    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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