pairwise_p
Pairwise comparison tests
Calculate pairwise comparisons between group levels with corrections for multiple testing.
Usage
pairwise_p(data, x, y, type = "parametric", tr = 0.1, paired = FALSE,
var.equal = FALSE, p.adjust.method = "holm", k = 2,
messages = TRUE, ...)Arguments
- data
A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.
- x
The grouping variable from the dataframe
data.- y
The response (a.k.a. outcome or dependent) variable from the dataframe
data.- type
Type of statistic expected (
"parametric"or"nonparametric"or"robust"or"bayes").Corresponding abbreviations are also accepted:"p"(for parametric),"np"(nonparametric),"r"(robust), or"bf"resp.- tr
Trim level for the mean when carrying out
robusttests. If you get error stating "Standard error cannot be computed because of Winsorized variance of 0 (e.g., due to ties). Try to decrease the trimming level.", try to play around with the value oftr, which is by default set to0.1. Lowering the value might help.- paired
a logical indicating whether you want a paired t-test.
- var.equal
a logical variable indicating whether to treat the variances in the samples as equal. If
TRUE, then a simple F test for the equality of means in a one-way analysis of variance is performed. IfFALSE, an approximate method of Welch (1951) is used, which generalizes the commonly known 2-sample Welch test to the case of arbitrarily many samples.- p.adjust.method
Adjustment method for p-values for multiple comparisons. Possible methods are:
"holm"(default),"hochberg","hommel","bonferroni","BH","BY","fdr","none".- k
Number of digits after decimal point (should be an integer) (Default:
k = 2).- messages
Decides whether messages references, notes, and warnings are to be displayed (Default:
TRUE).- ...
Additional arguments.
See Also
ggbetweenstats, grouped_ggbetweenstats
Other helper_messages: bartlett_message,
effsize_ci_message,
ggcorrmat_matrix_message,
grouped_message,
normality_message,
palette_message
Examples
# NOT RUN {
# time consuming, so not run on `CRAN` machines
# }
# NOT RUN {
# show all columns in a tibble
options(tibble.width = Inf)
# for reproducibility
set.seed(123)
#------------------- between-subjects design ----------------------------
# parametric
# if `var.equal = TRUE`, then Student's *t*-test will be run
ggstatsplot::pairwise_p(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "p",
var.equal = TRUE,
paired = FALSE,
p.adjust.method = "bonferroni"
)
# if `var.equal = FALSE`, then Games-Howell test will be run
ggstatsplot::pairwise_p(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "p",
var.equal = FALSE,
paired = FALSE,
p.adjust.method = "bonferroni"
)
# non-parametric
ggstatsplot::pairwise_p(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "np",
paired = FALSE,
p.adjust.method = "none"
)
# robust
ggstatsplot::pairwise_p(
data = ggplot2::msleep,
x = vore,
y = brainwt,
type = "r",
paired = FALSE,
p.adjust.method = "fdr"
)
# }
# NOT RUN {
#------------------- within-subjects design ----------------------------
set.seed(123)
library(jmv)
data("bugs", package = "jmv")
# converting to long format
bugs_long <- bugs %>%
tibble::as_tibble(.) %>%
tidyr::gather(., key, value, LDLF:HDHF)
# parametric
ggstatsplot::pairwise_p(
data = bugs_long,
x = key,
y = value,
type = "p",
paired = TRUE,
p.adjust.method = "BH"
)
# non-parametric
ggstatsplot::pairwise_p(
data = bugs_long,
x = key,
y = value,
type = "np",
paired = TRUE,
p.adjust.method = "BY"
)
# robust
ggstatsplot::pairwise_p(
data = bugs_long,
x = key,
y = value,
type = "r",
paired = TRUE,
p.adjust.method = "hommel"
)
# }