pairwiseComparisons
: Multiple Pairwise Comparison Tests
Package | Status | Usage | GitHub | References |
---|---|---|---|---|
Introduction
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 Bayes Factor statistical tests are available.
Installation
To get the latest, stable CRAN
release:
install.packages("pairwiseComparisons")
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/pairwiseComparisons/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")
# downloading the package from GitHub
remotes::install_github(
repo = "IndrajeetPatil/pairwiseComparisons", # package path on GitHub
dependencies = FALSE, # assumes you have already installed needed packages
quick = TRUE # skips docs, demos, and vignettes
)
If time is not a constraint-
remotes::install_github(
repo = "IndrajeetPatil/pairwiseComparisons", # package path on GitHub
dependencies = TRUE, # installs packages which pairwiseComparisons depends on
upgrade_dependencies = TRUE # updates any out of date dependencies
)
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? |
---|---|---|---|
Parametric | No | Games-Howell test | Yes |
Parametric | Yes | Student’s t-test | Yes |
Non-parametric | No | Dunn test | Yes |
Robust | No | Yuen’s trimmed means test | Yes |
Bayes Factor | NA | Student’s t-test | NA |
Within-subjects design
Type | Test | p-value adjustment? |
---|---|---|
Parametric | Student’s t-test | Yes |
Non-parametric | Durbin-Conover test | Yes |
Robust | Yuen’s trimmed means test | Yes |
Bayes Factor | Student’s t-test | NA |
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, Bayes Factor)
- p-value adjustment methods
Between-subjects design
# for reproducibility
set.seed(123)
library(pairwiseComparisons)
# 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"
)
#> # A tibble: 6 x 8
#> group1 group2 mean.difference p.value significance label test.details p.value.adjustment
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 carni herbi 0.542 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Student's t-test Bonferroni
#> 2 carni insecti -0.0577 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Student's t-test Bonferroni
#> 3 carni omni 0.0665 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Student's t-test Bonferroni
#> 4 herbi insecti -0.600 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Student's t-test Bonferroni
#> 5 herbi omni -0.476 0.979 ns list(~italic(p)[ adjusted ]== 0.979 ) Student's t-test Bonferroni
#> 6 insecti omni 0.124 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Student's t-test 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"
)
#> # A tibble: 6 x 11
#> group1 group2 mean.difference se t.value df p.value significance label test.details
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 carni insecti -0.058 0.027 1.53 10.7 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Games-Howell test
#> 2 herbi carni -0.542 0.25 1.54 19.4 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Games-Howell test
#> 3 herbi insecti -0.6 0.249 1.70 19.1 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Games-Howell test
#> 4 omni carni -0.066 0.061 0.774 21.1 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Games-Howell test
#> 5 omni herbi 0.476 0.255 1.32 20.9 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Games-Howell test
#> 6 omni insecti -0.124 0.057 1.55 17.2 1 ns list(~italic(p)[ adjusted ]== 1.000 ) Games-Howell test
#> p.value.adjustment
#> <chr>
#> 1 Bonferroni
#> 2 Bonferroni
#> 3 Bonferroni
#> 4 Bonferroni
#> 5 Bonferroni
#> 6 Bonferroni
# non-parametric
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "nonparametric",
paired = FALSE,
p.adjust.method = "none"
)
#> # A tibble: 6 x 8
#> group1 group2 z.value p.value significance label test.details p.value.adjustment
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 carni herbi 0.582 0.561 ns list(~italic(p)[ unadjusted ]== 0.561 ) Dunn test None
#> 2 carni insecti 1.88 0.0595 ns list(~italic(p)[ unadjusted ]== 0.060 ) Dunn test None
#> 3 carni omni 1.14 0.254 ns list(~italic(p)[ unadjusted ]== 0.254 ) Dunn test None
#> 4 herbi insecti 1.63 0.102 ns list(~italic(p)[ unadjusted ]== 0.102 ) Dunn test None
#> 5 herbi omni 0.717 0.474 ns list(~italic(p)[ unadjusted ]== 0.474 ) Dunn test None
