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Helper function for ggstatsplot::ggbetweenstats to apply this function across multiple levels of a given factor and combining the resulting plots using ggstatsplot::combine_plots.

Usage

grouped_ggbetweenstats(
  data,
  ...,
  grouping.var,
  output = "plot",
  plotgrid.args = list(),
  annotation.args = list()
)

Arguments

data

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.

...

Arguments passed on to ggbetweenstats

plot.type

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).

xlab

Labels for x and y axis variables. If NULL (default), variable names for x and y will be used.

ylab

Labels for x and y axis variables. If NULL (default), variable names for x and y will be used.

pairwise.comparisons

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 pairwise_comparisons function. For more details about pairwise comparisons, see the documentation for that function.

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"

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.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors.

bf.message

Logical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: TRUE).

results.subtitle

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.

subtitle

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

caption

The text for the plot caption.

outlier.color

Default aesthetics for outliers (Default: "black").

outlier.tagging

Decides whether outliers should be tagged (Default: FALSE).

outlier.label

Label to put on the outliers that have been tagged. This can't be the same as x argument.

outlier.shape

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.

outlier.label.args

A list of additional aesthetic arguments to be passed to ggrepel::geom_label_repel for outlier label plotting.

outlier.coef

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).

centrality.plotting

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.

centrality.type

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.

point.args

A list of additional aesthetic arguments to be passed to the geom_point displaying the raw data.

violin.args

A list of additional aesthetic arguments to be passed to the geom_violin.

ggplot.component

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.

package

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).

palette

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).

centrality.point.args

A list of additional aesthetic arguments to be passed to geom_point and ggrepel::geom_label_repel geoms, which are involved in mean plotting.

centrality.label.args

A list of additional aesthetic arguments to be passed to geom_point and ggrepel::geom_label_repel geoms, which are involved in mean plotting.

ggsignif.args

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

ggtheme

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.).

x

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.

y

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

type

A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.

effsize.type

Type of effect size needed for parametric tests. The argument can be "eta" (partial eta-squared) or "omega" (partial omega-squared).

k

Number of digits after decimal point (should be an integer) (Default: k = 2L).

var.equal

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.

conf.level

Scalar between 0 and 1. If unspecified, the defaults return 95% confidence/credible intervals (0.95).

nboot

Number of bootstrap samples for computing confidence interval for the effect size (Default: 100L).

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.

grouping.var

A single grouping variable.

output

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.

plotgrid.args

A list of additional arguments passed to patchwork::wrap_plots, except for guides argument which is already separately specified here.

annotation.args

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

Examples

# \donttest{
if (require("PMCMRplus")) {
  # to get reproducible results from bootstrapping
  set.seed(123)
  library(ggstatsplot)
  library(dplyr, warn.conflicts = FALSE)
  library(ggplot2)

  # the most basic function call
  grouped_ggbetweenstats(
    data = filter(ggplot2::mpg, drv != "4"),
    x = year,
    y = hwy,
    grouping.var = drv
  )

  # modifying individual plots using `ggplot.component` argument
  grouped_ggbetweenstats(
    data = filter(
      movies_long,
      genre %in% c("Action", "Comedy"),
      mpaa %in% c("R", "PG")
    ),
    x = genre,
    y = rating,
    grouping.var = mpaa,
    ggplot.component = scale_y_continuous(
      breaks = seq(1, 9, 1),
      limits = (c(1, 9))
    )
  )
}

# }