Jesper N. Wulff

# alphaN

The goal of alphaN is to help the user set their significance level
as a function of the sample size. The function `alphaN`

allows users to set the significance level as function of the sample
size based on the evidence and the prior features they desire. The
function `JABt`

and `JABp`

converts test
statistics and \(p\)-values into sample
size dependent Bayes factors. `JAB_plot`

plots the Bayes
factor as a function of the \(p\)-value, and `alphaN_plot()`

plots the alpha level as a function of sample size for a given Bayes
factor.

## Installation

You can install the development version of alphaN from GitHub with:

```
# install.packages("devtools")
devtools::install_github("jespernwulff/alphaN")
```

## Example

Here is an example: We are planning to run a linear regression model
with 1000 observations. We thus set `n = 1000`

. The default
`BF`

is 1 meaning that we want to avoid Lindley’s paradox,
i.e. we just want the null and the alternative to be at least equally
likely when we reject the null.

```
library(alphaN)
alpha <- alphaN(n = 1000, BF = 1)
alpha
#> [1] 0.008582267
```

Therefore, to obtain evidence of at least 1, we should set our alpha
to 0.0086.