[R-sig-ME] Model specification/family for a continuous/proportional response with many zeros

[Michael Lawson]

Hello,

I am new to GLMMs and have a dataset where I have two distinct groups (A
and B) of 7 individuals each. The data consists of repeated measurements of
each individual where the amount of time spent at either zone_A or zone_B
is recorded (out of a total time of 300s/observation period). For most of
the time period the individuals are in neither zone.

I want to test if group A and group B spend more time in zone A compared to
zone B (and vice versa).

Speaking to someone else, they said I should use a Binomial GLMM using
cbind. i.e.
cbind(time_at_zone_A, time_at_zone_B) ~ group + (1| id).

However, the response variable is continuous (albeit with an upper bound of
300 seconds per observation period), so I'm not sure if this is appropriate?

Should I convert the response into a proportion and use something like a
Beta GLMM or else use a continuous (Gamma) GLMM? e.g. something like:
prop_time ~ zone*group + (1|id)

The data is quite heavily right-skewed and contains a lot of 0's, so
reading around it also looks like I may need to convert these into a
zero-inflated/hurdle model?

Thank you for any suggestions,
Mike

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