A combination of box and violin plots along with jittered data points for between-subjects designs with statistical details included in the plot as a subtitle.
ggbetweenstats(
data,
x,
y,
plot.type = "boxviolin",
type = "parametric",
pairwise.comparisons = TRUE,
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,
k = 2L,
var.equal = FALSE,
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,
min.segment.length = 0),
outlier.tagging = FALSE,
outlier.label = NULL,
outlier.coef = 1.5,
outlier.shape = 19,
outlier.color = "black",
outlier.label.args = list(size = 3),
point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha
= 0.4, size = 3, stroke = 0),
violin.args = list(width = 0.5, alpha = 0.2),
ggsignif.args = list(textsize = 3, tip_length = 0.01),
ggtheme = ggstatsplot::theme_ggstatsplot(),
package = "RColorBrewer",
palette = "Dark2",
ggplot.component = NULL,
output = "plot",
...
)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.
The grouping (or independent) variable from the dataframe 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 the
dataframe data.
Character describing the type of plot. Currently supported
plots are "box" (for only boxplots), "violin" (for only violin plots),
and "boxviolin" (for a combination of box and violin plots; default).
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
Logical that decides whether pairwise comparisons
are to be displayed (default: TRUE). Please note that only
significant comparisons will be shown by default. To change this
behavior, select appropriate option with pairwise.display argument. The
pairwise comparison dataframes are prepared using the
pairwiseComparisons::pairwise_comparisons function. For more details
about pairwise comparisons, see the documentation for that function.
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.
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.
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.
Labels for x and y axis variables. If NULL (default),
variable names for x and y will be used.
The text for the plot caption.
The text for the plot title.
The text for the plot subtitle. Will work only if
results.subtitle = FALSE.
Number of digits after decimal point (should be an integer)
(Default: k = 2L).
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.
Scalar between 0 and 1. If unspecified, the defaults
return 95% confidence/credible intervals (0.95).
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.
Decides whether outliers should be tagged (Default:
FALSE).
Label to put on the outliers that have been tagged. This
can't be the same as x argument.
Coefficient for outlier detection using Tukey's method.
With Tukey's method, outliers are below (1st Quartile) or above (3rd
Quartile) outlier.coef times the Inter-Quartile Range (IQR) (Default:
1.5).
Hiding the outliers can be achieved by setting
outlier.shape = NA. Importantly, this does not remove the outliers,
it only hides them, so the range calculated for the y-axis will be
the same with outliers shown and outliers hidden.
Default aesthetics for outliers (Default: "black").
A list of additional aesthetic arguments to be
passed to ggrepel::geom_label_repel for outlier label plotting.
A list of additional aesthetic arguments to be passed to
the geom_point displaying the raw data.
A list of additional aesthetic arguments to be passed to
the 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.,
ggplot2::theme_bw()), or themes from extension packages are allowed
(e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(),
etc.).
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.
Character that describes what is to be returned: can be
"plot" (default) or "subtitle" or "caption". Setting this to
"subtitle" will return the expression containing statistical results. If
you have set results.subtitle = FALSE, then this will return a NULL.
Setting this to "caption" will return the expression containing details
about Bayes Factor analysis, but valid only when type = "parametric" and
bf.message = TRUE, otherwise this will return a NULL.
Currently ignored.
For more details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html
grouped_ggbetweenstats, ggwithinstats,
grouped_ggwithinstats
# NOT RUN {
# to get reproducible results from bootstrapping
set.seed(123)
library(ggstatsplot)
# simple function call with the defaults
ggbetweenstats(mtcars, am, mpg)
# more detailed function call
ggbetweenstats(
data = morley,
x = Expt,
y = Speed,
type = "robust",
plot.type = "box",
xlab = "The experiment number",
ylab = "Speed-of-light measurement",
pairwise.comparisons = TRUE,
p.adjust.method = "fdr",
outlier.tagging = TRUE,
outlier.label = Run
)
# }
Run the code above in your browser using DataLab