I'm running a GLMM through the lme4 package in R to detect differences in time spent feeding (response) before and after birth (my 2 categories in the variable inf_cat). I started with a Poisson GLMM, with female ID as a random effect (because I have repeated measures) and an offset of the total amount of time observing the female in a block (to avoid using proportion values):
ba.feed <- glmer(feeding ~ inf_cat + offset(total_inf_cat) +
(1|female), family=poisson, data=mothers_beforeafter)
The summary showed that my data had overdispersion so I switched to a negative binomial GLMM, but it gave me this error message:
boundary (singular) fit: see ?isSingular
The summary shows that the variance of my random effect is 1.255e-11.
From my understanding, this singular fit comes from either a model that is too complex or not enough random effects levels. I've been troubleshooting with this site but I'm still confused about what I can do moving forward. I don't think I'm able to simply drop my fixed effect variable and rerun the model because I have repeated measures in my dataset.