[R-sig-ME] Calculating proportion variance explained by random effects in zi-component

[Ziaja, Dominik]

Dear GLMM-modelers,

I would like to report the proportion of variance each random effect
explains in addition to the fixed effects. For this, I use the
"get_variance" function from the insight package for the conditional
components. For the data I want to model I needed to implement a
zero-inflation model (not a hurdle model) However, I can't really find a
downstream wrapper/implementation to use the get_variance function onto
the zero-inflation component (as is the case for e.g. emmeans, Anova).

Using the VarCorr function/the summary output I'm able to get the
variances of the individual random effects. However, I don't exactly
know how to calculate the other sums of variances reported with the
insight package (var.fixed, var.residual, var.distribution,
var.dispersion).

I was thinking of 3 different ways to achieve this goal and was
wondering whether someone might have an idea/a hint/a direction.

1. Is there actually an implemented possibility to apply get_variance()
to the zi-component which I simply overlooked?

2. Is there a way to get the information which measurements were used
for the zero-inflation-component of the zero-inflation model so I could
then calculate a binomial model on exactly these measurements. My hope
would be to then apply get_variance() onto this model.

3. How would I go about to calculate the missing variances manually by
myself (var.fixed, var.residual, var.distribution, var.dispersion)?

I also made a stack-overflow post about this some time ago, I hope it is
worded correctly and clearly enough.
Link:
https://stackoverflow.com/questions/74689961/calculate-proportion-of-random-effect-variance-from-zero-inflation-component-of

I'm happy for any answers, hints or directions.

sincerely,
Dominik