tidyBF: Tidy Wrapper for BayesFactor Package
Overview 
tidyBF package is a tidy wrapper around the BayesFactor package that
always expects the data to be in the tidy format and return a tibble
containing Bayes Factor values. Additionally, it provides a more
consistent syntax and by default returns a dataframe with rich details.
These functions can also return expressions containing results from
Bayes Factor tests that can then be displayed in custom plots.
Installation
To get the latest, stable CRAN release:
install.packages("tidyBF")You can get the development version of the package from GitHub. To
see what new changes (and bug fixes) have been made to the package since
the last release on CRAN, you can check the detailed log of changes
here:
https://indrajeetpatil.github.io/tidyBF/news/index.html
If you are in hurry and want to reduce the time of installation, prefer-
# needed package to download from GitHub repo
install.packages("remotes")
remotes::install_github(
repo = "IndrajeetPatil/tidyBF", # package path on GitHub
quick = TRUE # skips docs, demos, and vignettes
)If time is not a constraint-
remotes::install_github(
repo = "IndrajeetPatil/tidyBF", # package path on GitHub
dependencies = TRUE, # installs packages which `tidyBF` depends on
upgrade_dependencies = TRUE # updates any out of date dependencies
)Benefits
Below are few concrete examples of where tidyBF wrapper might provide
a more friendly way to access output from or write functions around
BayesFactor.
Syntax consistency
BayesFactor is inconsistent with its formula interface. tidyBF
avoids this as it doesn’t provide the formula interface for any of the
functions.
# setup
set.seed(123)
# with `BayesFactor` ----------------------------------------
suppressPackageStartupMessages(library(BayesFactor))
data(sleep)
# independent t-test: accepts formula interface
ttestBF(formula = wt ~ am, data = mtcars)
#> Bayes factor analysis
#> --------------
#> [1] Alt., r=0.707 : 1383.367 ±0%
#>
#> Against denominator:
#> Null, mu1-mu2 = 0
#> ---
#> Bayes factor type: BFindepSample, JZS
# paired t-test: doesn't accept formula interface
ttestBF(formula = extra ~ group, data = sleep, paired = TRUE)
#> Error in ttestBF(formula = extra ~ group, data = sleep, paired = TRUE): Cannot use 'paired' with formula.
# with `tidyBF` ----------------------------------------
library(tidyBF)
# independent t-test
bf_ttest(data = mtcars, x = am, y = wt)
#> # A tibble: 1 x 13
#> term estimate conf.low conf.high pd rope.percentage
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Difference -1.26 -1.70 -0.820 1 0
#> prior.distribution prior.location prior.scale effect component bf10
#> <chr> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 cauchy 0 0.707 fixed conditional 1383.
#> log_e_bf10
#> <dbl>
#> 1 7.23
# paired t-test
bf_ttest(data = sleep, x = group, y = extra, paired = TRUE)
#> # A tibble: 1 x 13
#> term estimate conf.low conf.high pd rope.percentage
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Difference 1.40 0.697 2.10 0.996 0
#> prior.distribution prior.location prior.scale effect component bf10
#> <chr> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 cauchy 0 0.707 fixed conditional 17.3
#> log_e_bf10
#> <dbl>
#> 1 2.85Expressions for plots
Although all functions default to returning a dataframe, you can also use it to extract expressions that can be displayed in plots.
t-test
# setup
set.seed(123)
library(ggplot2)
# using the expression to display details in a plot
ggplot(ToothGrowth, aes(supp, len)) +
geom_boxplot() + # two-sample t-test results in an expression
labs(subtitle = bf_ttest(ToothGrowth, supp, len, output = "alternative"))
anova
# setup
set.seed(123)
library(ggplot2)
library(ggforce)
library(tidyBF)
# plot with subtitle
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_violin() +
geom_sina() +
labs(subtitle = bf_oneway_anova(iris, Species, Sepal.Length, output = "h0"))
correlation test
# setup
set.seed(123)
library(ggplot2)
library(tidyBF)
# using the expression to display details in a plot
ggplot(mtcars, aes(wt, mpg)) + # Pearson's r results in an expression
geom_point() +
geom_smooth(method = "lm") +
labs(subtitle = bf_corr_test(mtcars, wt, mpg, output = "null"))
#> `geom_smooth()` using formula 'y ~ x'
contingency tabs analysis
# setup
set.seed(123)
library(ggplot2)
library(tidyBF)
