EDR: Compute the empirical detection/rejection rate for Type I...

In philchalmers/SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Compute the empirical detection/rejection rate for Type I errors and Power

Description

Computes the detection/rejection rate for determining empirical Type I error and power rates using information from p-values.

Usage

EDR(p, alpha = 0.05, unname = FALSE)

Arguments

p	a numeric vector or matrix/data.frame of p-values from the desired statistical estimator. If a matrix, each statistic must be organized by column, where the number of rows is equal to the number of replications
alpha	the detection threshold (typical values are .10, .05, and .01). Default is .05
unname	logical; apply unname to the results to remove any variable names?

Author(s)

Phil Chalmers [e-mail]

References

Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248")}

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}

See Also

ECR, Bradley1978

Examples

rates <- numeric(100)
for(i in 1:100){
   dat <- rnorm(100)
   rates[i] <- t.test(dat)$p.value
}

EDR(rates)
EDR(rates, alpha = .01)

# multiple rates at once
rates <- cbind(runif(1000), runif(1000))
EDR(rates)

philchalmers/SimDesign documentation built on April 25, 2024, 5:35 a.m.