#> 6 insecti omni 1.14 0.254 ns list(~italic(p)[ unadjusted ]== 0.254 ) Dunn test None
# robust
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "robust",
paired = FALSE,
p.adjust.method = "fdr"
)
#> # A tibble: 6 x 10
#> group1 group2 psihat conf.low conf.high p.value significance label test.details
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 carni herbi -0.0530 -0.274 0.168 0.969 ns list(~italic(p)[ adjusted ]== 0.969 ) Yuen's trimmed means test
#> 2 carni insecti 0.0577 -0.0609 0.176 0.969 ns list(~italic(p)[ adjusted ]== 0.969 ) Yuen's trimmed means test
#> 3 carni omni 0.00210 -0.151 0.155 0.969 ns list(~italic(p)[ adjusted ]== 0.969 ) Yuen's trimmed means test
#> 4 herbi insecti 0.111 -0.0983 0.320 0.969 ns list(~italic(p)[ adjusted ]== 0.969 ) Yuen's trimmed means test
#> 5 herbi omni 0.0551 -0.173 0.283 0.969 ns list(~italic(p)[ adjusted ]== 0.969 ) Yuen's trimmed means test
#> 6 insecti omni -0.0556 -0.184 0.0728 0.969 ns list(~italic(p)[ adjusted ]== 0.969 ) Yuen's trimmed means test
#> p.value.adjustment
#> <chr>
#> 1 Benjamini & Hochberg
#> 2 Benjamini & Hochberg
#> 3 Benjamini & Hochberg
#> 4 Benjamini & Hochberg
#> 5 Benjamini & Hochberg
#> 6 Benjamini & Hochberg
# Bayes Factor
pairwise_comparisons(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "bayes",
paired = FALSE
)
#> # A tibble: 6 x 12
#> group1 group2 bf10 error bf01 log_e_bf10 log_e_bf01 log_10_bf10 log_10_bf01 bf.prior label test.details
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 omni herbi 0.571 0.0000411 1.75 -0.560 0.560 -0.243 0.243 0.707 list(~log[e](BF[10])==-0.56) Student's t-test
#> 2 omni carni 0.427 0.000105 2.34 -0.851 0.851 -0.369 0.369 0.707 list(~log[e](BF[10])==-0.85) Student's t-test
#> 3 omni insecti 0.545 0.0000190 1.83 -0.606 0.606 -0.263 0.263 0.707 list(~log[e](BF[10])==-0.61) Student's t-test
#> 4 herbi carni 0.540 0.0000100 1.85 -0.617 0.617 -0.268 0.268 0.707 list(~log[e](BF[10])==-0.62) Student's t-test
#> 5 herbi insecti 0.540 0.0000175 1.85 -0.616 0.616 -0.267 0.267 0.707 list(~log[e](BF[10])==-0.62) Student's t-test
#> 6 carni insecti 0.718 0.000152 1.39 -0.332 0.332 -0.144 0.144 0.707 list(~log[e](BF[10])==-0.33) Student's t-test
Within-subjects design
# for reproducibility
set.seed(123)
# parametric
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
type = "parametric",
paired = TRUE,
p.adjust.method = "BH"
)
#> # A tibble: 6 x 8
#> group1 group2 mean.difference p.value significance label test.details p.value.adjustment
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 HDHF HDLF -1.11 1.00e- 3 *** list(~italic(p)[ adjusted ]== 0.001 ) Student's t-test Benjamini & Hochberg
#> 2 HDHF LDHF -0.474 7.07e- 2 ns list(~italic(p)[ adjusted ]== 0.071 ) Student's t-test Benjamini & Hochberg
#> 3 HDHF LDLF -2.14 7.64e-12 *** list(~italic(p)[ adjusted ]<= 0.001 ) Student's t-test Benjamini & Hochberg
#> 4 HDLF LDHF 0.637 5.47e- 2 ns list(~italic(p)[ adjusted ]== 0.055 ) Student's t-test Benjamini & Hochberg
#> 5 HDLF LDLF -1.03 1.39e- 3 ** list(~italic(p)[ adjusted ]== 0.001 ) Student's t-test Benjamini & Hochberg
#> 6 LDHF LDLF -1.66 6.67e- 9 *** list(~italic(p)[ adjusted ]<= 0.001 ) Student's t-test Benjamini & Hochberg
# non-parametric
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
type = "nonparametric",
paired = TRUE,
p.adjust.method = "BY"
)
#> # A tibble: 6 x 8
#> group1 group2 W p.value significance label test.details p.value.adjustment
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 HDHF HDLF 4.78 1.44e- 5 *** list(~italic(p)[ adjusted ]<= 0.001 ) Durbin-Conover test Benjamini & Yekutieli
#> 2 HDHF LDHF 2.44 4.47e- 2 * list(~italic(p)[ adjusted ]== 0.045 ) Durbin-Conover test Benjamini & Yekutieli
#> 3 HDHF LDLF 8.01 5.45e-13 *** list(~italic(p)[ adjusted ]<= 0.001 ) Durbin-Conover test Benjamini & Yekutieli
#> 4 HDLF LDHF 2.34 4.96e- 2 * list(~italic(p)[ adjusted ]== 0.050 ) Durbin-Conover test Benjamini & Yekutieli
#> 5 HDLF LDLF 3.23 5.05e- 3 ** list(~italic(p)[ adjusted ]== 0.005 ) Durbin-Conover test Benjamini & Yekutieli
#> 6 LDHF LDLF 5.57 4.64e- 7 *** list(~italic(p)[ adjusted ]<= 0.001 ) Durbin-Conover test Benjamini & Yekutieli
# robust
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
type = "robust",
paired = TRUE,
p.adjust.method = "hommel"
)
#> # A tibble: 6 x 10
