Plot with the regression coefficients' point estimates as dots with confidence interval whiskers and other statistical details included as labels.
Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:
The dot-whisker plot contains a dot representing the estimate and their
confidence intervals (95% is the default). The estimate can either be
effect sizes (for tests that depend on the F-statistic) or regression
coefficients (for tests with t-, chi^2-, and z-statistic), etc. The
function will, by default, display a helpful x-axis label that should
clear up what estimates are being displayed. The confidence intervals can
sometimes be asymmetric if bootstrapping was used.
The label attached to dot will provide more details from the statistical test carried out and it will typically contain estimate, statistic, and p-value.
The caption will contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaike's Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, the "better" the model is.
The output of this function will be a {ggplot2} object and, thus,
it can be further modified (e.g. change themes) with {ggplot2}.
ggcoefstats(
x,
statistic = NULL,
conf.int = TRUE,
conf.level = 0.95,
digits = 2L,
exclude.intercept = FALSE,
effectsize.type = "eta",
meta.analytic.effect = FALSE,
meta.type = "parametric",
bf.message = TRUE,
sort = "none",
xlab = NULL,
ylab = NULL,
title = NULL,
subtitle = NULL,
caption = NULL,
only.significant = FALSE,
point.args = list(size = 3, color = "blue", na.rm = TRUE),
errorbar.args = list(height = 0, na.rm = TRUE),
vline = TRUE,
vline.args = list(linewidth = 1, linetype = "dashed"),
stats.labels = TRUE,
stats.label.color = NULL,
stats.label.args = list(size = 3, direction = "y", min.segment.length = 0, na.rm =
TRUE),
package = "RColorBrewer",
palette = "Dark2",
ggtheme = ggstatsplot::theme_ggstatsplot(),
...
)A model object to be tidied, or a tidy data frame from a regression
model. Function internally uses parameters::model_parameters() to get a
tidy data frame. If a data frame, it must contain at the minimum two
columns named term (names of predictors) and estimate (corresponding
estimates of coefficients or other quantities of interest).
Relevant statistic for the model ("t", "f", "z", or
"chi") in the label. Relevant only if x is a data frame.
Logical. Decides whether to display confidence intervals as
error bars (Default: TRUE).
Numeric deciding level of confidence or credible intervals
(Default: 0.95).
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()).
Logical that decides whether the intercept should be
excluded from the plot (Default: FALSE).
This is the same as es_type argument of
parameters::model_parameters(). Defaults to "eta", and relevant for
ANOVA-like objects.
Logical that decides whether subtitle for
meta-analysis via linear (mixed-effects) models (default: FALSE). If
TRUE, input to argument subtitle will be ignored. This will be mostly
relevant if a data frame with estimates and their standard errors is
entered.
Type of statistics used to carry out random-effects
meta-analysis. If "parametric" (default), metafor::rma() will be
used. If "robust", metaplus::metaplus() will be used. If "bayes",
metaBMA::meta_random() will be used.
Logical that decides whether results from running a
Bayesian meta-analysis assuming that the effect size d varies across
studies with standard deviation t (i.e., a random-effects analysis)
should be displayed in caption. Defaults to TRUE.
If "none" (default) do not sort, "ascending" sort by
increasing coefficient value, or "descending" sort by decreasing
coefficient value.
Label for x axis variable. If NULL (default),
variable name for x will be used.
Labels for y axis variable. If NULL (default),
variable name for y will be used.
The text for the plot title.
The text for the plot subtitle. The input to this argument
will be ignored if meta.analytic.effect is set to TRUE.
The text for the plot caption. This argument is relevant only
if bf.message = FALSE.
If TRUE, only stats labels for significant effects
is shown (Default: FALSE). This can be helpful when a large number of
regression coefficients are to be displayed in a single plot.
A list of additional aesthetic arguments to be passed to
the ggplot2::geom_point().
Additional arguments that will be passed to
geom_errorbarh() geom. Please see documentation for that function
to know more about these arguments.
Decides whether to display a vertical line (Default: "TRUE").
Additional arguments that will be passed to
geom_vline geom. Please see documentation for that function to
know more about these arguments.
Logical. Decides whether the statistic and p-values for
each coefficient are to be attached to each dot as a text label using
{ggrepel} (Default: TRUE).
Color for the labels. If set to NULL, colors will
be chosen from the specified package (Default: "RColorBrewer") and
palette (Default: "Dark2").
Additional arguments that will be passed to
ggrepel::geom_label_repel().
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).
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.
Additional arguments to tidying method. For more, see
parameters::model_parameters().
| graphical element | geom used | argument for further modification |
| regression estimate | ggplot2::geom_point() | point.args |
| error bars | ggplot2::geom_errorbarh() | errorbar.args |
| vertical line | ggplot2::geom_vline() | vline.args |
| label with statistical details | ggrepel::geom_label_repel() | stats.label.args |
The table below provides summary about:
statistical test carried out for inferential statistics
type of effect size estimate and a measure of uncertainty for this estimate
functions used internally to compute these details
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() |
For details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html