[R-sig-ME] completely nested glmer

[David Duffy]

Troels Ring asked:

> I have 2013 measurements of capillary flow speed obtained from 204
> glomeruli originating from 29 rats, 10 of whom are controls, 11 made
> diabetic, and 8 made hyperglycemic otherwise.  Altogether I have 621
> capillaries from controls, 964 from diabetics and 428 from hyperglycemic.
> If I make a direct aov
> summary(z2 <- aov(Speed~TRT,SCAN))
> TukeyHSD(z2)
> #$TRT
> #                 diff         lwr         upr     p adj
> #Diab-Ctrl  -0.4266708 -0.65092625 -0.20241526 0.0000255
> #Hyper-Ctrl -0.2206938 -0.49449343  0.05310581 0.1416500
> #Hyper-Diab  0.2059769 -0.04716959  0.45912348 0.1365243

> summary(z3a <- glmer(Speed~TRT + (1|RAT)+(1|ind) + (1|RAT:ind)
> ,data=SCAN,family=Gamma)) where ind is an indicator for each of the 204
> glomeruli. And the fixed effect TRT is not significant.

I would look at the permutation test for the Jonckheere-Terpstra  test statistic for Speed ~ TRT, where TRT is a quantitative
variable 0=Ctrl 1=Hypergly 2=Frank diabetes, given I would have a strong prior hypothesis that an
association will take this form. You should look to see if RAT:ind is required eg
z3b <-  glmer(Speed~TRT + (1|RAT)+(1|ind),...)
anova(z3a, z3b)
and look at diagnostics for whether a gamma is really best (cf log-normal).

Just 2c, David Duffy.