# basic pie chart
ggplot(as.data.frame(table(mpg$class)), aes(x = "", y = Freq, fill = factor(Var1))) +
geom_bar(width = 1, stat = "identity") +
theme(axis.line = element_blank()) +
# cleaning up the chart and adding results from one-sample proportion test
coord_polar(theta = "y", start = 0) +
labs(
fill = "Class",
x = NULL,
y = NULL,
title = "Pie Chart of class (type of car)",
subtitle = bf_contingency_tab(as.data.frame(table(mpg$class)), Var1, counts = Freq, output = "h1")$expr
)
meta-analysis
# setup
set.seed(123)
library(metaviz)
library(ggplot2)
# meta-analysis forest plot with results random-effects meta-analysis
viz_forest(
x = mozart[, c("d", "se")],
study_labels = mozart[, "study_name"],
xlab = "Cohen's d",
variant = "thick",
type = "cumulative"
) + # use `statsExpressions` to create expression containing results
labs(
title = "Meta-analysis of Pietschnig, Voracek, and Formann (2010) on the Mozart effect",
subtitle = bf_meta(dplyr::rename(mozart, estimate = d, std.error = se), output = "h1")$expr
) +
theme(text = element_text(size = 12))
Convenient way to extract detailed output from BayesFactor objects
The package provides bf_extractor function to conveniently extract
important details from these objects:
# setup
set.seed(123)
library(tidyBF)
library(BayesFactor)
data(puzzles)
# model
result <-
anovaBF(
RT ~ shape * color + ID,
data = puzzles,
whichRandom = "ID",
whichModels = "top",
progress = FALSE
)
# extract details
bf_extractor(result)
#> # A tibble: 21 x 13
#> term estimate conf.low conf.high pd rope.percentage
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu 45.0 43.9 46.1 1 0
#> 2 shape-round 0.429 0.131 0.741 0.992 0.141
#> 3 shape-square -0.429 -0.741 -0.131 0.992 0.141
#> 4 color-color -0.426 -0.711 -0.0995 0.990 0.162
#> 5 color-monochromatic 0.426 0.0995 0.711 0.990 0.162
#> 6 ID-1 2.47 1.04 3.98 0.995 0
#> 7 ID-2 0.439 -1.02 1.78 0.698 0.231
#> 8 ID-3 0.907 -0.501 2.32 0.848 0.156
#> 9 ID-4 0.466 -0.959 1.98 0.704 0.218
#> 10 ID-5 3.17 1.83 4.66 0.999 0
#> prior.distribution prior.location prior.scale effect component bf10
#> <chr> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 <NA> NA NA fixed extra 2.65
#> 2 <NA> NA NA fixed conditional 0.233
#> 3 <NA> NA NA fixed conditional 0.239
#> 4 <NA> NA NA fixed conditional 2.65
#> 5 <NA> NA NA fixed conditional 0.233
#> 6 <NA> NA NA random conditional 0.239
#> 7 <NA> NA NA random conditional 2.65
#> 8 <NA> NA NA random conditional 0.233
#> 9 <NA> NA NA random conditional 0.239
#> 10 <NA> NA NA random conditional 2.65
#> log_e_bf10
#> <dbl>
#> 1 0.974
#> 2 -1.45
#> 3 -1.43
#> 4 0.974
#> 5 -1.45
#> 6 -1.43
#> 7 0.974
#> 8 -1.45
#> 9 -1.43
#> 10 0.974
#> # ... with 11 more rowsDataframe with all the details
BayesFactor can return the Bayes Factor value corresponding to either
evidence in favor of the null hypothesis over the alternative hypothesis
(BF01) or in favor of the alternative over the null (BF10),
depending on how this object is called. tidyBF on the other hand
return both of these values and their logarithms.
# `BayesFactor` object
bf <- BayesFactor::correlationBF(y = iris$Sepal.Length, x = iris$Petal.Length)
# alternative
bf
#> Bayes factor analysis
#> --------------
#> [1] Alt., r=0.333 : 2.136483e+43 ±0%
#>
#> Against denominator:
#> Null, rho = 0
#> ---
#> Bayes factor type: BFcorrelation, Jeffreys-beta*
# null
1 / bf
#> Bayes factor analysis
#> --------------
#> [1] Null, rho = 0 : 4.680589e-44 ±0%
#>
#> Against denominator:
#> Alternative, r = 0.333333333333333, rho =/= 0
#> ---
#> Bayes factor type: BFcorrelation, Jeffreys-beta*
# `tidyBF` output
bf_corr_test(iris, Sepal.Length, Petal.Length, bf.prior = 0.333)
#> # A tibble: 1 x 13
#> term estimate conf.low conf.high pd rope.percentage prior.distribution
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 rho 0.863 0.828 0.897 1 0 cauchy
#> prior.location prior.scale effect component bf10 log_e_bf10
#> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 0 0.333 fixed conditional 2.13e43 99.8Note that the log-transformed values are helpful because in case of strong effects, the raw Bayes Factor values can be pretty large, but the log-transformed values continue to remain easy to work with.
Acknowledgments
The hexsticker was generously designed by Sarah Otterstetter (Max Planck Institute for Human Development, Berlin).
Code of Conduct
Please note that the tidyBF project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.