Getting R^2 from Dredge() when global model has an optimizer - r

Question

Goal: get R^2 marginal and conditional in dredge results using an optimizer in the origninal model
This branches off of this question: dredge doesnt work when specifying glmer optimizer and the two solutions provided.
Solution 1: change r.squaredLR.R package code
Solution 2: add a function into the dredge function to call r.squaredGLMM instead of r.squaredLR
I tried Solution 2 first, which works perfectly on the simulated data, but when I try it on my model I get the error :
Error in r.squaredGLMM(x, null = nullmodel)["delta", ] :
subscript out of bounds
I then tried Solution 1 by altering the source code of r.squaredLR.R as descripbed and saving it as a R script and using source() to call the edited 'null.fit' function as to avoid editing r.squaredLR.R permenantly (I call MuMIn before sourcing the edited function). Yet this doesn't work.
Back to Solution 2...
I tried to simulate data similar to mine and was able to get the same error (the lmercontrol argument is disregarded in this global model, but I get the desired error so I didn't try to correct the data to need lmercontrol).
#Solution 2 attempt
set.seed(101)
dd <- data.frame(x1= rnorm(1920), x2=rnorm(1920), x3=rnorm(1920), x4=rnorm(1920),
treatment = factor(rep(1:2, each=3)),
replicate = factor(rep(1:3, each=1)),
stage = factor(rep(1:5, each=384)),
country = factor(rep(1:4, each=96)),
plot = factor(rep(1:10, each=24)),
chamber = factor(rep(1:6, each=1)),
n = 1920)
library(lme4)
dd$y <- simulate(~ x1 + x2 + x3 + (1|plot),
family = binomial,
weights = dd$n,
newdata = dd,
newparams = list(beta = c(1,1,1,1),
theta = 1))[[1]]
# my real response variable 'y' has a poisson distribution, but I had difficulty figuring
# out how to simulate a poisson distribution so I left the bionomial.
m0 <- lmer(y~ x1 + x2 + x3 + x4 + treatment*replicate*stage + (1|chamber) + (1|country/plot),
data=dd,
na.action = "na.fail",
REML = F,
lmercontrol = glmerControl(optimizer="bobyqa"))
nullmodel <- MuMIn:::.nullFitRE(m0)
dredge(m0, m.lim = c(0,5), rank = "AIC", extra =list(R2 = function(x) {
r.squaredGLMM(x, null = nullmodel)["delta", ]}))
A suggested reason for the error "subscript out of bounds" was that "the data being put into the algorithm are not in the format that the function expects."
Indeed, the function works when I remove ["delta", ] and I get the columns R21 and R22, but without taking into account the delta column these values are probably incorrect and I'm not sure which one is marginal and conditional R^2.
If you have any ideas, I'm all ears! Thanks in advance for all help.