% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat_compare_means.R
\name{stat_compare_means}
\alias{stat_compare_means}
\title{Add Mean Comparison P-values to a ggplot}
\usage{
stat_compare_means(
  mapping = NULL,
  data = NULL,
  method = NULL,
  paired = FALSE,
  id = NULL,
  method.args = list(),
  ref.group = NULL,
  comparisons = NULL,
  hide.ns = FALSE,
  label.sep = ", ",
  label = NULL,
  label.x.npc = "left",
  label.y.npc = "top",
  label.x = NULL,
  label.y = NULL,
  vjust = 0,
  tip.length = 0.03,
  bracket.size = 0.3,
  step.increase = 0,
  symnum.args = list(),
  p.format.style = "default",
  p.digits = NULL,
  p.leading.zero = NULL,
  p.min.threshold = NULL,
  p.decimal.mark = NULL,
  signif.cutoffs = NULL,
  signif.symbols = NULL,
  ns.symbol = "ns",
  use.four.stars = FALSE,
  show.signif = TRUE,
  geom = "text",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)
}
\arguments{
\item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and
\code{inherit.aes = TRUE} (the default), it is combined with the default mapping
at the top level of the plot. You must supply \code{mapping} if there is no plot
mapping.}

\item{data}{The data to be displayed in this layer. There are three
options:

If \code{NULL}, the default, the data is inherited from the plot
data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}.

A \code{data.frame}, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
\code{\link[ggplot2:fortify]{fortify()}} for which variables will be created.

A \code{function} will be called with a single argument,
the plot data. The return value must be a \code{data.frame}, and
will be used as the layer data. A \code{function} can be created
from a \code{formula} (e.g. \code{~ head(.x, 10)}).}

\item{method}{a character string indicating which method to be used for
comparing means.}

\item{paired}{a logical indicating whether you want a paired test. Used only
in \code{\link[stats]{t.test}} and in \link[stats]{wilcox.test}.}

\item{id}{optional character string naming a column that identifies matched
subjects for a \strong{paired} comparison (\code{paired = TRUE}, with
\code{method = "t.test"} or \code{"wilcox.test"}, and without
\code{comparisons}). Without it the paired test pairs observations by row
order, so the p-value is wrong when the data are not sorted so that the
compared groups align by subject. Providing \code{id} pairs the observations
by subject id instead (row-order independent); see \code{\link{compare_means}}.}

\item{method.args}{a list of additional arguments used for the test method.
For example one might use \code{method.args = list(alternative = "greater")}
for wilcoxon test.}

\item{ref.group}{a character string specifying the reference group. If
 specified, for a given grouping variable, each of the group levels will be
 compared to the reference group (i.e. control group).

 \code{ref.group} can be also \code{".all."}. In this case, each of the
 grouping variable levels is compared to all (i.e. basemean).}

\item{comparisons}{A list of length-2 vectors. The entries in the vector are
 either the names of 2 values on the x-axis or the 2 integers that correspond
 to the index of the groups of interest, to be compared.

 Note: when \code{comparisons} is specified, each pairwise test is computed
 independently and the displayed p-values are \strong{not} adjusted for
 multiple comparisons. For p-values corrected for multiple testing, use
 \code{\link{geom_pwc}()}, or \code{\link{stat_pvalue_manual}()} together with
 \code{\link{compare_means}(..., p.adjust.method = )}.}

\item{hide.ns}{logical value. If TRUE, hide ns symbol when displaying
significance levels.}

\item{label.sep}{a character string to separate the terms. Default is ", ", to
separate the test method name and the p-value.}

\item{label}{character string specifying label type. Allowed values include
\code{"p.signif"} (shows the significance levels), \code{"p.format"} (shows
the formatted p-value), and \code{"p.format.signif"} (shows the formatted
p-value followed by significance stars, e.g., "p = 0.01 **").}

\item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character}
vector of the same length as the number of groups and/or panels. If too
short they will be recycled. \itemize{ \item If \code{numeric}, value should
be between 0 and 1. Coordinates to be used for positioning the label,
expressed in "normalized parent coordinates". \item If \code{character},
allowed values include: i) one of c('right', 'left', 'center', 'centre',
'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre',
'middle') for y-axis.}}

\item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used
for absolute positioning of the label. If too short they will be recycled.}

\item{vjust}{move the text up or down relative to the bracket.}

\item{tip.length}{numeric vector with the fraction that the bracket tips go
 down to indicate the precise column. Default is 0.03. Can be of
 same length as the number of comparisons to adjust specifically the tip
 length of each comparison. For example tip.length = c(0.01, 0.03).

 If too short they will be recycled.

 Note: when \code{comparisons} is set, brackets are drawn via
 \code{ggsignif::geom_signif()} and \code{tip.length} is a fraction of the
 trained data range, so the absolute tip length scales with your data (plots
 with different scales get different tip lengths). To obtain visually constant
 tip lengths across plots, compute the tests with \code{\link{compare_means}()}
 and draw them with \code{\link{stat_pvalue_manual}(..., tip.length.ref = "axis")}
 (which makes \code{tip.length} a fraction of the y-axis range). Passing
 \code{tip.length.ref} directly to \code{stat_compare_means()} has no effect
 and emits an informative message.}

\item{bracket.size}{Width of the lines of the bracket.}

\item{step.increase}{numeric vector with the increase in fraction of total
height for every additional comparison to minimize overlap.}

\item{symnum.args}{a list of arguments to pass to the function
 \code{\link[stats]{symnum}} for symbolic number coding of p-values. For
 example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001,
 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*",  "ns"))}.

 In other words, we use the following convention for symbols indicating
 statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item
 \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}:  p <= 0.0001 }

 Note: If \code{signif.cutoffs} is provided, it takes precedence over
 \code{symnum.args}.}

\item{p.format.style}{character string specifying the p-value formatting style.
One of: \code{"default"} (backward compatible, uses scientific notation),
\code{"apa"} (APA style, no leading zero), \code{"nejm"} (NEJM style),
\code{"lancet"} (Lancet style), \code{"ama"} (AMA style), \code{"graphpad"}
(GraphPad style), or \code{"scientific"} (scientific notation for GWAS).
See \code{\link{list_p_format_styles}} for details.}

\item{p.digits}{integer specifying the number of decimal places for p-values.
If provided, overrides the style default.}

\item{p.leading.zero}{logical indicating whether to include leading zero before
decimal point (e.g., "0.05" vs ".05"). If provided, overrides the style default.}

\item{p.min.threshold}{numeric specifying the minimum p-value to display exactly.
Values below this threshold are shown as "< threshold". If provided, overrides
the style default.}

\item{p.decimal.mark}{character string to use as the decimal mark. If NULL,
uses \code{getOption("OutDec")}.}

\item{signif.cutoffs}{numeric vector of p-value cutoffs in descending order
for assigning significance symbols. For example, \code{c(0.10, 0.05, 0.01)}
means p < 0.10 gets "*", p < 0.05 gets "**", p < 0.01 gets "***".
If \code{use.four.stars = TRUE}, can include a fourth level.
Default is NULL, which uses the package defaults.}

\item{signif.symbols}{character vector of symbols corresponding to
\code{signif.cutoffs}. If NULL, auto-generated as "*", "**", "***"
(and "****" if \code{use.four.stars = TRUE}).}

\item{ns.symbol}{character string for non-significant results. Default is "ns".
Use "" (empty string) to show nothing.}

\item{use.four.stars}{logical. If TRUE, allows four stars (****) for the most
significant level. Default is FALSE.}

\item{show.signif}{logical. If TRUE (default), shows significance symbols when
using \code{label = "p.format.signif"}. If FALSE, falls back to showing only
the p-value (equivalent to \code{label = "p.format"}) with a warning.}

\item{geom}{The geometric object to use to display the data for this layer.
When using a \verb{stat_*()} function to construct a layer, the \code{geom} argument
can be used to override the default coupling between stats and geoms. The
\code{geom} argument accepts the following:
\itemize{
\item A \code{Geom} ggproto subclass, for example \code{GeomPoint}.
\item A string naming the geom. To give the geom as a string, strip the
function name of the \code{geom_} prefix. For example, to use \code{geom_point()},
give the geom as \code{"point"}.
\item For more information and other ways to specify the geom, see the
\link[ggplot2:layer_geoms]{layer geom} documentation.
}}

