Description Usage Arguments Value References Examples
View source: R/pairwise_comparisons.R
Calculate parametric, nonparametric, robust, and Bayes Factor pairwise comparisons between group levels with corrections for multiple testing.
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data 
A dataframe (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. 
x 
The grouping (or independent) variable from the dataframe 
y 
The response (or outcome or dependent) variable from the
dataframe 
subject.id 
Relevant in case of a repeated measures or withinsubjects
design ( 
type 
A character specifying the type of statistical approach:
You can specify just the initial letter. 
paired 
Logical that decides whether the experimental design is
repeated measures/withinsubjects or betweensubjects. The default is

var.equal 
a logical variable indicating whether to treat the
two variances as being equal. If 
tr 
Trim level for the mean when carrying out 
bf.prior 
A number between 
p.adjust.method 
Adjustment method for pvalues for multiple
comparisons. Possible methods are: 
k 
Number of digits after decimal point (should be an integer)
(Default: 
... 
Additional arguments passed to other methods. 
A tibble dataframe containing two columns corresponding to group
levels being compared with each other (group1
and group2
) and p.value
column corresponding to this comparison. The dataframe will also contain a
p.value.label
column containing a label for this pvalue, in case
this needs to be displayed in ggsignif::geom_ggsignif
. In addition to
these common columns across the different types of statistics, there will
be additional columns specific to the type
of test being run.
This function provides a unified syntax to carry out pairwise comparison tests and internally relies on other packages to carry out these tests. For more details about the included tests, see the documentation for the respective functions:
parametric : stats::pairwise.t.test()
(paired) and
PMCMRplus::gamesHowellTest()
(unpaired)
nonparametric :
PMCMRplus::durbinAllPairsTest()
(paired) and
PMCMRplus::kwAllPairsDunnTest()
(unpaired)
robust :
WRS2::rmmcp()
(paired) and WRS2::lincon()
(unpaired)
Bayes Factor : BayesFactor::ttestBF()
For more, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/pairwise.html
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# for reproducibility
set.seed(123)
library(ggstatsplot)
library(statsExpressions) # for data
# show all columns and make the column titles bold
# as a user, you don't need to do this; this is just for the package website
options(tibble.width = Inf, pillar.bold = TRUE, pillar.neg = TRUE, pillar.subtle_num = TRUE)
# betweensubjects design 
# parametric
# if `var.equal = TRUE`, then Student's ttest will be run
pairwise_comparisons(
data = mtcars,
x = cyl,
y = wt,
type = "parametric",
var.equal = TRUE,
paired = FALSE,
p.adjust.method = "none"
)
# if `var.equal = FALSE`, then GamesHowell test will be run
pairwise_comparisons(
data = mtcars,
x = cyl,
y = wt,
type = "parametric",
var.equal = FALSE,
paired = FALSE,
p.adjust.method = "bonferroni"
)
# nonparametric (Dunn test)
pairwise_comparisons(
data = mtcars,
x = cyl,
y = wt,
type = "nonparametric",
paired = FALSE,
p.adjust.method = "none"
)
# robust (Yuen's trimmed means *t*test)
pairwise_comparisons(
data = mtcars,
x = cyl,
y = wt,
type = "robust",
paired = FALSE,
p.adjust.method = "fdr"
)
# Bayes Factor (Student's *t*test)
pairwise_comparisons(
data = mtcars,
x = cyl,
y = wt,
type = "bayes",
paired = FALSE
)
# withinsubjects design 
# parametric (Student's *t*test)
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "parametric",
paired = TRUE,
p.adjust.method = "BH"
)
# nonparametric (DurbinConover test)
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "nonparametric",
paired = TRUE,
p.adjust.method = "BY"
)
# robust (Yuen's trimmed means ttest)
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "robust",
paired = TRUE,
p.adjust.method = "hommel"
)
# Bayes Factor (Student's *t*test)
pairwise_comparisons(
data = bugs_long,
x = condition,
y = desire,
subject.id = subject,
type = "bayes",
paired = TRUE
)
}

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