I have ran this model in R:
glm(alert ~ water.height + ssp*ssp.zone + log(count) + ssp*days, family=quasibinomial, data=ScanSampling_sub_alert)
Alert= proportion of a goose flock that were alert,
ssp= subspecific identity of flock,
ssp.zone= which part of site they were in,
log(count)= log of flock size, and
days= days from 27.9.13
This is a plot of the the predicted proportion of flock that is alert conditional on
Two things are strike me as slightly strange:
- The top two lines for predicted values are curved, even though the model doesn't include a polynomial term.
- The confidence interval for the top two lines in uneven and the predicted values are not always in the centre of the confidence interval.
Can anyone explain why I'm seeing a curved line for predictions and an uneven confidence interval? Is it something to do with using
family=quasibinomial? Unfortunately I cannot recreate these patterns using any built-in R datasets.