I am thinking about a factorial experiment with two factors. Both factors are ordered factors. Factor 1 has two levels: small and large. Factor 2 has four levels: never, sometimes, frequently, and often. I also want to conduct the experiment in a number of locations, so I will include location as a sort of "block." I expect larger responses for increasing levels of both factors, and I expect an interaction effect, too. Thus, I have a model as follows:
Response ~ Block + Factor1*Factor2 + error, which will have at least 40 observations, maybe 80, maybe 120, or so on until I can detect an effect.
I'll be measuring a number of response variables, most of which will be counts or 0 truncated (latency of response). I'm wondering how to simulate responses from my model with the expectation of a moderate effect size. I want to know what sample size is appropriate to detect a moderate effect from my treatments, but I'm not familiar enough with simulation to know where to start with such a problem. Any advice or direction or requests for more information would be much appreciated.
Additional information: I'm using R to do everything.
EDIT: I implemented Mark T Patterson's answer to my question modifying it to fit my particular experimental setup and attempt to simulate poisson data, but I get warnings when I run the function. Fortunately, there are some relevant answers on CrossValidate: Generate data samples from Poisson regression. I'll keep learning how to simulate other data to match the other kinds of response variables I'll be measuring.