The results are shown below, although for clarity I took the log of the p-value. ![]() my.design with(my.design, aggregate(p, list(es=es), mean)) Then, I created a grid of values for es and msb, that is I want to check whether varying these parameters has an effect on the estimated p-value. Here is a toy example for simulating a one-way ANOVA in R.įirst, I just defined a general function that expect an effect size ( es), which is simply the ratio MSB/MSW (between/within mean squares), a value for the MSB, the number of groups, which might or not be of equal sizes: sim.exp sim.exp(verbose=TRUE)
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