#> group1 group2 psihat conf.low conf.high p.value significance label test.details
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 HDHF HDLF 1.16 0.318 2.00 1.49e- 3 ** list(~italic(p)[ adjusted ]== 0.001 ) Yuen's trimmed means test
#> 2 HDHF LDHF 0.5 -0.188 1.19 6.20e- 2 ns list(~italic(p)[ adjusted ]== 0.062 ) Yuen's trimmed means test
#> 3 HDHF LDLF 2.10 1.37 2.82 1.79e-10 *** list(~italic(p)[ adjusted ]<= 0.001 ) Yuen's trimmed means test
#> 4 HDLF LDHF -0.701 -1.71 0.303 6.20e- 2 ns list(~italic(p)[ adjusted ]== 0.062 ) Yuen's trimmed means test
#> 5 HDLF LDLF 0.938 0.0694 1.81 1.36e- 2 * list(~italic(p)[ adjusted ]== 0.014 ) Yuen's trimmed means test
#> 6 LDHF LDLF 1.54 0.810 2.27 1.16e- 6 *** list(~italic(p)[ adjusted ]<= 0.001 ) Yuen's trimmed means test
#> p.value.adjustment
#> <chr>
#> 1 Hommel
#> 2 Hommel
#> 3 Hommel
#> 4 Hommel
#> 5 Hommel
#> 6 Hommel
# Bayes Factor
pairwise_comparisons(
data = WRS2::WineTasting,
x = Wine,
y = Taste,
type = "bayes",
paired = TRUE,
bf.prior = 0.77
)
#> # A tibble: 3 x 12
#> group1 group2 bf10 error bf01 log_e_bf10 log_e_bf01 log_10_bf10 log_10_bf01 bf.prior label
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 Wine A Wine B 0.219 0.000366 4.57 -1.52 1.52 -0.660 0.660 0.77 list(~log[e](BF[10])==-1.52)
#> 2 Wine A Wine C 3.60 0.00000709 0.277 1.28 -1.28 0.557 -0.557 0.77 list(~log[e](BF[10])==1.28)
#> 3 Wine B Wine C 50.5 0.000000840 0.0198 3.92 -3.92 1.70 -1.70 0.77 list(~log[e](BF[10])==3.92)
#> test.details
#> <chr>
#> 1 Student's t-test
#> 2 Student's t-test
#> 3 Student's t-test
Using pairwiseComparisons
with ggsignif
to display results
Example-1: between-subjects
# needed libraries
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
(df <-
pairwise_comparisons(mtcars, cyl, wt, messages = FALSE) %>%
dplyr::mutate(.data = ., groups = purrr::pmap(.l = list(group1, group2), .f = c)) %>%
dplyr::arrange(.data = ., group1))
#> # A tibble: 3 x 12
#> group1 group2 mean.difference se t.value df p.value significance label test.details
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 4 8 1.71 0.188 6.44 23.0 0 *** list(~italic(p)[ adjusted ]<= 0.001 ) Games-Howell test
#> 2 6 4 -0.831 0.154 3.81 16.0 0.008 ** list(~italic(p)[ adjusted ]== 0.008 ) Games-Howell test
#> 3 6 8 0.882 0.172 3.62 19.0 0.008 ** list(~italic(p)[ adjusted ]== 0.008 ) Games-Howell test
#> p.value.adjustment groups
#> <chr> <list>
#> 1 Holm <chr [2]>
#> 2 Holm <chr [2]>
#> 3 Holm <chr [2]>
# using `geom_signif` to display results
p +
ggsignif::geom_signif(
comparisons = df$groups,
map_signif_level = TRUE,
tip_length = 0.01,
y_position = c(5.5, 5.75, 6),
annotations = df$label,
test = NULL,
na.rm = TRUE,
parse = TRUE
)
Example-2: within-subjects
# 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
(df <-
pairwise_comparisons(WRS2::WineTasting, Wine, Taste, type = "bayes", paired = TRUE) %>%
dplyr::mutate(.data = ., groups = purrr::pmap(.l = list(group1, group2), .f = c)) %>%
dplyr::arrange(.data = ., group1))
#> # A tibble: 3 x 13
#> group1 group2 bf10 error bf01 log_e_bf10 log_e_bf01 log_10_bf10 log_10_bf01 bf.prior label
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 Wine A Wine B 0.235 0.000313 4.25 -1.45 1.45 -0.628 0.628 0.707 list(~log[e](BF[10])==-1.45)
#> 2 Wine A Wine C 3.71 0.0000120 0.269 1.31 -1.31 0.570 -0.570 0.707 list(~log[e](BF[10])==1.31)
#> 3 Wine B Wine C 50.5 0.000000769 0.0198 3.92 -3.92 1.70 -1.70 0.707 list(~log[e](BF[10])==3.92)
#> test.details groups
#> <chr> <list>
#> 1 Student's t-test <chr [2]>
#> 2 Student's t-test <chr [2]>
#> 3 Student's t-test <chr [2]>
# 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).
Contributing
I’m happy to receive bug reports, suggestions, questions, and (most of
all) contributions to fix problems and add features. I personally prefer
using the GitHub
issues system over trying to reach out to me in other
ways (personal e-mail, Twitter, etc.). Pull Requests for contributions
are encouraged.
Here are some simple ways in which you can contribute (in the increasing order of commitment):
Read and correct any inconsistencies in the documentation
Raise issues about bugs or wanted features
Review code
Add new functionality (in the form of new plotting functions or helpers for preparing subtitles)
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.