I'm a little confused about how to use the quasipoisson family in the glm function. It was recommended by someone that I use it for my analysis, even though the data are continuous - and as such, I don't get the same warning messages (regarding the fact that my data are non-discrete) as I do when I use it for the Poisson family. Why is this? Is the quasipoisson family actually compatible with continuous data? I thought Gamma might be more suitable for a continuous dataset, but about 50% of my values are zeroes (this was the original reason quasipoisson was recommended).
I don't quite understand all of the information I've been given about lm's/glm's in general - do they assume anything about the distribution of y (the response variable), or just the distribution of the residuals, when a model has already come up with fitted values?
As I said earlier, I was given the typical advice of 'if the assumptions for the Poisson family aren't met, use quasipoisson.' ...but how can I test if the assumptions of the quasipoisson distribution are met (or Gamma, if I just add one to everything)?
I can't use a Kolmogorov-Smirnov test, as the function does not like repeated values (as I said, about half of the y-values are zero).
Would you suggest I just give up and use nonparametric stats? My project is already overdue and I have lots of other work to do!
I'm ok with sharing my data if that helps