A combination of box and violin plots along with raw (unjittered) data points for within-subjects designs with statistical details included in the plot as a subtitle.
ggwithinstats(
data,
x,
y,
type = "parametric",
pairwise.display = "significant",
p.adjust.method = "holm",
effsize.type = "unbiased",
bf.prior = 0.707,
bf.message = TRUE,
results.subtitle = TRUE,
xlab = NULL,
ylab = NULL,
caption = NULL,
title = NULL,
subtitle = NULL,
digits = 2L,
conf.level = 0.95,
nboot = 100L,
tr = 0.2,
centrality.plotting = TRUE,
centrality.type = type,
centrality.point.args = list(size = 5, color = "darkred"),
centrality.label.args = list(size = 3, nudge_x = 0.4, segment.linetype = 4),
centrality.path = TRUE,
centrality.path.args = list(linewidth = 1, color = "red", alpha = 0.5),
point.args = list(size = 3, alpha = 0.5, na.rm = TRUE),
point.path = TRUE,
point.path.args = list(alpha = 0.5, linetype = "dashed"),
boxplot.args = list(width = 0.2, alpha = 0.5, na.rm = TRUE),
violin.args = list(width = 0.5, alpha = 0.2, na.rm = TRUE),
ggsignif.args = list(textsize = 3, tip_length = 0.01, na.rm = TRUE),
ggtheme = ggstatsplot::theme_ggstatsplot(),
package = "RColorBrewer",
palette = "Dark2",
ggplot.component = NULL,
...
)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.
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
Decides which pairwise comparisons to display. Available options are:
"significant" (abbreviation accepted: "s")
"non-significant" (abbreviation accepted: "ns")
"all"
You can use this argument to make sure that your plot is not uber-cluttered
when you have multiple groups being compared and scores of pairwise
comparisons being displayed. If set to "none", no pairwise comparisons
will be displayed.
Adjustment method for p-values for multiple
comparisons. Possible methods are: "holm" (default), "hochberg",
"hommel", "bonferroni", "BH", "BY", "fdr", "none".
Type of effect size needed for parametric tests. The
argument can be "eta" (partial eta-squared) or "omega" (partial
omega-squared).
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.
Logical that decides whether to display Bayes Factor in
favor of the null hypothesis. This argument is relevant only for
parametric test (Default: TRUE).
Decides whether the results of statistical tests are
to be displayed as a subtitle (Default: TRUE). If set to FALSE, only
the plot will be returned.
Label for x axis variable. If NULL (default),
variable name for x will be used.
Labels for y axis variable. If NULL (default),
variable name for y will be used.
The text for the plot caption. This argument is relevant only
if bf.message = FALSE.
The text for the plot title.
The text for the plot subtitle. Will work only if
results.subtitle = FALSE.
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()).
Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals
will be computed.
Number of bootstrap samples for computing confidence interval
for the effect size (Default: 100L).
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.
Logical that decides whether centrality tendency
measure is to be displayed as a point with a label (Default: TRUE).
Function decides which central tendency measure to show depending on the
type argument.
mean for parametric statistics
median for non-parametric statistics
trimmed mean for robust statistics
MAP estimator for Bayesian statistics
If you want default centrality parameter, you can specify this using
centrality.type argument.
Decides which centrality parameter is to be displayed.
The default is to choose the same as type argument. You can specify this
to be:
"parameteric" (for mean)
"nonparametric" (for median)
robust (for trimmed mean)
bayes (for MAP estimator)
Just as type argument, abbreviations are also accepted.
A list of additional aesthetic
arguments to be passed to ggplot2::geom_point() and
ggrepel::geom_label_repel geoms, which are involved in mean plotting.
A list of additional aesthetic
arguments passed on to ggplot2::geom_path() connecting raw data points
and mean points.
A list of additional aesthetic arguments to be passed to
the ggplot2::geom_point() displaying the raw data.
Logical that decides whether individual
data points and means, respectively, should be connected using
ggplot2::geom_path(). Both default to TRUE. Note that point.path
argument is relevant only when there are two groups (i.e., in case of a
t-test). In case of large number of data points, it is advisable to set
point.path = FALSE as these lines can overwhelm the plot.
A list of additional aesthetic arguments passed on to
ggplot2::geom_boxplot().
A list of additional aesthetic arguments to be passed to
the ggplot2::geom_violin().
A list of additional aesthetic
arguments to be passed to ggsignif::geom_signif.
A {ggplot2} theme. Default value is
ggstatsplot::theme_ggstatsplot(). Any of the {ggplot2} themes (e.g.,
theme_bw()), or themes from extension packages are allowed (e.g.,
ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.).
But note that sometimes these themes will remove some of the details that
{ggstatsplot} plots typically contains. For example, if relevant,
ggbetweenstats() shows details about multiple comparison test as a label
on the secondary Y-axis. Some themes (e.g.
ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and
thus the details as well.
Name of the package from which the given palette is to
be extracted. The available palettes and packages can be checked by running
View(paletteer::palettes_d_names).
A ggplot component to be added to the plot prepared
by {ggstatsplot}. This argument is primarily helpful for grouped_
variants of all primary functions. Default is NULL. The argument should
be entered as a {ggplot2} function or a list of {ggplot2} functions.
Currently ignored.
| graphical element | geom used | argument for further modification |
| raw data | ggplot2::geom_point() | point.args |
| point path | ggplot2::geom_path() | point.path.args |
| box plot | ggplot2::geom_boxplot() | boxplot.args |
| density plot | ggplot2::geom_violin() | violin.args |
| centrality measure point | ggplot2::geom_point() | centrality.point.args |
| centrality measure point path | ggplot2::geom_path() | centrality.path.args |
| centrality measure label | ggrepel::geom_label_repel() | centrality.label.args |
| pairwise comparisons | ggsignif::geom_signif() | ggsignif.args |
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
| Type | Measure | Function used |
| Parametric | mean | datawizard::describe_distribution() |
| Non-parametric | median | datawizard::describe_distribution() |
| Robust | trimmed mean | datawizard::describe_distribution() |
| Bayesian | MAP | datawizard::describe_distribution() |
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 | No. of groups | Test | Function used |
| Parametric | 2 | Student's or Welch's t-test | stats::t.test() |
| Non-parametric | 2 | Mann-Whitney U test | stats::wilcox.test() |
| Robust | 2 | Yuen's test for trimmed means | WRS2::yuen() |
| Bayesian | 2 | Student's t-test | BayesFactor::ttestBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | 2 | Cohen's d, Hedge's g | Yes | effectsize::cohens_d(), effectsize::hedges_g() |
| Non-parametric | 2 | r (rank-biserial correlation) | Yes | effectsize::rank_biserial() |
| Robust | 2 | Algina-Keselman-Penfield robust standardized difference | Yes | WRS2::akp.effect() |
| Bayesian | 2 | difference | Yes | bayestestR::describe_posterior() |
Hypothesis testing
| Type | No. of groups | Test | Function used |
| Parametric | 2 | Student's t-test | stats::t.test() |
| Non-parametric | 2 | Wilcoxon signed-rank test | stats::wilcox.test() |
| Robust | 2 | Yuen's test on trimmed means for dependent samples | WRS2::yuend() |
| Bayesian | 2 | Student's t-test | BayesFactor::ttestBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | 2 | Cohen's d, Hedge's g | Yes | effectsize::cohens_d(), effectsize::hedges_g() |
| Non-parametric | 2 | r (rank-biserial correlation) | Yes | effectsize::rank_biserial() |
| Robust | 2 | Algina-Keselman-Penfield robust standardized difference | Yes | WRS2::wmcpAKP() |
| Bayesian | 2 | difference | Yes | bayestestR::describe_posterior() |
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 | No. of groups | Test | Function used |
| Parametric | > 2 | Fisher's or Welch's one-way ANOVA | stats::oneway.test() |
| Non-parametric | > 2 | Kruskal-Wallis one-way ANOVA | stats::kruskal.test() |
| Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means | WRS2::t1way() |
| Bayes Factor | > 2 | Fisher's ANOVA | BayesFactor::anovaBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | > 2 | partial eta-squared, partial omega-squared | Yes | effectsize::omega_squared(), effectsize::eta_squared() |
| Non-parametric | > 2 | rank epsilon squared | Yes | effectsize::rank_epsilon_squared() |
| Robust | > 2 | Explanatory measure of effect size | Yes | WRS2::t1way() |
| Bayes Factor | > 2 | Bayesian R-squared | Yes | performance::r2_bayes() |
Hypothesis testing
| Type | No. of groups | Test | Function used |
| Parametric | > 2 | One-way repeated measures ANOVA | afex::aov_ez() |
| Non-parametric | > 2 | Friedman rank sum test | stats::friedman.test() |
| Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means | WRS2::rmanova() |
| Bayes Factor | > 2 | One-way repeated measures ANOVA | BayesFactor::anovaBF() |
Effect size estimation
| Type | No. of groups | Effect size | CI available? | Function used |
| Parametric | > 2 | partial eta-squared, partial omega-squared | Yes | effectsize::omega_squared(), effectsize::eta_squared() |
| Non-parametric | > 2 | Kendall's coefficient of concordance | Yes | effectsize::kendalls_w() |
| Robust | > 2 | Algina-Keselman-Penfield robust standardized difference average | Yes | WRS2::wmcpAKP() |
| Bayes Factor | > 2 | Bayesian R-squared | Yes | performance::r2_bayes() |
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.
For details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html
grouped_ggbetweenstats, ggbetweenstats,
grouped_ggwithinstats