[R] using glmer to fit a mixed-effects model with gamma-distributed response variable

[Ben Bolker]

Benjamin Caldwell <btcaldwell <at> berkeley.edu> writes:

> 
> Sub: using glmer to fit a mixed-effects model with gamma-distributed
> response variable
> 
> Hello,
> I'm currently trying to fit a mixed effects model , i.e.:
> 
> > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+
> bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian,
> na.action=na.omit, data=rws30.BL)

  [stuff about turning site, transect, plot into factors snipped]

> > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+
> bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=Gamma,
> na.action=na.omit, data=rws30.BL)
> 

  Alas, Gamma GLMMs are not yet feasible in glmer -- this is in the
works but I wouldn't hold my breath (try searching the r-sig-mixed-model
archives for this topic). At this point your options are somewhat
limited, to 'build your own model' tools such as WinBUGS or AD Model
Builder (it is conceivable that Gamma GLMMs could be added in the
development version of glmmADMB, but again I wouldn't hold my breath).

  If I may ask, is there a reason you need Gamma GLMMs and not
log-normal GLMMs?  At least qualitatively, the properties of Gamma
and lognormal distributions are reasonably similar (2-parameter families,
domain = non-negative reals, distribution ranges from 'L-shaped' to
approximately normal ...)  You can fit a log-normal GLMM by simply
log-transforming your data (dealing with zeros appropriately) and
fitted a regular, linear mixed model ...

  Further discussions on this topic would probably be better on 
the r-sig-mixed-models list.

  Ben Bolker