So my problem revolves around trying to find the right test for my data in R. I've been doing an experiment that is measuring sublethal effects in a toxic environment, and as a result, even though I started with the same numbers for all groups, my sample numbers have shifted due to mortality, leaving me with groups that have different numbers of data points. I've been trying to do nonparametric tests because the data distribution isn't normal, but can't do Friedman's due to the differences in number of data points. I essentially want something like a nonparametric two way ANOVA.
If you want more specifics I have 3 species that I work with: Chione, Venerupis, and Musculista. I exposed all these species to three different copper exposure levels: 0 ppb, 25 ppb, or 50 ppb. For this particular problem, I'm comparing their mucus production. So, independent variables are copper exposure and species, dependent variable is mucus production. Due to the deaths, they all have different numbers of survivors and therefore different numbers of data points. I want to be able to compare them, and would normally use a ANOVA, but they aren't distributed normally.
The mucus data is already transformed because it was a ratio to begin with (mucus/dry mass ratio), and was transformed via arcsine. The mucus collection procedure is fatal, so it's one value per mollusk. The differences in the data sets arise from the number of mollusks that were able to be tested in each group. For example, the 50 ppb groups high the highest mortality rates, so I was only able to collect mucus from a handful in comparison to the 0 ppb groups.