\item{position}{A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The \code{position} argument accepts the following:
\itemize{
\item The result of calling a position function, such as \code{position_jitter()}.
This method allows for passing extra arguments to the position.
\item A string naming the position adjustment. To give the position as a
string, strip the function name of the \code{position_} prefix. For example,
to use \code{position_jitter()}, give the position as \code{"jitter"}.
\item For more information and other ways to specify the position, see the
\link[ggplot2:layer_positions]{layer position} documentation.
}}

\item{na.rm}{If FALSE (the default), removes missing values with a warning. If
TRUE silently removes missing values.}

\item{show.legend}{logical. Should this layer be included in the legends?
\code{NA}, the default, includes if any aesthetics are mapped.
\code{FALSE} never includes, and \code{TRUE} always includes.
It can also be a named logical vector to finely select the aesthetics to
display. To include legend keys for all levels, even
when no data exists, use \code{TRUE}.  If \code{NA}, all levels are shown in legend,
but unobserved levels are omitted.}

\item{inherit.aes}{If \code{FALSE} (the default for most ggpubr functions),
overrides the default aesthetics, rather than combining with them. This is
most useful for helper functions that define both data and aesthetics and
shouldn't inherit behaviour from the default plot specification. Set to
\code{TRUE} to inherit aesthetics from the parent ggplot layer.}

\item{...}{other arguments to pass to \code{\link[ggplot2]{geom_text}} or
\code{\link[ggplot2:geom_text]{geom_label}}.}
}
\description{
Add mean comparison p-values to a ggplot, such as box plots, dot
 plots and stripcharts.
}
\details{
For grouped plots, if one or more subsets do not contain enough levels
 to identify the requested comparison, those subsets are skipped and valid
 subsets are still tested. This avoids fatal layer failures in sparse grouped
 data settings (Issue #663).
}
\examples{
# Load data
data("ToothGrowth")
head(ToothGrowth)

# Two independent groups
# :::::::::::::::::::::::::::::::::::::::::::::::::
p <- ggboxplot(ToothGrowth,
  x = "supp", y = "len",
  color = "supp", palette = "npg", add = "jitter"
)

#  Add p-value
p + stat_compare_means()
# Change method
p + stat_compare_means(method = "t.test")

# Paired samples
# :::::::::::::::::::::::::::::::::::::::::::::::::
ggpaired(ToothGrowth,
  x = "supp", y = "len",
  color = "supp", line.color = "gray", line.size = 0.4,
  palette = "npg"
) +
  stat_compare_means(paired = TRUE)

# Paired samples matched by a subject id column (row-order independent):
# `id` gives the correct p-value even when the rows are not in subject order.
# :::::::::::::::::::::::::::::::::::::::::::::::::
tg <- ToothGrowth
tg$id <- c(1:30, 30:1) # subjects, with the second group in reverse order
ggpaired(tg, x = "supp", y = "len", color = "supp",
  line.color = "gray", palette = "npg") +
  stat_compare_means(paired = TRUE, id = "id")

# More than two groups
# :::::::::::::::::::::::::::::::::::::::::::::::::
# Pairwise comparisons: Specify the comparisons you want
my_comparisons <- list(c("0.5", "1"), c("1", "2"), c("0.5", "2"))
ggboxplot(ToothGrowth,
  x = "dose", y = "len",
  color = "dose", palette = "npg"
) +
  # Add pairwise comparisons p-value
  stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40)) +
  stat_compare_means(label.y = 45) # Add global Anova p-value

# Multiple pairwise test against a reference group
ggboxplot(ToothGrowth,
  x = "dose", y = "len",
  color = "dose", palette = "npg"
) +
  stat_compare_means(method = "anova", label.y = 40) + # Add global p-value
  stat_compare_means(aes(label = after_stat(p.signif)),
    method = "t.test", ref.group = "0.5"
  )

# Multiple grouping variables
# :::::::::::::::::::::::::::::::::::::::::::::::::
# Box plot facetted by "dose"
p <- ggboxplot(ToothGrowth,
  x = "supp", y = "len",
  color = "supp", palette = "npg",
  add = "jitter",
  facet.by = "dose", short.panel.labs = FALSE
)
# Use only p.format as label. Remove method name.
p + stat_compare_means(
  aes(label = paste0("p = ", after_stat(p.format)))
)

}
\seealso{
\code{\link{compare_means}}
}
