Thank you for providing such a marvelous example. I wish I could
reward your dilligence with a simple, complete answer. Unfortunately,
the best I can offer at the moment is a guess and a reference. First, I
believe you are correct in that the "weights" argument describes
"the
within-group heteroscedasticity structure". To specify between-group
heterscadisticity, have you considered the following:
foo <- Orthodont
foo$w <- c(rep(1, 5*4), rep(0.5, 22*4))
> lme(distance ~ 1, random = ~ w-1|Subject,
+ method="ML", data = foo)
Linear mixed-effects model fit by maximum likelihood
Data: foo
Log-likelihood: -258.8586
Fixed: distance ~ 1
(Intercept)
23.8834
Random effects:
Formula: ~w - 1 | Subject
w Residual
StdDev: 3.370796 2.233126
Number of Observations: 108
Number of Groups: 27
Second, have you consulted Pinheiro and Bates (2000) Mixed-Effects
Models in S and S-Plus (Springer)? If you have not already, I encourage
you to spend some quality time with that book. For me, this book helped
transform "lme" from an inaequately documented and unusable black box
into a simple, understandable, elegant tool. I may have recommeded it
to more people than any other single work over the past five years.
hope this helps.
spencer graves
Marco Geraci wrote:
> Dear R users,
> I'm trying to fit a simple random intercept model with a fixed
intercept.
> Suppose I want to assign a weight w_i to the i-th contribute to the
log-likelihood, i.e.
>
> w_i * logLik_i
>
> where logLik_i is the log-likelihood for the i-th subject.
> I want to maximize the likelihood for N subjects
>
> Sum_i {w_i * logLik_i}
>
> Here is a simple example to reproduce
>
> # require(nlme)
> > foo <- Orthodont
>
>>lme(distance ~ 1, random = ~ 1|Subject, method="ML", data =
foo)
>
> Linear mixed-effects model fit by maximum likelihood
> Data: foo
> Log-likelihood: -257.7456
> Fixed: distance ~ 1
> (Intercept)
> 24.02315
> Random effects:
> Formula: ~1 | Subject
> (Intercept) Residual
> StdDev: 1.888748 2.220312
> Number of Observations: 108
> Number of Groups: 27
>
> Then I assign arbitrary weights, constant within the group. I want to give
weight 1 to the first 5 subjects, and weight 0.5 to the others 22 (4 is the
number of repeated measurements for each subject)
>
>
> > foo$w <- c(rep(1, 5*4), rep(0.5, 22*4))
>
> Maybe I am missing something, but I believe that
>
> > lme(distance ~ 1, random = ~ 1|Subject, method="ML", data
= foo, weight=~w)
>
> does not maximize the likelihood Sum_i {w_i * logLik_i},
since 'weight' describes the with-in heteroscedasticity
structure.> I think I need something like the option 'iweight'
(importance weight) for the command 'xtreg' of
Stata.>
> Any suggestion for R?
>
> Thanks in advance,
>
> Marco Geraci
>
> > sessionInfo()
> R version 2.2.1, 2005-12-20, i386-pc-mingw32
> attached base packages:
> [1] "methods" "stats" "graphics"
"grDevices" "utils"
> [6] "datasets" "base"
> other attached packages:
> nlme
> "3.1-66"
>
>
>
>
>
>
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