c_power_fast.RdEstimate power for conflict effect using monte-carlo simulation, faster version
c_power_fast(subjects, c_nmst, nc_nmst, num_sims = 10000, alpha = 0.05)
| subjects | A number for the number of subjects in simulated experiment |
|---|---|
| c_nmst | A vector containing the parameters for an ex-gaussian distribution, c(n, mu, sigma, tau), where n is the number of trials. |
| nc_nmst | A vector 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 is a faster version of c_power.
This function uses monte-carlo simulation to determine statistical power associated for detecting a conflict effect, and includes paramaters for number of subjects in the experiment, number of trials in each condition (conflict vs. no-conflict), and paramaters (mu,sigma,tau) for each reaction time distribution.
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.
c_power_fast(subjects=10, c_nmst = c(20,550,100,100), nc_nmst = c(20,500,100,100), num_sims = 1000, alpha = .05) #> [1] 0.879