c_power_fast: Estimate power for conflict effect using monte-carlo...

In CrumpLab/conflictPower: Simulation based power analysis for adaptive control designs

Estimate power for conflict effect using monte-carlo simulation, faster version

Description

Estimate power for conflict effect using monte-carlo simulation, faster version

Usage

c_power_fast(subjects, c_nmst, nc_nmst, num_sims = 10000, alpha = 0.05)

Arguments

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

Details

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.

Value

A number, power: the proportion of simulated experiments that returned a p-value less than the alpha criterion for the conflict effect

Examples

c_power_fast(subjects=10,
  c_nmst = c(20,550,100,100),
  nc_nmst = c(20,500,100,100),
  num_sims = 1000,
  alpha = .05)

CrumpLab/conflictPower documentation built on May 17, 2022, 5:48 a.m.