R/grouped_ggcorrmat.R
grouped_ggcorrmat.Rd
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) )
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 |
cor.vars.names | Optional list of names to be used for |
grouping.var | A single grouping variable (can be entered either as a
bare name |
title.prefix | Character string specifying the prefix text for the fixed
plot title (name of each factor level) (Default: |
output | Character that decides expected output from this function. If
|
... | Arguments passed on to
|
plotgrid.args | A list of additional arguments to |
title.text | String or plotmath expression to be drawn as title for the combined plot. |
title.args | A list of additional arguments
provided to |
caption.text | String or plotmath expression to be drawn as the caption for the combined plot. |
caption.args | A list of additional arguments
provided to |
sub.text | The label with which the combined plot should be annotated. Can be a plotmath expression. |
sub.args | A list of additional arguments
provided to |
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.
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
# \donttest{ # for reproducibility set.seed(123) # for plot ggstatsplot::grouped_ggcorrmat( data = iris, grouping.var = Species, type = "robust", p.adjust.method = "holm" )# 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# }