| Title: | Justifying Alpha Levels for Hypothesis Tests |
| Version: | 0.1.2 |
| Description: | Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>). |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | stats, BayesFactor, ggplot2, stringr, shiny, qpdf |
| Suggests: | knitr, rmarkdown, testthat, Superpower, pwr, shinydashboard |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-07-02 13:34:36 UTC; maxim |
| Author: | Maximilian Maier [aut, cre],
Daniel Lakens 
[aut] |
| Maintainer: | Maximilian Maier <maximilianmaier0401@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-02 14:00:02 UTC |
Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using anova.
Description
Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using anova.
Usage
ftestEvidence(evidence, df1, df2, paired = FALSE, printplot = FALSE)
Arguments
evidence |
Desired level of evidence: "Lindley" to avoid the Lindley Paradox, "moderate" to achieve moderate evidence and "strong" to achieve strong evidence. Users that are more familiar with Bayesian statistics can also directly enter their desired Bayes factor. |
df1 |
Numerator degrees of freedom. |
df2 |
Denominator degrees of freedom. |
paired |
If true a within subjects design is assumed. |
printplot |
If true prints a plot relating Bayes factors and p-values. |
Value
numeric alpha level required to avoid Lindley's paradox.
References
Maier & Lakens (2021). Justify Your Alpha: A Primer on Two Practical Approaches
Examples
## Avoid the Lindley paradox for an anova with 1 numerator and 248 denominator degrees of freedom.
ftestEvidence("lindley", 1, 248)
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 = "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
## 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$beta
res$errorrate
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_sample(
power_function,
errorgoal = 0.05,
costT1T2 = 1,
priorH1H0 = 1,
error = "minimize",
printplot = FALSE
)
Arguments
power_function |
Function that outputs the power, calculated with an analytic function. |
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. |
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, samplesize = the desired samplesize. plot = plot of alpha, beta, and error rate as a function of samplesize (only if printplot = TRUE)
References
Maier & Lakens (2021). Justify Your Alpha: A Primer on Two Practical Approaches
Examples
## Optimize power for a independent t-test, smallest effect of interest
## d = 0.5, desired weighted combined error rate = 5%
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
Launch the Justify your alpha shiny app.
Description
Launch the Justify your alpha shiny app.
Usage
runApp()
Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using a t-test.
Description
Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using a t-test.
Usage
ttestEvidence(
evidence,
n1,
n2 = 0,
one.sided = FALSE,
rscale = sqrt(2)/2,
printplot = FALSE
)
Arguments
evidence |
Desired level of evidence: "Lindley" to avoid the Lindley Paradox, "moderate" to achieve moderate evidence and "strong" to achieve strong evidence. Users that are more familiar with Bayesian statistics can also directly enter their desired Bayes factor. |
n1 |
Sample size in Group 1. |
n2 |
Sample size in Group 2. Leave blank for a one-sample or paired-sample |
one.sided |
Indicates whether the test is one sided or two sided. |
rscale |
Scale of the Cauchy prior |
printplot |
If true prints a plot relating Bayes factors and p-values. |
Value
numeric alpha level required to avoid Lindley's paradox.
References
Maier & Lakens (2021). Justify Your Alpha: A Primer on Two Practical Approaches
Examples
## Avoid the Lindley paradox for a two sample t-test with 300 participants per condition
ttestEvidence("lindley", 300, 300)