JABp: Title

In alphaN: Set Alpha Based on Sample Size Using Bayes Factors

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)

alphaN documentation built on Jan. 13, 2023, 1:14 a.m.