JABp {alphaN}

Title

Description

Title

Usage

JABp(n, p, z = TRUE, df = NULL, method = "JAB", upper = 1)

Arguments

n

Sample size.

p

The p-value.

z

Is the p-value based on a z- or t-statistic? TRUE if z.

df

If z=FALSE, provide the degrees of freedom for the t-statistic.

method

Used for the choice of 'b'. Currently one of:

• "JAB": this choice of b produces Jeffery's approximate BF (Wagenmakers, 2022)

• "min": uses the minimal training sample for the prior (Gu et al., 2018)

• "robust": a robust version of "min" that prevents too small b (O'Hagan, 1995)

• "balanced": this choice of b balances the type I and type II errors (Gu et al, 2016)

upper

The upper limit for the range of realistic effect sizes. Only relevant when method="balanced". Defaults to 1 such that the range of realistic effect sizes is uniformly distributed between 0 and 1, U(0,1).

Value

A numeric value for the BF in favour of H1.

Examples

# Transform a p-value of 0.007038863 from a z-test into JAB
# using a sample size of 200.
JABp(200, 0.007038863)

# Transform a p-value of 0.007038863 from a t-test with 190
# degrees of freedom into JAB using a sample size of 200.
JABp(200, 0.007038863, z=FALSE, df=190)