Visualization of a correlation matrix
Correlation matrix containing results from pairwise correlation tests. If you want a data frame of (grouped) correlation matrix, use correlation::correlation() instead. It can also do grouped analysis when used with output from dplyr::group_by().
ggcorrmat( data, cor.vars = NULL, cor.vars.names = NULL, matrix.type = "upper", type = "parametric", tr = 0.2, partial = FALSE, digits = 2L, sig.level = 0.05, conf.level = 0.95, bf.prior = 0.707, p.adjust.method = "holm", pch = "cross", ggcorrplot.args = list(method = "square", outline.color = "black", pch.cex = 14), package = "RColorBrewer", palette = "Dark2", colors = c("#E69F00", "white", "#009E73"), ggtheme = ggstatsplot::theme_ggstatsplot(), ggplot.component = NULL, title = NULL, subtitle = NULL, caption = NULL, ... )
data: A data frame from which variables specified are to be taken.
cor.vars: 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.
cor.vars.names: Optional list of names to be used for cor.vars. The names should be entered in the same order.
matrix.type: Character, "upper" (default), "lower", or "full", display full matrix, lower triangular or upper triangular matrix.
type: A character specifying the type of statistical approach:
"parametric""nonparametric""robust""bayes"You can specify just the initial letter.
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.
partial: Can be TRUE for partial correlations. For Bayesian partial correlations, "full" instead of pseudo-Bayesian partial correlations (i.e., Bayesian correlation based on frequentist partialization) are returned.
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()).
sig.level: Significance 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.
conf.level: Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.
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.
p.adjust.method: Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
pch: Decides the point shape to be used for insignificant correlation coefficients (only valid when insig = "pch"). Default: pch = "cross".
ggcorrplot.args: A 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: corr, method, p.mat, sig.level, ggtheme, colors, lab, pch, legend.title, digits.
package, 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).
colors: A 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.
ggtheme: A {ggplot2} theme. Default value is theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., ggplot2::theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contains. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.
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.
title: The text for the plot title.
subtitle: The text for the plot subtitle. Will work only if results.subtitle = FALSE.
caption: The text for the plot caption. This argument is relevant only if bf.message = FALSE.
...: Currently ignored.
For details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
| graphical element | geom used | argument for further modification |
| correlation matrix | ggcorrplot::ggcorrplot() | ggcorrplot.args |
The table below provides summary about:
Hypothesis testing and Effect size estimation
| Type | Test | CI available? | Function used |
| Parametric | Pearson's correlation coefficient | Yes | correlation::correlation() |
| Non-parametric | Spearman's rank correlation coefficient | Yes | correlation::correlation() |
| Robust | Winsorized Pearson's correlation coefficient | Yes | correlation::correlation() |
| Bayesian | Bayesian Pearson's correlation coefficient | Yes | correlation::correlation() |
set.seed(123) library(ggcorrplot) ggcorrmat(iris)
grouped_ggcorrmat ggscatterstats
grouped_ggscatterstats
Useful links