# optimal_alpha: Justify your alpha level by minimizing or balancing Type 1... In JustifyAlpha: Justifying Alpha Levels for Hypothesis Tests

## Description

Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates.

## Usage

 1 2 3 4 5 6 7 8 optimal_alpha( power_function, costT1T2 = 1, priorH1H0 = 1, error = "minimize", verbose = FALSE, printplot = FALSE )

## Arguments

 power_function Function that outputs the power, calculated with an analytic function. costT1T2 Relative cost of Type 1 errors vs. Type 2 errors. priorH1H0 How much more likely a-priori is H1 than H0? error Either "minimize" to minimize error rates, or "balance" to balance error rates. verbose Print each iteration of the optimization function if TRUE. Defaults to FALSE. printplot Print a plot to illustrate the alpha level calculation.

## Value

Returns a list of the following alpha = alpha or Type 1 error that minimizes or balances combined error rates, beta = beta or Type 2 error that minimizes or balances combined error rates, errorrate = weighted combined error rate, objective = value that is the result of the minimization, either 0 (for balance) or the combined weighted error rates. plot_data = data used for plotting (only if printplot = TRUE) plot = plot of error rates depending on alpha (only if printplot = TRUE)

## References

Maier & Lakens (2021). Justify Your Alpha: A Primer on Two Practical Approaches

## Examples

 1 2 3 4 5 6 7 ## Optimize power for a independent t-test, smallest effect of interest ## d = 0.5, 100 participants per condition res <- optimal_alpha(power_function = "pwr::pwr.t.test(d = 0.5, n = 100, sig.level = x, type = 'two.sample', alternative = 'two.sided')\$power") res\$alpha res\$beta res\$errorate

JustifyAlpha documentation built on Sept. 15, 2021, 9:08 a.m.