I have data of multiple factors. Let's call them: size (2 levels), geometry (2 levels) and time (242 levels but I can limit my focus to 3 levels, which are relevant). I also have a measure (dependent variable) called "distance", which is continuous.
Description: I measured the distance of the cells to starting point in different geometries, different pore sizes over time. There are multiple measurements for each group. It looks like that the distribution of the distances of these groups are different from each other.
I would like to know, if distance can be predicted by size and geometry. My data is not normally distributed, so I would like to apply a non-parametric test. It would be great to include all time points to compare "curves" or time-course but if not possible, it is enough to do the test on 3 relevant time points. I am using R.
I think I cannot use: Friedman test, as it is for non-replicated data. Kruskal Wallis, as it is one-way (last resort?). OLR, as it is for ordinal data, not continuous.