[R-sig-ME] unbalanced data in nested lmer model

[Jana Bürger]

Dear Andrew and other list members,
As I described in an earlier 
post(https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q1/003503.html)
my data is actually hierarchical down to the level of fields within farms.

There is more than 500 cases on 8 farms in 6 regions.
Would you not think that gives enough power to distinguish within region 
variability vs. between regions?

Moreover I don't understand your argument that fitting random efects 
with less than 5 levels was dodgy, as often examples in the books have 3 
samples from one beach, or 3 laboratory workers within one laboratory. 
These are less than 5 levels, are they not?

Regards, Jana

Andrew Dolman schrieb:
> Dear Jana,
> 
>  >An anova(lm1, lm2)  lm1<-lmer(y~x1+x2+...+(1|region)+(1|region:farm)); 
> lm2<-lmer(y+x1+x2+...+(1|farm)) said models did not differ significantly 
> and AIC was about the same. So I know there is no additional explanatory 
> power including the region term.
> 
>  >Yet, I would like to keep the region effect in the model to separate 
> and compare the effect size of region vs. farm. Is it valid to do so 
> even if  some of the regions are only represented by one farm?
> 
> I don't think you have the data to ask questions about differences 
> between regions as distinct from differences between farms. Look at it 
> this way. If you were just doing a normal comparison between regions and 
> you only looked at 1 or 2 farms per region, would you have the 
> statistical power to say that differences were due to region rather than 
> farm? Answer = No.
> 
> Similarly, are the differences between the farms because they are in 
> different regions or just normal variation between farms? Well you only 
> have 2 farms per region so it's hard to tell. Maybe you just have enough 
> data if pairs of farms within regions are always very similar and 
> differences between regions large.
> 
> Also. Fitting random effects with fewer than 5 levels is dodgy, and you 
> only have 2 levels of farm per region, sometimes 1.
> 
> Perhaps you could look at it this way.
> 
> compare
> 
> m1 <- lmer (y~(1|region))
> m2 <- lmer (y~(1|farm))
> 
> If m2 is better then there is variation between farms within regions, if 
> there's no difference then region accounts for most of the variation. 
> BUT you've not got much power to detect farm effects within regions, so 
> a null result is not strong evidence for the absence of farm variation 
> within regions.
> 
> 
> Andy.
>  
> 
> 
> andydolman at gmail.com <mailto:andydolman at gmail.com>
> 
> 
> 

-- 
Jana Bürger

Universität Rostock
Agrar-  und Umweltwissenschaftliche Fakultät
FG Phytomedizin
Satower Straße 48
18059 Rostock

Tel. 0381-498 31 71
Fax.0381-498 31 62