JAB: Transforms a t-statistic from a glm or lm object into...

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

Transforms a t-statistic from a glm or lm object into Jeffreys' approximate Bayes factor

Description

Transforms a t-statistic from a glm or lm object into Jeffreys' approximate Bayes factor

Usage

JAB(glm_obj, covariate, method = "JAB", upper = 1)

Arguments

glm_obj	a glm or lm object.
covariate	the name of the covariate that you want a BF for as a string.
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

# Simulate data

## Sample size
n <- 200

## Regressors
Z1 <- runif(n, -1, 1)
Z2 <- runif(n, -1, 1)
Z3 <- runif(n, -1, 1)
Z4 <- runif(n, -1, 1)
X <- runif(n, -1, 1)

## Error term
U <- rnorm(n, 0, 0.5)

## Outcome
Y <- X/sqrt(n) + U

# Run a GLM
LM <- glm(Y ~ X + Z1 + Z2 + Z3 + Z4)

# Compute JAB for "X" based on the regression results
JAB(LM, "X")

# Compute JAB using the minimum prior
JAB(LM, "X", method = "min")

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