performance::r2_nakagawa() and r.squaredGLMM() give different values for Gaussian glmmTMB models without random effects about performance HOT 4 CLOSED

library(performance)
library(glmmTMB)
library(MuMIn)
data(Owls)

m_NORAND <- glmmTMB(NegPerChick ~ BroodSize + ArrivalTime, data = Owls)

r2(m_NORAND)
#> Random effect variances not available. Returned R2 does not account for random effects.
#> # R2 for Mixed Models
#> 
#>   Conditional R2: NA
#>      Marginal R2: 0.127
r.squaredGLMM(m_NORAND)
#> Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.
#> Warning in r.squaredGLMM.glmmTMB(m_NORAND): the effects of zero-inflation and
#> dispersion model are ignored
#>             R2m        R2c
#> [1,] 0.05606123 0.05606123

m_easy <- lm(NegPerChick ~ BroodSize + ArrivalTime, data = Owls)
r.squaredGLMM(m_easy)
#>             R2m        R2c
#> [1,] 0.05579612 0.05579612
r2(m_easy)
#> # R2 for Linear Regression
#>        R2: 0.056
#>   adj. R2: 0.053

r <- residuals(m_NORAND)
f <- fitted(m_NORAND)
rss <- sum(r^2)
mss <- sum((f - mean(f))^2)
mss / (mss + rss)
#> [1] 0.05597288

library(performance)
library(glmmTMB)
library(MuMIn)
data(Owls)

m_NORAND <- glmmTMB(NegPerChick ~ BroodSize + ArrivalTime, data = Owls)
r2(m_NORAND)
#> # R2 for Linear Regression
#>   R2: 0.056
r.squaredGLMM(m_NORAND)
#> Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.
#> Warning in r.squaredGLMM.glmmTMB(m_NORAND): the effects of zero-inflation and
#> dispersion model are ignored
#>             R2m        R2c
#> [1,] 0.05606123 0.05606123