In debruine/bfrr: Bayes Factors and Robustness Regions

knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
set.seed(8675309)

bfrr

Calculate Bayes Factors and robustness regions from summary statistics.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("debruine/bfrr")

Example

library(bfrr)
library(ggplot2)
library(cowplot)

First, we'll simulate 50 data points from a normal distribution with a mean of 0.25 and SD of 1 and conduct a one-sample t-test.

simdat <- rnorm(50, 0.25, 1)
t.test(simdat)

Set up the test using bfrr().

rr <- bfrr(
  sample_mean = mean(simdat), # mean of the sample
  sample_se = sd(simdat) / sqrt(length(simdat)), # SE of the sample
  sample_df = length(simdat) - 1, # degrees of freedom
  model = "normal",
  mean = 0, # mean of the H1 distribution
  sd = 0.25, # SD of the H1 distribution
  tail = 1, # is the test 1-tailed or 2-tailed
  criterion = 6, # BF against which to test for support for H1/H0
  rr_interval = list( # ranges to vary H1 parameters for robustness regions
    mean = c(-2, 2), # explore H1 means from 0 to 2
    sd = c(0, 2) # explore H1 SDs from 0 to 2
  ),
  precision = 0.05 # precision to vary RR parameters
)

Use summary(rr) to output a summary paragraph.

summary(rr)

Use plot(rr) to view a plot of your data.

plot(rr)

If your mean is 0 or you set the same number for the lower and upper bounds of a parameter's rr_interval, that parameter won't vary and you'll get a graph that looks like this.

r1 <- bfrr(sample_mean = 0.25, tail = 1)
r2 <- bfrr(sample_mean = 0.25, tail = 2)
p1 <- plot(r1)
p2 <- plot(r2)

p1t <- paste0("One-tailed H1, RR = [", toString(r1$RR$sd), "]")
p2t <- paste0("Two-tailed H1, RR = [", toString(r2$RR$sd), "]")
cowplot::plot_grid(p1 + ggtitle(p1t), 
                   p2 + ggtitle(p2t), 
                   nrow = 2)