`optimal_sample.Rd`

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

optimal_sample( power_function, errorgoal = 0.05, costT1T2 = 1, priorH1H0 = 1, error = "minimize", printplot = FALSE )

power_function | Function that outputs the power, calculated with an analytic function. |
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errorgoal | Desired weighted combined error rate |

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. |

printplot | Print a plot to illustrate the alpha level calculation. This will make the function considerably slower. |

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, samplesize = the desired samplesize.

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

## Optimize power for a independent t-test, smallest effect of interest ## d = 0.5, desired weighted combined error rate = 5% if (FALSE) { res <- optimal_sample(power_function = "pwr::pwr.t.test(d = 0.5, n = sample_n, sig.level = x, type = 'two.sample', alternative = 'two.sided')$power",errorgoal = 0.05) res$alpha res$beta res$errorrate res$samplesize }