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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