GLMM - R squared

You have the same number of random effects grouping levels (plots, 16)
as total observations (16).  The way that the conditional and marginal
R-squareds differ is by adding an observation-level random effect to
the model; since you already have an observation-level random effect
in your model, adding another observation-level random effect doesn't
make a difference.

While we're at it, I would be *very* careful fitting a model with 4-6
parameters (depending on whether you count the intercept and/or the
random effects variance) to a data set with 16 points; a general rule
of thumb (Harrell, *Regression Modeling Strategies*) is that you need
*at least* 10-20 data points per parameter ...

Possibly of interest:


library(dplyr)
ss <- arm::rescale ## avoid Error: Unsupported type CLOSXP for column "width"
comuni1sc <- comuni1 %>%
  mutate_each(funs(ss),-c(riquplanta,plot,lot))
MM1B <- update(MM1A,data=comuni1sc)
library(ggplot2)
dotwhisker::dwplot(MM1B)+geom_vline(xintercept=0,lty=2)



On Wed, Mar 22, 2017 at 5:44 PM, Marcos Monasterolo

Dear all. I am working with a GLMM in the lme4 package using 4 fixed
factors and 1 random factor (plot). An unexpected result comes up when I
calculate the model's conditional and marginal R-squared using the
r.squaredGLMM funtion in the MuMIn package. Both values are the same. Does
this mean the random term does not add any explanatory power to the model
(and could thus be dropped)? I provide a working code below. Thanks in
advance for your help.
Marcos

id <- "0Bzd8I1jr8z_iRm1aRWhqdHJHcmc" # google file ID
comuni <- read.table(sprintf("https://docs.google.com/uc?id=%s&
export=download", id), head=T, sep="")
comuni1<-comuni[-c(3,6,7,8),] #these data points I don't need
library(lme4)
MM1A <- glmer(riquplanta ~width+lot+exph200+db500+(1|plot), data = comuni1,
family=poisson, control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
summary(MM1A)
library(MuMIn)
r.squaredGLMM(MM1A) #what's going on here?


----
Bi?l. Marcos Monasterolo
Becario doctoral - C?tedra de Bot?nica General, Facultad de Agronom?a, UBA

        [[alternative HTML version deleted]]

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models