Modeling proportion data in glmmTMB without 'quasi-binomial' family I am having some difficulty identifying the appropriate GLM family for my proportional data in a mixed model. My response data is inherently proportional (0,1) but also includes values of 1.
My proposed model:
prop.surv1 <- glmmTMB(worker.prop ~ Source.pop + (1 | Colony_ID) + (1 | Col_Season),
                      weights= Worker.start, family = quasi_binomial(), data=count)

I have had trouble modeling this with different family types since glmer() and glmmTMB() no longer include the quasi-binomial family.
I've tried out using the beta_family() arg but the beta_family() arg uses a 'logit' link which does not match my data since it includes some values of 1.
Any suggestions that might work for fitting proportional data still within glmmTMB?
Thank you.

What about a zero-inflated beta distribution? Dummy example:
x <- rbeta(n = 1000, shape1 = 1, shape2 = 5)
x[1:100] <- 0
hist(x)
mdl <- glmmTMB(x ~ 1, family = beta_family(), ziformula = ~ 1)

You'll have to define worker.prop_m1 <- 1 - worker.prop as the response to switch the 1s to 0s.
I'm not convinced the quasibinomial is suitable, as that's meant for modelling overdispersed binomial data. Proportion and binomial responses are not the same thing.
