Here is an example: I have a set of observations of different individuals from lots of different families of grasses:
individual#, Fam, Genus, Factor1(3 levels), Factor2(7 levels), Factor3(5 levels), Response1(3 levels), Response2(3 levels)
What I am hoping to discover is whether the frequency of occurrences of Response1 and 2 are linked to family groups, and whether Factors 1 - 3 (things like soil type, sun exposure etc) have an impact.
family, resp1a, resp1b, resp1c 1, 14%(20), 16%(24), 67%(98), Total N = 147 2, 38%(98), 86%(220), 48%(123), Total N = 256 ...
First, I need to see whether these differences in responses between families is significant (chi-squared?). Secondly, I need to see if one of the 3 factors has an effect on the response.
Now it seems in my basic understanding, that if the response(s) were continuous measurement, ANOVA/MANOVA would work. Easy-peasy. However, since everything is discreet categories (including the independent and dependent variables) I can't do this. Additionally, since the responses are not mutually exclusive, this seems to violate an assumption of the log-linear model.
I've scoured, and keep bouncing around between Multinomial Logistic Regression, or just independent Chi-Square tests, or... hell I don't know anymore.
And yes, I am trying to swim before learning to float.
Oh, and this is all happening in R.