Scatterplots from ggplot2
combined with marginal
histograms/boxplots/density plots with statistical details added as a
subtitle.
ggscatterstats(data, x, y, type = "pearson", conf.level = 0.95,
bf.prior = 0.707, bf.message = FALSE, label.var = NULL,
label.expression = NULL, xlab = NULL, ylab = NULL, method = "lm",
method.args = list(), formula = y ~ x, point.color = "black",
point.size = 3, point.alpha = 0.4, point.width.jitter = NULL,
point.height.jitter = NULL, line.size = 1.5, line.color = "blue",
marginal = TRUE, marginal.type = "histogram", marginal.size = 5,
margins = c("both", "x", "y"), package = "wesanderson",
palette = "Royal1", direction = 1, xfill = "#009E73",
yfill = "#D55E00", xalpha = 1, yalpha = 1, xsize = 0.7,
ysize = 0.7, centrality.para = NULL, results.subtitle = TRUE,
title = NULL, subtitle = NULL, caption = NULL, nboot = 100,
beta = 0.1, k = 2, axes.range.restrict = FALSE,
ggtheme = ggplot2::theme_bw(), ggstatsplot.layer = TRUE,
messages = TRUE)
Dataframe from which variables specified are preferentially to be taken.
A vector containing the explanatory variable.
The response - a vector of length the number of rows of x
.
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's), "np"
(nonparametric/spearman), "r"
(robust),
"bf"
(for bayes factor), resp.
Scalar between 0 and 1. If unspecified, the defaults return
95%
lower and upper confidence intervals (0.95
).
A number between 0.5 and 2 (default 0.707
), the prior width
to use in calculating Bayes factors.
Logical. Decides whether to display Bayes Factor in favor
of null hypothesis for parametric test (Default: FALSE
).
Variable to use for points labels. Must be entered as a
character string e.g. "y"
.
An expression evaluating to a logical vector that
determines the subset of data points to label. Must be entered as a
character string e.g. "y < 4 & z < 20"
.
Label for x
axis variable.
Label for y
axis variable.
Smoothing method (function) to use, accepts either a character vector,
e.g. "auto"
, "lm"
, "glm"
, "gam"
, "loess"
or a function, e.g.
MASS::rlm
or mgcv::gam
, base::lm
, or base::loess
.
For method = "auto"
the smoothing method is chosen based on the
size of the largest group (across all panels). loess()
is
used for less than 1,000 observations; otherwise mgcv::gam()
is
used with formula = y ~ s(x, bs = "cs")
. Somewhat anecdotally,
loess
gives a better appearance, but is \(O(N^{2})\) in memory,
so does not work for larger datasets.
If you have fewer than 1,000 observations but want to use the same gam()
model that method = "auto"
would use, then set
method = "gam", formula = y ~ s(x, bs = "cs")
.
List of additional arguments passed on to the modelling
function defined by method
.
Formula to use in smoothing function, eg. y ~ x
,
y ~ poly(x, 2)
, y ~ log(x)
Aesthetics specifying geom point
(defaults: point.color = "black"
, point.size = 3
,point.alpha = 0.4
).
Degree of jitter in x
direction. Defaults to 40%
of the resolution of the data.
Degree of jitter in y
direction. Defaults to
40% of the resolution of the data.
Size for the regression line.
color for the regression line.
Decides whether ggExtra::ggMarginal()
plots will be
displayed; the default is TRUE
.
Type of marginal distribution to be plotted on the axes
("histogram"
, "boxplot"
, "density"
, "violin"
, "densigram"
).
Integer describing the relative size of the marginal
plots compared to the main plot. A size of 5
means that the main plot is
5x wider and 5x taller than the marginal plots.
Character describing along which margins to show the plots.
Any of the following arguments are accepted: "both"
, "x"
, "y"
.
Name of package from which the palette is desired as string or symbol.
Name of palette as string or symbol.
Either 1
or -1
. If -1
the palette will be reversed.
Character describing color fill for x
and y
axes
marginal distributions (default: "#009E73"
(for x
) and "#D55E00"
(for
y
)). If set to NULL
, manual specification of colors will be turned off
and 2 colors from the specified palette
from package
will be selected.
Numeric deciding transparency levels for the marginal
distributions. Any numbers from 0
(transparent) to 1
(opaque). The
default is 1
for both axes.
Size for the marginal distribution boundaries (Default:
0.7
).
Decides which measure of central tendency ("mean"
or "median"
) is to be displayed as vertical (for x
) and horizontal (for
y
) lines.
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.
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.
Number of bootstrap samples for computing effect size (Default:
100
).
bending constant (Default: 0.1
). For more, see ?WRS2::pbcor
.
Number of decimal places expected for results.
Logical decides whether to restrict the axes
values ranges to min and max values of the x
and y
variables (Default:
FALSE
).
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_economist()
,
hrbrthemes::theme_ipsum_ps()
, ggthemes::theme_fivethirtyeight()
, etc.).
Logical that decides whether theme_ggstatsplot
theme elements are to be displayed along with the selected ggtheme
(Default: TRUE
).
Decides whether messages references, notes, and warnings are
to be displayed (Default: TRUE
).
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html
# NOT RUN {
# to get reproducible results from bootstrapping
set.seed(123)
# creating dataframe
mtcars_new <- mtcars %>%
tibble::rownames_to_column(., var = "car") %>%
tibble::as_tibble(x = .)
# simple function call with the defaults
ggstatsplot::ggscatterstats(
data = mtcars_new,
x = wt,
y = mpg,
type = "np",
label.var = "car",
label.expression = "wt < 4 & mpg < 20",
axes.range.restrict = TRUE,
centrality.para = "median",
xfill = NULL
)
# }
Run the code above in your browser using DataLab