Visualization of a correlation matrix

ggcorrmat(
  data,
  cor.vars = NULL,
  cor.vars.names = NULL,
  output = "plot",
  matrix.type = "full",
  matrix.method = "square",
  type = "parametric",
  beta = 0.1,
  k = 2L,
  sig.level = 0.05,
  conf.level = 0.95,
  bf.prior = 0.707,
  p.adjust.method = "none",
  pch = "cross",
  ggcorrplot.args = list(outline.color = "black"),
  package = "RColorBrewer",
  palette = "Dark2",
  colors = c("#E69F00", "white", "#009E73"),
  ggtheme = ggplot2::theme_bw(),
  ggstatsplot.layer = TRUE,
  ggplot.component = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  messages = TRUE,
  ...
)

Arguments

data

Dataframe from which variables specified are preferentially 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.

output

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.

matrix.type

Character, "full" (default), "upper" or "lower", display full matrix, lower triangular or upper triangular matrix.

matrix.method

The visualization method of correlation matrix to be used. Allowed values are "square" (default) or "circle".

type

Type 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.

beta

bending constant (Default: 0.1). For more, see WRS2::pbcor().

k

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

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. Relevant only when output = "plot".

conf.level

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

bf.prior

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

p.adjust.method

What 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.

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 by ggstatsplot: corr, method, p.mat, sig.level, ggtheme, colors, matrix.type, lab, pch, legend.title, digits.

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).

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 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.layer

Logical 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.component

A 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.

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.

messages

Decides whether messages references, notes, and warnings are to be displayed (Default: TRUE).

...

Currently ignored.

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

See also

Examples

# \donttest{ # for reproducibility set.seed(123) # if `cor.vars` not specified, all numeric variables used ggstatsplot::ggcorrmat(iris)
# to get the correlalogram # note that the function will run even if the vector with variable names is # not of same length as the number of variables ggstatsplot::ggcorrmat( data = ggplot2::msleep, type = "robust", cor.vars = sleep_total:bodywt, cor.vars.names = c("total sleep", "REM sleep"), matrix.type = "lower" )
#> Warning: No. of variable names doesn't equal no. of variables.
#>
# to get the correlation analyses results in a dataframe ggstatsplot::ggcorrmat( data = ggplot2::msleep, cor.vars = sleep_total:bodywt, output = "dataframe" )
#> # A tibble: 15 x 10 #> parameter1 parameter2 r ci_low ci_high t df p method nobs #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <chr> <int> #> 1 sleep_total sleep_rem 0.752 0.617 0.844 8.76 59 2.92e- 12 Pearson 61 #> 2 sleep_total sleep_cycle -0.474 -0.706 -0.150 -2.95 30 6.17e- 3 Pearson 32 #> 3 sleep_total awake -1.00 -1.00 -1.00 -5329. 81 2.42e-226 Pearson 83 #> 4 sleep_total brainwt -0.360 -0.569 -0.108 -2.84 54 6.35e- 3 Pearson 56 #> 5 sleep_total bodywt -0.312 -0.494 -0.103 -2.96 81 4.09e- 3 Pearson 83 #> 6 sleep_rem sleep_cycle -0.338 -0.614 0.0120 -1.97 30 5.84e- 2 Pearson 32 #> 7 sleep_rem awake -0.752 -0.844 -0.617 -8.76 59 2.91e- 12 Pearson 61 #> 8 sleep_rem brainwt -0.221 -0.476 0.0670 -1.54 46 1.31e- 1 Pearson 48 #> 9 sleep_rem bodywt -0.328 -0.535 -0.0826 -2.66 59 9.95e- 3 Pearson 61 #> 10 sleep_cycle awake 0.474 0.150 0.706 2.95 30 6.17e- 3 Pearson 32 #> 11 sleep_cycle brainwt 0.852 0.709 0.927 8.60 28 2.42e- 9 Pearson 30 #> 12 sleep_cycle bodywt 0.418 0.0809 0.669 2.52 30 1.73e- 2 Pearson 32 #> 13 awake brainwt 0.360 0.108 0.569 2.84 54 6.35e- 3 Pearson 56 #> 14 awake bodywt 0.312 0.103 0.494 2.96 81 4.09e- 3 Pearson 83 #> 15 brainwt bodywt 0.934 0.889 0.961 19.2 54 9.16e- 26 Pearson 56
# }