optimal_alpha {Superpower} | R Documentation |
Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates.
Description
Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates.
Usage
optimal_alpha(
power_function,
costT1T2 = 1,
priorH1H0 = 1,
error = "minimal",
plot = Superpower_options("plot")
)
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 "minimal" to minimize error rates, or "balance" to balance error rate |
plot |
When set to TRUE, automatically outputs a plot of alpha (x-axis) and beta (y-axis) error rates |
Value
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 objective = value that is the result of the minimization, either 0 (for balance) or the combined weighted error rates plot =
References
too be added
Examples
## 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
[Package Superpower version 0.2.0 Index]