Grouped box/violin plots for repeated measures comparisons

Applies ggwithinstats() across all levels of grouping.var and combines the resulting plots using combine_plots().

Usage

grouped_ggwithinstats(
  data,
  ...,
  grouping.var,
  plotgrid.args = list(),
  annotation.args = list()
)

Arguments

data

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.

...

Arguments passed on to ggwithinstats

x

The condition (or repeated-measures) variable from data.

point.path,centrality.path

Logical that decides whether individual data points and centrality measures, respectively, are connected using ggplot2::geom_path() (Default: TRUE). point.path is relevant only when there are two groups (i.e., for a t-test). With many data points, consider point.path = FALSE, as these lines can overwhelm the plot.

centrality.path.args,point.path.args

A list of additional aesthetic arguments passed to ggplot2::geom_path() connecting centrality measures and raw data points, respectively.

subject.id

The subject (or repeated measures) identifier from data. Each subject must appear at most once per level of x. If NULL (default), observations are paired by their row order within each level of x. If you are not sure the data are sorted this way, specify this argument. Rows with a missing x or subject.id are ignored, and subjects with a missing (NA) dependent variable in any condition are dropped list-wise (see statsExpressions::complete_subjects()), so the plot, its labels, and the statistical results are all based on the same complete subjects.

xlab

Label for x axis variable. If NULL (default), variable name for x will be used.

ylab

Label for y axis variable. If NULL (default), variable name for y will be used.

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

pairwise.display

Decides which pairwise comparisons to display. Available options are:

• "significant" (abbreviation accepted: "s")

• "non-significant" (abbreviation accepted: "ns")

• "all"

• "none"

Use this to keep the plot uncluttered when many groups are compared. Pairwise comparisons are carried out (and displayed) only when x has three or more levels. For type = "bayes", there are no p-values, so all comparisons are displayed unless this is "none".

pairwise.alpha

Numeric alpha threshold used to decide which pairwise comparisons are displayed when pairwise.display = "significant" or pairwise.display = "non-significant" (Default: 0.05). It is compared against p-values adjusted using p.adjust.method.

bf.message

Logical that decides whether to display the Bayes Factor in favor of the null hypothesis as a caption. Relevant only for parametric tests (Default: TRUE).

results.subtitle

Logical that decides whether the results of statistical tests are displayed as a subtitle (Default: TRUE). Setting this to FALSE also skips the automatic Bayes Factor caption. Other plot details have their own arguments.

subtitle

The text for the plot subtitle. Used only if results.subtitle = FALSE.

caption

The text for the plot caption. It will be replaced by the Bayes Factor caption whenever that is displayed (see bf.message).

centrality.plotting

Logical that decides whether central 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

Use centrality.type to choose a different centrality measure.

centrality.type

Decides which centrality measure is displayed. Defaults to the same value as type. Can be one of:

• "parametric" (for mean)

• "nonparametric" (for median)

• "robust" (for trimmed mean)

• "bayes" (for MAP estimator)

As with type, abbreviations are accepted.

point.args

A list of additional aesthetic arguments passed to ggplot2::geom_point().

boxplot.args

A list of additional aesthetic arguments passed to ggplot2::geom_boxplot(). By default, the whiskers extend to 1.5 times the interquartile range (IQR) from the box (Tukey-style). Use coef to change this, e.g., boxplot.args = list(coef = 3) extends the whiskers to the most extreme observations within 3 * IQR of the hinges.

violin.args

A list of additional aesthetic arguments passed to ggplot2::geom_violin().

ggplot.component

A {ggplot2} component, or a list of components, to add to the plot (Default: NULL). This is mostly helpful for the grouped_ variants, where it is applied to each individual plot.

palette

Name of the discrete palette in "package::palette" format to be used for coloring (via {paletteer}). Run View(paletteer::palettes_d_names) to see all available options.

centrality.point.args,centrality.label.args

A list of additional aesthetic arguments passed to ggplot2::geom_point() and ggrepel::geom_label_repel(), respectively, which display the centrality measure.

ggsignif.args

A list of additional aesthetic arguments passed to ggsignif::geom_signif().

ggtheme

A {ggplot2} theme (Default: theme_ggstatsplot()). Any {ggplot2} theme (e.g., ggplot2::theme_bw()) or theme from an extension package (e.g., ggthemes::theme_fivethirtyeight()) can be used. Note that some themes remove plot details. For example, ggthemes::theme_fivethirtyeight() removes the secondary Y-axis, which ggbetweenstats() uses to show details about pairwise comparisons.

y

The response (or outcome or dependent) variable from data.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter (e.g. "np" or "bf"). Matching is on the initial lowercase letter only, so any other value (including capitalized values such as "Bayes") falls back to "parametric" without a warning.

digits

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()).

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95).

bf.prior

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.

tr

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.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

grouping.var

A single grouping variable. A separate plot is created for each of its levels (in factor level order, or in order of appearance for character variables). Rows with a missing value in this variable are removed.

plotgrid.args

A list of additional arguments passed to patchwork::wrap_plots(), except for guides, which has its own argument.

annotation.args

A list of additional arguments passed to patchwork::plot_annotation().

Value

A patchwork object combining one plot per level of grouping.var (see combine_plots()). Applying extract_stats() to it returns a list with the statistical details for each of these plots.

See also

ggwithinstats(), ggbetweenstats(), grouped_ggbetweenstats()

Examples

# for reproducibility
set.seed(123)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)

# the most basic function call
grouped_ggwithinstats(
  data             = filter(bugs_long, condition %in% c("HDHF", "HDLF")),
  x                = condition,
  y                = desire,
  subject.id       = subject,
  grouping.var     = gender,
  type             = "np",
  # additional modifications for **each** plot using `{ggplot2}` functions
  ggplot.component = scale_y_continuous(breaks = seq(0, 10, 1), limits = c(0, 10)),
  annotation.args  = list(title = "Desire ratings by condition for each gender")
)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.