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https://github.com/IndrajeetPatil/pairwiseComparisons
Pairwise comparison tests for one-way designs 🔬📝
https://github.com/IndrajeetPatil/pairwiseComparisons
bayes-factor pairwise-comparison-tests parametric robust statistics
Last synced: 2 months ago
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Pairwise comparison tests for one-way designs 🔬📝
- Host: GitHub
- URL: https://github.com/IndrajeetPatil/pairwiseComparisons
- Owner: IndrajeetPatil
- License: other
- Archived: true
- Created: 2019-08-21T16:58:17.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-03-23T22:07:18.000Z (almost 3 years ago)
- Last Synced: 2024-08-06T03:04:08.631Z (6 months ago)
- Topics: bayes-factor, pairwise-comparison-tests, parametric, robust, statistics
- Language: R
- Homepage: https://indrajeetpatil.github.io/pairwiseComparisons/
- Size: 6.12 MB
- Stars: 45
- Watchers: 3
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS.txt
Awesome Lists containing this project
- jimsghstars - IndrajeetPatil/pairwiseComparisons - Pairwise comparison tests for one-way designs 🔬📝 (R)
README
---
output: github_document
---
```{r, echo = FALSE}
# pretty printing for the tibble
options(
tibble.width = Inf,
pillar.bold = TRUE,
pillar.neg = TRUE,
pillar.subtle_num = TRUE,
pillar.min_chars = Inf
)
knitr::opts_chunk$set(
collapse = TRUE,
dpi = 300,
out.width = "100%",
comment = "#>",
warning = FALSE,
message = FALSE,
fig.path = "man/figures/README-"
)
```
# `{pairwiseComparisons}`: Multiple Pairwise Comparison Tests
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://github.com/IndrajeetPatil/pairwiseComparisons)
[](https://github.com/IndrajeetPatil/pairwiseComparisons/actions)
# Introduction 
[`{pairwiseComparisons}`](https://indrajeetpatil.github.io/pairwiseComparisons/)
provides a tidy data friendly way to carry out pairwise comparison tests.
It currently supports *post hoc* multiple pairwise comparisons tests for both
between-subjects and within-subjects one-way analysis of variance designs. For
both of these designs, parametric, non-parametric, robust, and Bayesian
statistical tests are available.
# Installation
Type | Source | Command
---|---|---
Release | CRAN | `install.packages("pairwiseComparisons")`
Development | GitHub | `remotes::install_github("IndrajeetPatil/pairwiseComparisons")`
Linux users may encounter some installation problems. In particular, the
`{pairwiseComparisons}` package depends on the `PMCMRplus` package.
```
ERROR: dependencies ‘gmp’, ‘Rmpfr’ are not available for package ‘PMCMRplus’
ERROR: dependency ‘pairwiseComparisons’ is not available for package ‘ggstatsplot’
```
This means that your operating system lacks `gmp` and `Rmpfr` libraries.
