General linear mixed exposure (flicker) of heteroskedasticity

Question

My variables are measured on a randomized block with a subsample, where my treatment methods are 23 Accesion. I have 3 full blocks and 6 samples per block. The sample data frame has 4 response variables (LH, REN, FTT, DFR), Accesion (processing), Bloque (block number), and a graph (i.e., a variable that takes into account subsampling). Data head:

  Plot Accesion Bloque   LH  REN FTT DFR
1  221       22      1 20.6 1127  23  88
2  221       22      1 20.5 1638  20  88
3  221       22      1 24.5 1319  16  88
4  221       22      1 21.4  960  17  88
5  221       22      1 25.7 1469  18  88
6  221       22      1 25.8 1658  21  88

Thus, the data are abnormal and heterosedical for almost all 100 response variables after all types of transformations (log, boxcox, power, etc.). Most variables show a chi-square or Poisson distribution with a different dispersion for each Accesion.

glmer() FTT . :

FTTglme = glmer(FTT ~ Accesion + Bloque + (1|Plot), data = Lyc,   
family=poisson(link="identity"))

.test(). , , , . Accesion :

, , glme. , , , :

vf <- varIdent(form=~Accesion)

FTTglme = glmer(FTT ~ Accesion + Bloque + (1|Plot), data = Lyc,   
family=poisson(link="identity"), weights = vf)

, Accesion. :

Error in model.frame.default(data = Lyc, weights = varIdent(form = ~Accesion),  : 
  variable lengths differ (found for '(weights)')

- , Accesions glmer()?

.

Answer 1

. glmer() , . , :

VAR<-aggregate(Lyc[,6],by=list(Lyc$Accesion), var)
colnames(VAR)<-c("Accesion", "Var")

/, :

 Accesion       Var
1       22  4.369281
2       23 16.251634
3       24 13.911765
4       25 15.404412
5       26 15.895833
6       27 44.838095

,

Lyc2<-merge(VAR, Lyc, by="Accesion")

, 1 , , .

Lyc2$Var<-(1/Lyc2$Var)

:

FTTglme = glmer(FTT ~ Accesion + Bloque + (1|Plot), data = Lyc2,    
family=poisson(link="log"), weights = Lyc2$Var)

: