grouped_ggwithinstats.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/grouped_ggwithinstats.R
\name{grouped_ggwithinstats}
\alias{grouped_ggwithinstats}
\title{Violin plots for group or condition comparisons in within-subjects
designs repeated across all levels of a grouping variable.}
\usage{
grouped_ggwithinstats(
  data,
  x,
  y,
  grouping.var,
  outlier.label = NULL,
  output = "plot",
  plotgrid.args = list(),
  annotation.args = list(),
  ...
)
}
\arguments{
\item{data}{A dataframe (or a tibble) from which variables specified are to
be taken. Other data types (e.g., matrix,table, array, etc.) will \strong{not}
be accepted.}

\item{x}{The grouping (or independent) variable from the dataframe \code{data}. In
case of a repeated measures or within-subjects design, if \code{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 \strong{not} sorted, the results
\emph{can} be inaccurate when there are more than two levels in \code{x} and there
are \code{NA}s present. The data is expected to be sorted by user in
subject-1,subject-2, ..., pattern.}

\item{y}{The response (or outcome or dependent) variable from the
dataframe \code{data}.}

\item{grouping.var}{A single grouping variable (can be entered either as a
bare name \code{x} or as a string \code{"x"}).}

\item{outlier.label}{Label to put on the outliers that have been tagged. This
\strong{can't} be the same as \code{x} argument.}

\item{output}{Character that describes what is to be returned: can be
\code{"plot"} (default) or \code{"subtitle"} or \code{"caption"}. Setting this to
\code{"subtitle"} will return the expression containing statistical results. If
you have set \code{results.subtitle = FALSE}, then this will return a \code{NULL}.
Setting this to \code{"caption"} will return the expression containing details
about Bayes Factor analysis, but valid only when \code{type = "parametric"} and
\code{bf.message = TRUE}, otherwise this will return a \code{NULL}.}

\item{plotgrid.args}{A \code{list} of additional arguments passed to
\code{patchwork::wrap_plots}, except for \code{guides} argument which is already
separately specified here.}

\item{annotation.args}{A \code{list} of additional arguments passed to
\code{patchwork::plot_annotation}.}

\item{...}{
  Arguments passed on to \code{\link[=ggwithinstats]{ggwithinstats}}
  \describe{
    \item{\code{point.path}}{Logical that decides whether individual data
points and means, respectively, should be connected using \code{geom_path}. Both
default to \code{TRUE}. Note that \code{point.path} argument is relevant only when
there are two groups (i.e., in case of a \emph{t}-test). In case of large number
of data points, it is advisable to set \code{point.path = FALSE} as these lines
can overwhelm the plot.}
    \item{\code{centrality.path}}{Logical that decides whether individual data
points and means, respectively, should be connected using \code{geom_path}. Both
default to \code{TRUE}. Note that \code{point.path} argument is relevant only when
there are two groups (i.e., in case of a \emph{t}-test). In case of large number
of data points, it is advisable to set \code{point.path = FALSE} as these lines
can overwhelm the plot.}
    \item{\code{centrality.path.args}}{A list of additional aesthetic
arguments passed on to \code{geom_path} connecting raw data points and mean
points.}
    \item{\code{point.path.args}}{A list of additional aesthetic
arguments passed on to \code{geom_path} connecting raw data points and mean
points.}
    \item{\code{type}}{A character specifying the type of statistical approach:
\itemize{
\item \code{"parametric"}
\item \code{"nonparametric"}
\item \code{"robust"}
\item \code{"bayes"}
}

You can specify just the initial letter.}
    \item{\code{pairwise.comparisons}}{Logical that decides whether pairwise comparisons
are to be displayed (default: \code{TRUE}). Please note that only
\strong{significant} comparisons will be shown by default. To change this
behavior, select appropriate option with \code{pairwise.display} argument. The
pairwise comparison dataframes are prepared using the
\code{pairwiseComparisons::pairwise_comparisons} function. For more details
about pairwise comparisons, see the documentation for that function.}
    \item{\code{pairwise.display}}{Decides \emph{which} pairwise comparisons to display.
Available options are:
\itemize{
\item \code{"significant"} (abbreviation accepted: \code{"s"})
\item \code{"non-significant"} (abbreviation accepted: \code{"ns"})
\item \code{"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.}
    \item{\code{p.adjust.method}}{Adjustment method for \emph{p}-values for multiple
comparisons. Possible methods are: \code{"holm"} (default), \code{"hochberg"},
\code{"hommel"}, \code{"bonferroni"}, \code{"BH"}, \code{"BY"}, \code{"fdr"}, \code{"none"}.}
    \item{\code{effsize.type}}{Type of effect size needed for \emph{parametric} tests. The
argument can be \code{"eta"} (partial eta-squared) or \code{"omega"} (partial
omega-squared).}
    \item{\code{bf.prior}}{A number between \code{0.5} and \code{2} (default \code{0.707}), the prior
width to use in calculating Bayes factors.}
    \item{\code{bf.message}}{Logical that decides whether to display Bayes Factor in
favor of the \emph{null} hypothesis. This argument is relevant only \strong{for
parametric test} (Default: \code{TRUE}).}
    \item{\code{results.subtitle}}{Decides whether the results of statistical tests are
to be displayed as a subtitle (Default: \code{TRUE}). If set to \code{FALSE}, only
the plot will be returned.}
    \item{\code{xlab}}{Labels for \code{x} and \code{y} axis variables. If \code{NULL} (default),
variable names for \code{x} and \code{y} will be used.}
    \item{\code{ylab}}{Labels for \code{x} and \code{y} axis variables. If \code{NULL} (default),
variable names for \code{x} and \code{y} will be used.}
    \item{\code{caption}}{The text for the plot caption.}
    \item{\code{subtitle}}{The text for the plot subtitle. Will work only if
\code{results.subtitle = FALSE}.}
    \item{\code{k}}{Number of digits after decimal point (should be an integer)
(Default: \code{k = 2L}).}
    \item{\code{conf.level}}{Scalar between \code{0} and \code{1}. If unspecified, the defaults
return \verb{95\%} confidence/credible intervals (\code{0.95}).}
    \item{\code{nboot}}{Number of bootstrap samples for computing confidence interval
for the effect size (Default: \code{100L}).}
    \item{\code{tr}}{Trim level for the mean when carrying out \code{robust} tests. In case
of an error, try reducing the value of \code{tr}, which is by default set to
\code{0.2}. Lowering the value might help.}
    \item{\code{centrality.plotting}}{Logical that decides whether centrality tendency
measure is to be displayed as a point with a label (Default: \code{TRUE}).
Function decides which central tendency measure to show depending on the
\code{type} argument.
\itemize{
\item \strong{mean} for parametric statistics
\item \strong{median} for non-parametric statistics
\item \strong{trimmed mean} for robust statistics
\item \strong{MAP estimator} for Bayesian statistics
}

If you want default centrality parameter, you can specify this using
\code{centrality.type} argument.}
    \item{\code{centrality.type}}{Decides which centrality parameter is to be displayed.
The default is to choose the same as \code{type} argument. You can specify this
to be:
\itemize{
\item \code{"parameteric"} (for \strong{mean})
\item \code{"nonparametric"} (for \strong{median})
\item \code{robust} (for \strong{trimmed mean})
\item \code{bayes} (for \strong{MAP estimator})
}

Just as \code{type} argument, abbreviations are also accepted.}
    \item{\code{centrality.point.args}}{A list of additional aesthetic
arguments to be passed to \code{ggplot2::geom_point} and
\code{ggrepel::geom_label_repel} geoms, which are involved in mean plotting.}
    \item{\code{centrality.label.args}}{A list of additional aesthetic
arguments to be passed to \code{ggplot2::geom_point} and
\code{ggrepel::geom_label_repel} geoms, which are involved in mean plotting.}
    \item{\code{point.args}}{A list of additional aesthetic arguments to be passed to
the \code{geom_point} displaying the raw data.}
    \item{\code{outlier.tagging}}{Decides whether outliers should be tagged (Default:
\code{FALSE}).}
    \item{\code{outlier.coef}}{Coefficient for outlier detection using Tukey's method.
With Tukey's method, outliers are below (1st Quartile) or above (3rd
Quartile) \code{outlier.coef} times the Inter-Quartile Range (IQR) (Default:
\code{1.5}).}
    \item{\code{outlier.label.args}}{A list of additional aesthetic arguments to be
passed to \code{ggrepel::geom_label_repel} for outlier label plotting.}
    \item{\code{violin.args}}{A list of additional aesthetic arguments to be passed to
the \code{geom_violin}.}
    \item{\code{ggsignif.args}}{A list of additional aesthetic
arguments to be passed to \code{ggsignif::geom_signif}.}
    \item{\code{ggtheme}}{A \code{ggplot2} theme. Default value is
\code{ggstatsplot::theme_ggstatsplot()}. Any of the \code{ggplot2} themes (e.g.,
\code{ggplot2::theme_bw()}), or themes from extension packages are allowed
(e.g., \code{ggthemes::theme_fivethirtyeight()}, \code{hrbrthemes::theme_ipsum_ps()},
etc.).}
    \item{\code{package}}{Name of the package from which the given palette is to
be extracted. The available palettes and packages can be checked by running
\code{View(paletteer::palettes_d_names)}.}
    \item{\code{palette}}{Name of the package from which the given palette is to
be extracted. The available palettes and packages can be checked by running
\code{View(paletteer::palettes_d_names)}.}
    \item{\code{ggplot.component}}{A \code{ggplot} component to be added to the plot prepared
by \code{ggstatsplot}. This argument is primarily helpful for \code{grouped_}
variants of all primary functions. Default is \code{NULL}. The argument should
be entered as a \code{ggplot2} function or a list of \code{ggplot2} functions.}
  }}
}
\description{
A combined plot of comparison plot created for levels of a
grouping variable.
}
\examples{
\donttest{
# to get reproducible results from bootstrapping
set.seed(123)
library(ggstatsplot)

# the most basic function call
grouped_ggwithinstats(
  data = VR_dilemma,
  x = modality,
  y = score,
  grouping.var = order,
  type = "np", # non-parametric test
  # additional modifications for **each** plot using `ggplot2` functions
  ggplot.component = ggplot2::scale_y_continuous(
    breaks = seq(0, 1, 0.1),
    limits = c(0, 1)
  )
)
}
}
\seealso{
\code{\link{ggwithinstats}}, \code{\link{ggbetweenstats}},
\code{\link{grouped_ggbetweenstats}}
}