View source: R/minimize_balance_alpha.R

optimal_alpha | R Documentation |

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

optimal_alpha( power_function, costT1T2 = 1, priorH1H0 = 1, error = "minimal", plot = Superpower_options("plot") )

`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 |

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 =

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## 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

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