If you use `Ubuntu`, you can install these dependencies:
```
sudo apt-get install libgmp3-dev
sudo apt-get install libmpfr-dev
```
The following `README` file briefly describes the installation procedure:
# Summary of types of statistical analyses
Following table contains a brief summary of the currently supported pairwise
comparison tests-
## Between-subjects design
Type | Equal variance? | Test | *p*-value adjustment? | Function used
----------- | --- | ------------------------- | --- | -----
Parametric | No | Games-Howell test | ✅ | `stats::pairwise.t.test`
Parametric | Yes | Student's *t*-test | ✅ | `PMCMRplus::gamesHowellTest`
Non-parametric | No | Dunn test | ✅ | `PMCMRplus::kwAllPairsDunnTest`
Robust | No | Yuen's trimmed means test | ✅ | `WRS2::lincon`
Bayesian | `NA` | Student's *t*-test | `NA` | `BayesFactor::ttestBF`
## Within-subjects design
Type | Test | *p*-value adjustment? | Function used
----------- | ---------------------------- | --- | -----
Parametric | Student's *t*-test | ✅ | `stats::pairwise.t.test`
Non-parametric | Durbin-Conover test | ✅ | `PMCMRplus::durbinAllPairsTest`
Robust | Yuen's trimmed means test | ✅ | `WRS2::rmmcp`
Bayesian | Student's *t*-test | `NA` | `BayesFactor::ttestBF`
# Examples
Here we will see specific examples of how to use this function for different
types of
- designs (between or within subjects)
- statistics (parametric, non-parametric, robust, Bayesian)
- *p*-value adjustment methods
## Between-subjects design
```{r}
# for reproducibility
set.seed(123)
library(pairwiseComparisons)
library(statsExpressions) # for data
# parametric
# if `var.equal = TRUE`, then Student's *t*-test will be run
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "parametric",
var.equal = TRUE,
paired = FALSE,
p.adjust.method = "bonferroni"
)
# if `var.equal = FALSE`, then Games-Howell test will be run
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "parametric",
var.equal = FALSE,
paired = FALSE,
p.adjust.method = "bonferroni"
)
# non-parametric
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "nonparametric",
paired = FALSE,
p.adjust.method = "none"
)
# robust
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "robust",
paired = FALSE,
p.adjust.method = "fdr"
)
# Bayesian
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "bayes",
paired = FALSE
)
```
## Within-subjects design
```{r}
# for reproducibility
set.seed(123)
# parametric
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "parametric",
paired = TRUE,
p.adjust.method = "BH"
)
# non-parametric
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "nonparametric",
paired = TRUE,
p.adjust.method = "BY"
)
# robust
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "robust",
paired = TRUE,
p.adjust.method = "hommel"
)
# Bayesian
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "bayes",
paired = TRUE,
bf.prior = 0.77
)
```
# Using `{pairwiseComparisons}` with `ggsignif`
## Example-1: between-subjects
```{r ggsignif, fig.height=5}
# needed libraries
set.seed(123)
library(ggplot2)
library(pairwiseComparisons)
library(ggsignif)
# converting to factor
mtcars$cyl <- as.factor(mtcars$cyl)
# creating a basic plot
p <- ggplot(mtcars, aes(cyl, wt)) +
geom_boxplot()
# using `{pairwiseComparisons}` package to create a dataframe with results
set.seed(123)
(df <-
pairwise_comparisons(mtcars, cyl, wt) %>%
dplyr::mutate(groups = purrr::pmap(.l = list(group1, group2), .f = c)) %>%
dplyr::arrange(group1))
# using `geom_signif` to display results
# (note that you can choose not to display all comparisons)
p +
ggsignif::geom_signif(
comparisons = list(df$groups[[1]]),
annotations = df$label[[1]],
test = NULL,
na.rm = TRUE,
parse = TRUE
)
```
## Example-2: within-subjects
```{r ggsignif2}
# needed libraries
library(ggplot2)
library(pairwiseComparisons)
library(ggsignif)
# creating a basic plot
p <- ggplot(WRS2::WineTasting, aes(Wine, Taste)) +
geom_boxplot()
# using `{pairwiseComparisons}` package to create a dataframe with results
set.seed(123)
(df <-
pairwise_comparisons(
WRS2::WineTasting,
Wine,
Taste,
subject.id = Taster,
type = "bayes",
paired = TRUE
) %>%
dplyr::mutate(groups = purrr::pmap(.l = list(group1, group2), .f = c)) %>%
dplyr::arrange(group1))
# using `geom_signif` to display results
p +
ggsignif::geom_signif(
comparisons = df$groups,
map_signif_level = TRUE,
tip_length = 0.01,
y_position = c(6.5, 6.65, 6.8),
annotations = df$label,
test = NULL,
na.rm = TRUE,
parse = TRUE
)
```
# Acknowledgments
The hexsticker was generously designed by Sarah Otterstetter (Max Planck
Institute for Human Development, Berlin).