A dot chart (as described by William S. Cleveland) with statistical details from one-sample test.
ggdotplotstats(
data,
x,
y,
xlab = NULL,
ylab = NULL,
title = NULL,
subtitle = NULL,
caption = NULL,
type = "parametric",
test.value = 0,
bf.prior = 0.707,
bf.message = TRUE,
effsize.type = "g",
conf.level = 0.95,
tr = 0.2,
digits = 2L,
results.subtitle = TRUE,
point.args = list(color = "black", size = 3, shape = 16),
centrality.plotting = TRUE,
centrality.type = type,
centrality.line.args = list(color = "blue", linewidth = 1, linetype = "dashed"),
ggplot.component = NULL,
ggtheme = ggstatsplot::theme_ggstatsplot(),
...
)A data frame (or a tibble) from which variables specified are to
be taken. Other data types (e.g., matrix,table, array, etc.) will not
be accepted. Additionally, grouped data frames from {dplyr} should be
ungrouped before they are entered as data.
A numeric variable from the data frame data.
Label or grouping variable.
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. Will work only if
results.subtitle = FALSE.
The text for the plot caption. This argument is relevant only
if bf.message = FALSE.
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
A number indicating the true value of the mean (Default:
0).
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.
Logical that decides whether to display Bayes Factor in
favor of the null hypothesis. This argument is relevant only for
parametric test (Default: TRUE).
Type of effect size needed for parametric tests. The
argument can be "d" (for Cohen's d) or "g" (for Hedge's g).
Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals
will be computed.
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.
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()).
Decides whether the results of statistical tests are
to be displayed as a subtitle (Default: TRUE). If set to FALSE, only
the plot will be returned.
A list of additional aesthetic arguments to be passed to
the ggplot2::geom_point().
Logical that decides whether centrality tendency
measure is to be displayed as a point with a label (Default: TRUE).
Function decides which central tendency measure to show depending on the
type argument.
mean for parametric statistics
median for non-parametric statistics
trimmed mean for robust statistics
MAP estimator for Bayesian statistics
If you want default centrality parameter, you can specify this using
centrality.type argument.
Decides which centrality parameter is to be displayed.
The default is to choose the same as type argument. You can specify this
to be:
"parameteric" (for mean)
"nonparametric" (for median)
robust (for trimmed mean)
bayes (for MAP estimator)
Just as type argument, abbreviations are also accepted.
A list of additional aesthetic arguments to be
passed to the ggplot2::geom_line() used to display the lines
corresponding to the centrality parameter.
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.
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.
Currently ignored.
| graphical element | geom used | argument for further modification |
| raw data | ggplot2::geom_point() | point.args |
| centrality measure line | ggplot2::geom_vline() | centrality.line.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
| Type | Test | Function used |
| Parametric | One-sample Student's t-test | stats::t.test() |
| Non-parametric | One-sample Wilcoxon test | stats::wilcox.test() |
| Robust | Bootstrap-t method for one-sample test | WRS2::trimcibt() |
| Bayesian | One-sample Student's t-test | BayesFactor::ttestBF() |
Effect size estimation
| Type | Effect size | CI available? | Function used |
| Parametric | Cohen's d, Hedge's g | Yes | effectsize::cohens_d(), effectsize::hedges_g() |
| Non-parametric | r (rank-biserial correlation) | Yes | effectsize::rank_biserial() |
| Robust | trimmed mean | Yes | WRS2::trimcibt() |
| Bayes Factor | difference | Yes | bayestestR::describe_posterior() |
For details, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggdotplotstats.html
grouped_gghistostats, gghistostats,
grouped_ggdotplotstats
if (FALSE) { # identical(Sys.getenv("NOT_CRAN"), "true")
# for reproducibility
set.seed(123)
# creating a plot
p <- ggdotplotstats(
data = ggplot2::mpg,
x = cty,
y = manufacturer,
title = "Fuel economy data",
xlab = "city miles per gallon"
)
# looking at the plot
p
# extracting details from statistical tests
extract_stats(p)
}
Run the code above in your browser using DataLab