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,

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.