[R-sig-ME] random effect variance per treatment group in lmer

[Simon Blomberg]

I think he is asking to stratify the variance of the innermost
residuals, or at least it's not clear. In lme that can be accomplished
with weights=varFixed(~1|Patient).

To stratify at different levels of nesting, say the data is this:
 dat <- data.frame(inner=rep(1:10, each=5), outer=rep(1:2, each=25),
x=rnorm(50))

Then this call to lme does the job:

 fit <- lme(x ~ 1, random=list(outer=~1, inner=~1), data=dat,
weights=varComb(varIdent(form=~1|outer), varIdent(form=~1|inner)))

edited output:

Combination of variance functions: 
 Structure: Different standard deviations per stratum
 Formula: ~1 | outer 
 Parameter estimates:
        1         2 
1.0000000 0.5170794 
 Structure: Different standard deviations per stratum
 Formula: ~1 | inner 
 Parameter estimates:
        1         2         3         4         5         6         7
8 
1.0000000 0.3127693 0.4475444 0.7323698 0.3647991 0.5962917 1.4127508
1.7664527 
        9        10 
0.9475334 0.3666155 

Cheers,

Simon.

weights=varOn Wed, 2007-07-11 at 15:04 +1000, Andrew Robinson wrote:
> Hi David,
> 
> as far as I am aware, there is no option for stratifying the variance
> of random effects in either lme or lmer.  One can stratify the
> variance of the innermost residuals in lme, but that is different than
> what you are asking for. 
> 
> Cheers,
> 
> Andrew
> 
> 
> On Tue, Jul 10, 2007 at 10:23:21AM -0400, Afshartous, David wrote:
> > 
> > All,
> > I didn't receive a response to the query below sent to the general
> > R-help mailing list so figured I'd try this mailing list.  Apologies
> > in advance if this is an overly simplistic question for this list; I
> > just started 
> > w/ lmer after not using lme for awhile.
> > Cheers,
> > Dave 
> > 
> > 
> > 
> > 
> > ___________________________________________________________
> > 
> > All,
> >  
> > How does one specify a model in lmer such that say the random effect for
> > 
> > the intercept has a different variance per treatment group?  
> > Thus, in the model equation, we'd have say b_ij represent the random
> > effect
> > for patient j in treatment group i, with variance depending on i, i.e,
> > var(b_ij) = tau_i.
> >  
> > Didn't see this in the docs or Pinherio & Bates (section 5.2 is specific
> > for 
> > modelling within group errors).  Sample repeated measures code below is
> > for 
> > a single random effect variance, where the random effect corresponds to
> > patient.
> > cheers,
> > dave
> >  
> >  
> > z <- rnorm(24, mean=0, sd=1)
> > time <- factor(paste("Time-", rep(1:6, 4), sep="")) 
> > Patient <- rep(1:4, each = 6) 
> > drug <- factor(rep(c("D", "P"), each = 6, times = 2)) ## P = placebo, D
> > = Drug
> > dat.new <- data.frame(time, drug, z, Patient) 
> > fm =  lmer(z ~ drug + time + (1 | Patient), data = dat.new )
> > 
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 
-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat. 
Lecturer and Consultant Statistician 
Faculty of Biological and Chemical Sciences 
The University of Queensland 
St. Lucia Queensland 4072 
Australia
Room 320 Goddard Building (8)
T: +61 7 3365 2506 
email: S.Blomberg1_at_uq.edu.au

Policies:
1.  I will NOT analyse your data for you.
2.  Your deadline is your problem.

The combination of some data and an aching desire for 
an answer does not ensure that a reasonable answer can 
be extracted from a given body of data. - John Tukey.