bfrr: Bayes Factor with Robustness Region (normal)

In debruine/bfrr: Bayes Factors and Robustness Regions

Description

Bayes Factor with Robustness Region (normal)

Usage

bfrr(sample_mean = 0, sample_se = 0.1, sample_df = 99,
  model = "normal", ..., criterion = 3, rr_interval = NA,
  precision = 0.05)

Arguments

sample_mean	the observed sample mean
sample_se	the observed sample standard error
sample_df	the observed sample degrees of freedom
model	the model under which to calculate likelihood (H0, normal or uniform
...	model parameters (mean, sd and tail for normal, lower and upper for uniform)
criterion	the cutoff Bayes Factor for concluding evidence in favour of H0 or H1
rr_interval	the parameter intervals within which to test for robustness
precision	the step size for calculating the robustness region (default 0.05)

Value

list

Examples

rr <- bfrr(0.5, 1/sqrt(30), 29, "normal", mean = 0, sd = .7, tail = 1)
summary(rr)
plot(rr)

debruine/bfrr documentation built on March 7, 2020, 5:47 p.m.