How to interpret model fit

[mmanus]

Hi all, quick question about interpretation.

I successfully used betta_random on a model that includes a main predictor variable as well as a few covariates (estimate = estimated diversity of infant fecal samples using DivNet). I'm wondering how to interpret the outputs for metrics of model fit, like homogeneity, heterogeneity, and model explanatory power. My model looks like this:

model1 <- betta_random(formula = estimate ~ predictor + covar1 + covar2 + covar3 + covar4 | individual, ses = error, data = data1)

How exactly should the corresponding model fit outputs be interpreted?

model1$homogeneity
[1] 249383.7 0.0

model1$global
[1] 421507.8 0.0

model1$ssq_u
0

Thank you!