pc_power_fast.RdEstimate power for proportion conflict effect using monte-carlo simulation
pc_power_fast( subjects, mc_c_nmst, mc_nc_nmst, mnc_c_nmst, mnc_nc_nmst, num_sims = 1000, alpha = 0.05 )
| subjects | A number for the number of subjects in simulated experiment |
|---|---|
| mc_c_nmst | A vector for mostly conflict (mc), conflict trials (c) containing the parameters for an ex-gaussian distribution, c(n, mu, sigma, tau), where n is the number of trials. |
| mc_nc_nmst | A vector for mostly conflict (mc), conflict trials (c) containing the parameters for an ex-gaussian distribution, c(n, mu, sigma, tau), where n is the number of trials. |
| mnc_c_nmst | A vector for mostly no-conflict (mnc), conflict trials (c) containing the parameters for an ex-gaussian distribution, c(n, mu, sigma, tau), where n is the number of trials. |
| mnc_nc_nmst | A vector for mostly no-conflict (mnc), conflict trials (c) containing the parameters for an ex-gaussian distribution, c(n, mu, sigma, tau), where n is the number of trials. |
| num_sims | A number, simulations to run |
| alpha | A number, alpha criterion |
A number, power: the proportion of simulated experiments that returned a p-value less than the alpha criterion for the conflict effect
This function uses monte-carlo simulation to determine statistical power associated for detecting a proportion conflict effect, specifically a difference between two conflict effect, typically based on a manipulation of the proportion of conflict and no-conflict trials.
For every simulated experiment, a one sample t-test (two-tailed) is computed, and the p-value is saved. Power is the proportion of simulated experiments that return p-values less than the defined alpha criterion.
pc_power_fast(subjects=10, mc_c_nmst = c(80,550,100,100), mc_nc_nmst = c(20,500,100,100), mnc_c_nmst = c(20,570,100,100), mnc_nc_nmst = c(80,500,100,100), num_sims = 1000, alpha = .05) #> [1] 0.205