[R-sig-ME] completely nested glmer
David.Duffy at qimrberghofer.edu.au
Thu Aug 31 08:46:36 CEST 2017
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))
> # 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),...)
and look at diagnostics for whether a gamma is really best (cf log-normal).
Just 2c, David Duffy.
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