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Calculate parametric, non-parametric, robust, and Bayes Factor pairwise comparisons between group levels with corrections for multiple testing.
pairwise_comparisons(
data,
x,
y,
subject.id = NULL,
type = "parametric",
paired = FALSE,
var.equal = FALSE,
tr = 0.2,
bf.prior = 0.707,
p.adjust.method = "holm",
digits = 2L,
...
)The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):
statistic: the numeric value of a statistic
df: the numeric value of a parameter being modeled (often degrees
of freedom for the test)
df.error and df: relevant only if the statistic in question has
two degrees of freedom (e.g. anova)
p.value: the two-sided p-value associated with the observed statistic
method: the name of the inferential statistical test
estimate: estimated value of the effect size
conf.low: lower bound for the effect size estimate
conf.high: upper bound for the effect size estimate
conf.level: width of the confidence interval
conf.method: method used to compute confidence interval
conf.distribution: statistical distribution for the effect
effectsize: the name of the effect size
n.obs: number of observations
expression: pre-formatted expression containing statistical details
For examples, see data frame output vignette.
A data frame (or a tibble) from which variables specified are to
be taken. Other data types (e.g., matrix,table, array, etc.) will not
be accepted. Additionally, grouped data frames from {dplyr} should be
ungrouped before they are entered as data.
The grouping (or independent) variable from data. In case of a
repeated measures or within-subjects design, if subject.id argument is
not available or not explicitly specified, the function assumes that the
data has already been sorted by such an id by the user and creates an
internal identifier. So if your data is not sorted, the results can
be inaccurate when there are more than two levels in x and there are
NAs present. The data is expected to be sorted by user in
subject-1, subject-2, ..., pattern.
The response (or outcome or dependent) variable from data.
Relevant in case of a repeated measures or within-subjects
design (paired = TRUE, i.e.), it specifies the subject or repeated
measures identifier. Important: Note that if this argument is NULL
(which is the default), the function assumes that the data has already been
sorted by such an id by the user and creates an internal identifier. So if
your data is not sorted and you leave this argument unspecified, the
results can be inaccurate when there are more than two levels in x and
there are NAs present.
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
Logical that decides whether the experimental design is
repeated measures/within-subjects or between-subjects. The default is
FALSE.
a logical variable indicating whether to treat the
two variances as being equal. If TRUE then the pooled
variance is used to estimate the variance otherwise the Welch
(or Satterthwaite) approximation to the degrees of freedom is used.
Trim level for the mean when carrying out robust tests. In case
of an error, try reducing the value of tr, which is by default set to
0.2. Lowering the value might help.
A number between 0.5 and 2 (default 0.707), the prior
width to use in calculating Bayes factors and posterior estimates. In
addition to numeric arguments, several named values are also recognized:
"medium", "wide", and "ultrawide", corresponding to r scale values
of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this
value corresponds to scale for fixed effects.
Adjustment method for p-values for multiple
comparisons. Possible methods are: "holm" (default), "hochberg",
"hommel", "bonferroni", "BH", "BY", "fdr", "none".
Number of digits for rounding or significant figures. May also
be "signif" to return significant figures or "scientific"
to return scientific notation. Control the number of digits by adding the
value as suffix, e.g. digits = "scientific4" to have scientific
notation with 4 decimal places, or digits = "signif5" for 5
significant figures (see also signif()).
Additional arguments passed to other methods.
The table below provides summary about:
statistical test carried out for inferential statistics
type of effect size estimate and a measure of uncertainty for this estimate
functions used internally to compute these details
Hypothesis testing
| Type | Equal variance? | Test | p-value adjustment? | Function used |
| Parametric | No | Games-Howell test | Yes | PMCMRplus::gamesHowellTest() |
| Parametric | Yes | Student's t-test | Yes | stats::pairwise.t.test() |
| Non-parametric | No | Dunn test | Yes | PMCMRplus::kwAllPairsDunnTest() |
| Robust | No | Yuen's trimmed means test | Yes | WRS2::lincon() |
| Bayesian | NA | Student's t-test | NA | BayesFactor::ttestBF() |
Effect size estimation
Not supported.
Hypothesis testing
| Type | Test | p-value adjustment? | Function used |
| Parametric | Student's t-test | Yes | stats::pairwise.t.test() |
| Non-parametric | Durbin-Conover test | Yes | PMCMRplus::durbinAllPairsTest() |
| Robust | Yuen's trimmed means test | Yes | WRS2::rmmcp() |
| Bayesian | Student's t-test | NA | BayesFactor::ttestBF() |
Effect size estimation
Not supported.
Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details. Journal of Open Source Software, 6(61), 3236, https://doi.org/10.21105/joss.03236
For more, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/pairwise.html
if (FALSE) { # identical(Sys.getenv("NOT_CRAN"), "true")
# for reproducibility
set.seed(123)
library(statsExpressions)
#------------------- between-subjects design ----------------------------
# parametric
# if `var.equal = TRUE`, then Student's t-test 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 Games-Howell test will be run
pairwise_comparisons(
data = mtcars,
x = cyl,
y = wt,
type = "parametric",
var.equal = FALSE,
paired = FALSE,
p.adjust.method = "bonferroni"
)
# non-parametric (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
)
#------------------- within-subjects 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"
)
# non-parametric (Durbin-Conover 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 t-test)
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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