Hi everyone,
I've searched the internet and lots of stats books high and low for this one,
but nothing seems to be quite what I want. I've got continuous data on four
different state activities recorded in seconds, however each continuous session
is not equally long, so the data are best expressed as proportions, i.e.:
Subject 1: Swimming, 0.5, Hiding, 0.25, Edge, 0.125, Inactive, 0.125 and so on
for each subject. I have two fixed effects and a possible random effect that
may only have a very small influence on the end results. The four response
variables are obviously not independent as they are a proportion of the total.
The data are not normal, even when arcsine transformed from proportions where
only the response variable with the highest proportion comes close (that being
swimming).
I have used four separate binomial glms with each response variable (in seconds
rather than as a proportion) against total time, but I'd rather incorporate
them all into one model somehow. I thought about doing a manova, but I'm pretty
sure lots of assumptions aren't met. For one, the response variables aren't
independent.
It confuses me that it's so hard for me to analyse this data, as I assumed it
would be relatively straight forward. Any ideas?
Best regards,
Freya
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
