MlmSoftRev")
Ah, that vignette is extremely useful: it deserves to be more widely
known.
I mainly intended this reply to be a thank you to yourself and Harold.
Unfortunately, there is (as always), one last thing that is still
puzzling me:
the df for fixed factors seems to vary between what I currently
understand to
be equivalent calls to lme and lmer, e.g:
-------
a<-rnorm(36);
b<-factor(rep(1:3,each=12))
c<-factor(rep(1:2,each=6,3))
d<-factor(rep(1:3,each=2,6))
c <- evalq(b:c)[,drop=T] #make unique factor levels
d <- evalq(c:d)[,drop=T] #make unique factor levels
summary(lme(a ~ b, random=~1|c/d))
# output includes estimates for fixed effects such as
# Value Std.Error DF t-value p-value
# (Intercept) 0.06908901 0.3318330 18 0.2082041 0.8374
# b2 0.13279084 0.4692828 3 0.2829655 0.7956
# b3 -0.26146698 0.4692828 3 -0.5571630 0.6163
# I understand the above lme model to be equivalent to
summary(lmer(a ~ b + (1|c) +(1|c:d))
#but this gives fixed effects estimates with differing DF:
# Estimate Std. Error DF t value Pr(>|t|)
# (Intercept) 0.069089 0.331724 33 0.2083 0.8363
# b2 0.132791 0.469128 33 0.2831 0.7789
# b3 -0.261467 0.469128 33 -0.5573 0.5811
Again, many thanks for your help: even more so if you or anyone
else can answer this last little niggle of mine.
Yan
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