A fitted GLM model for the power of radEmu to reject a truly false null hypothesis
Source:R/power_glm.R
power_model.Rd100 simulations were drawn with n = 10, 30, 50 and 100 total samples; J = 250 and 500 taxa; a single categorical covariate (p = 2); effect sizes beta11 (the parameter of interest) from 0, 0.5, 1, ... 2.5. beta0's range from -3 to 3, and the other beta1's range from -1 to 1 with no correlation between the beta0s (which, roughly speaking, control the relative abundance of the taxa) and the beta1s (which control the difference in abundance between the two covariate groups). Counts were drawn from a zero-inflated negative binomial model with size parameter 5, zero-inflation probability of 0.5 and average z's around log(50). A model for the probability of rejecting the null hypothesis of beta11 = 0 was fit. Model fitting was guided by plotting the log odds of rejection, where effect modification between n and beta11 was observed. This model may be useful for power calculations in future, though as with any simulation, its generalizability is limited to similar data generating processes. Simulation code that can be generalized is available at https://github.com/statdivlab/radEmu_supplementary under fig-power/power_simulations.R