How to test multicollinearity in Fixed Effects Model in R? I was using plm package in R and run some pooling and fixed effects model. For pooling models I was able to use vif() for getting Variance Inflation Factor, but when I run it for fixed effect model, it showed me the below error:
> > vif(modelFE.1.i) 
> > Error in R[subs, subs] : subscript out of bounds In
> addition: Warning message: In vif.default(modelFE.1.i) : No intercept:
> vifs may not be sensible.

So, I was wondering if there is some way to find multicollinearity under Fixed Effects settings?
The error says that VIF cannot be computed without intercept, I understand that. But, what can be the other tests that I can do for testing multicollinearity?
 A: Reprex (courtesy of https://easystats.github.io/blog/posts/performance_check_collinearity/):
library(glmmTMB); library(performance)
#note: needed to also install the insight package before installation of performance

data(Salamanders)

# create highly correlated pseudo-variable
set.seed(1)
Salamanders$cover2 <-
    Salamanders$cover * runif(n = nrow(Salamanders), min = .7, max = 1.3)

# fit mixed model with zero-inflation
model <- glmmTMB(
    count ~ spp + mined + cover + cover2 + (1 | site), 
    ziformula = ~ spp + mined, 
    family = truncated_poisson, 
    data = Salamanders
)

# now check for multicollinearity
check_collinearity(model)

The performance package offers check_collinearity function that handles ME models:
 library(performance)
 

 check_collinearity(model)
# Check for Multicollinearity
#--------------------------------------------------------
* conditional component:

Low Correlation

 Parameter  VIF Increased SE
       spp 1.07         1.04
     mined 1.17         1.08

High Correlation

 Parameter   VIF Increased SE
     cover 19.30         4.39
    cover2 19.12         4.37

* zero inflated component:

Low Correlation

 Parameter  VIF Increased SE
       spp 1.08         1.04
     mined 1.08         1.04

