I am trying to use R function performance::r2() to calculate marginal and conditional R2 of a fitted model of class glmmTMB. To ask this question, I am making use of existing code provided in the document ‘Getting started with the glmmTMB package’ (Bolker 2024), available at link, and the Owls dataset included in package glmmTMB. All code below is taken from that document verbatim, except the use of function performance::r2() in the last line. My question refers to the warning message (shown below) thrown by function performance::r2() when passed the fitted model of class glmmTMB.

library(glmmTMB)
library(performance)

Owls <- transform(Owls,
                  Nest=reorder(Nest,NegPerChick),
                  NCalls=SiblingNegotiation,
                  FT=FoodTreatment)

fit_zipoisson <- glmmTMB(NCalls~(FT+ArrivalTime)*SexParent+
                           offset(log(BroodSize))+(1|Nest),
                         data=Owls,
                         ziformula=~1,
                         family=poisson)

performance::r2(fit_zipoisson) 

Running lines above yields the following output (copied directly from console):

> performance::r2(fit_zipoisson)
# R2 for Mixed Models

  Conditional R2: 0.086
     Marginal R2: 0.019
Warning message:
mu of 2.1 is too close to zero, estimate of random effect variances may be unreliable.

I am trying to determine if this warning message is a simple incompatibility between
packages glmmTMB and performance which does not indicate any real issue with model fit, or if it indicates a real or potential issue with model fit. I have seen this warning consistently when fitting a simpler zero-inflated mixed effects model to another dataset. I am using the owls dataset from package glmmTMB for troubleshooting only.