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

grouped_ggcorrmat(
data,
cor.vars = NULL,
cor.vars.names = NULL,
grouping.var,
title.prefix = NULL,
output = "plot",
...,
plotgrid.args = list(),
title.text = NULL,
title.args = list(size = 16, fontface = "bold"),
caption.text = NULL,
caption.args = list(size = 10),
sub.text = NULL,
sub.args = list(size = 12)
)

## Arguments

data Dataframe from which variables specified are preferentially to be taken. List of variables for which the correlation matrix is to be computed and visualized. If NULL (default), all numeric variables from data will be used. Optional list of names to be used for cor.vars. The names should be entered in the same order. A single grouping variable (can be entered either as a bare name x or as a string "x"). Character string specifying the prefix text for the fixed plot title (name of each factor level) (Default: NULL). If NULL, the variable name entered for grouping.var will be used. Character that decides expected output from this function. If "plot", the visualization matrix will be returned. If "dataframe" (or literally anything other than "plot"), a dataframe containing all details from statistical analyses (e.g., correlation coefficients, statistic values, p-values, no. of observations, etc.) will be returned. Arguments passed on to ggcorrmat matrix.typeCharacter, "full" (default), "upper" or "lower", display full matrix, lower triangular or upper triangular matrix. matrix.methodThe visualization method of correlation matrix to be used. Allowed values are "square" (default) or "circle". sig.levelSignificance level (Default: 0.05). If the p-value in p-value matrix is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant and flagged as such in the plot. Relevant only when output = "plot". p.adjust.methodWhat adjustment for multiple tests should be used? ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"). See stats::p.adjust for details about why to use "holm" rather than "bonferroni"). Default is "none". If adjusted p-values are displayed in the visualization of correlation matrix, the adjusted p-values will be used for the upper triangle, while unadjusted p-values will be used for the lower triangle of the matrix. colorsA vector of 3 colors for low, mid, and high correlation values. If set to NULL, manual specification of colors will be turned off and 3 colors from the specified palette from package will be selected. pchDecides the point shape to be used for insignificant correlation coefficients (only valid when insig = "pch"). Default: pch = "cross". ggcorrplot.argsA list of additional (mostly aesthetic) arguments that will be passed to ggcorrplot::ggcorrplot function. The list should avoid any of the following arguments since they are already internally being used by ggstatsplot: corr, method, p.mat, sig.level, ggtheme, colors, matrix.type, lab, pch, legend.title, digits. messagesDecides whether messages references, notes, and warnings are to be displayed (Default: TRUE). typeType of association between paired samples required (""parametric": Pearson's product moment correlation coefficient" or ""nonparametric": Spearman's rho" or ""robust": percentage bend correlation coefficient" or ""bayes": Bayes Factor for Pearson's r"). Corresponding abbreviations are also accepted: "p" (for parametric/pearson), "np" (nonparametric/spearman), "r" (robust), "bf" (for bayes factor), resp. betabending constant (Default: 0.1). For more, see ?WRS2::pbcor. kNumber of digits after decimal point (should be an integer) (Default: k = 2). conf.levelScalar between 0 and 1. If unspecified, the defaults return 95% lower and upper confidence intervals (0.95). bf.priorA number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors. packageName of package from which the palette is desired as string or symbol. paletteName of palette as string or symbol. ggthemeA function, ggplot2 theme name. Default value is ggplot2::theme_bw(). Any of the ggplot2 themes, or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). ggstatsplot.layerLogical that decides whether theme_ggstatsplot theme elements are to be displayed along with the selected ggtheme (Default: TRUE). theme_ggstatsplot is an opinionated theme layer that override some aspects of the selected ggtheme. ggplot.componentA ggplot component to be added to the plot prepared by ggstatsplot. This argument is primarily helpful for grouped_ variant of the current function. Default is NULL. The argument should be entered as a function. subtitleThe text for the plot subtitle. Will work only if results.subtitle = FALSE. captionThe text for the plot caption. A list of additional arguments to cowplot::plot_grid. String or plotmath expression to be drawn as title for the combined plot. A list of additional arguments provided to title, caption and sub, resp. String or plotmath expression to be drawn as the caption for the combined plot. A list of additional arguments provided to title, caption and sub, resp. The label with which the combined plot should be annotated. Can be a plotmath expression. A list of additional arguments provided to title, caption and sub, resp.

## Value

Correlation matrix plot or a dataframe containing results from pairwise correlation tests. The package internally uses ggcorrplot::ggcorrplot for creating the visualization matrix, while the correlation analysis is carried out using the correlation::correlation function.

## References

https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html

ggcorrmat, ggscatterstats, grouped_ggscatterstats

## Examples

# \donttest{
# for reproducibility
set.seed(123)

# for plot
ggstatsplot::grouped_ggcorrmat(
data = iris,
grouping.var = Species,
type = "robust",
)
# for dataframe
ggstatsplot::grouped_ggcorrmat(
data = ggplot2::msleep,
grouping.var = vore,
type = "bayes",
output = "dataframe"
)#> Warning: Series not converged.#> Warning: Series not converged.#> Warning: Series not converged.#> Warning: Series not converged.#> # A tibble: 60 x 13
#>    vore  parameter1  parameter2      rho ci_low ci_high    pd rope_percentage
#>    <chr> <chr>       <chr>         <dbl>  <dbl>   <dbl> <dbl>           <dbl>
#>  1 carni sleep_total sleep_rem    0.850   0.641  0.961  1              0
#>  2 carni sleep_total sleep_cycle  0.213  -0.359  0.750  0.692          0.176
#>  3 carni sleep_total awake       -1.00   -1.00  -1.00   1              0
#>  4 carni sleep_total brainwt     -0.389  -0.787  0.0205 0.897          0.115
#>  5 carni sleep_total bodywt      -0.371  -0.654 -0.0701 0.96           0.0875
#>  6 carni sleep_rem   sleep_cycle  0.0727 -0.518  0.610  0.552          0.192
#>  7 carni sleep_rem   awake       -0.843  -0.958 -0.660  1              0
#>  8 carni sleep_rem   brainwt     -0.316  -0.763  0.244  0.785          0.157
#>  9 carni sleep_rem   bodywt      -0.366  -0.766  0.0411 0.887          0.116
#> 10 carni sleep_cycle awake       -0.214  -0.741  0.356  0.692          0.182
#>    prior_distribution prior_location prior_scale      bf  nobs
#>    <chr>                       <dbl>       <dbl>   <dbl> <int>
#>  1 cauchy                          0       0.707 112.       10
#>  2 cauchy                          0       0.707   0.714     5
#>  3 cauchy                          0       0.707  NA        19
#>  4 cauchy                          0       0.707   1.13      9
#>  5 cauchy                          0       0.707   1.72     19
#>  6 cauchy                          0       0.707   0.621     5
#>  7 cauchy                          0       0.707 112.       10
#>  8 cauchy                          0       0.707   0.848     6
#>  9 cauchy                          0       0.707   1.03     10
#> 10 cauchy                          0       0.707   0.714     5
#> # ... with 50 more rows# }