This fits a quantile regression to the data and draws the fitted quantiles
with lines. This is as a continuous analogue to geom_boxplot()
.
geom_quantile(
mapping = NULL,
data = NULL,
stat = "quantile",
position = "identity",
...,
lineend = "butt",
linejoin = "round",
linemitre = 10,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
stat_quantile(
mapping = NULL,
data = NULL,
geom = "quantile",
position = "identity",
...,
quantiles = c(0.25, 0.5, 0.75),
formula = NULL,
method = "rq",
method.args = list(),
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
Set of aesthetic mappings created by aes()
or
aes_()
. If specified and inherit.aes = TRUE
(the
default), it is combined with the default mapping at the top level of the
plot. You must supply mapping
if there is no plot mapping.
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot()
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify()
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame
, and
will be used as the layer data. A function
can be created
from a formula
(e.g. ~ head(.x, 10)
).
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Other arguments passed on to layer()
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
colour = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.
Line end style (round, butt, square).
Line join style (round, mitre, bevel).
Line mitre limit (number greater than 1).
If FALSE
, the default, missing values are removed with
a warning. If TRUE
, missing values are silently removed.
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders()
.
Use to override the default connection between
geom_quantile()
and stat_quantile()
.
conditional quantiles of y to calculate and display
formula relating y variables to x variables
Quantile regression method to use. Available options are "rq"
(for
quantreg::rq()
) and "rqss"
(for quantreg::rqss()
).
List of additional arguments passed on to the modelling
function defined by method
.
geom_quantile()
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
group
linetype
size
weight
Learn more about setting these aesthetics in vignette("ggplot2-specs")
.
quantile of distribution
m <-
ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
m + geom_quantile()
#> Smoothing formula not specified. Using: y ~ x
m + geom_quantile(quantiles = 0.5)
#> Smoothing formula not specified. Using: y ~ x
q10 <- seq(0.05, 0.95, by = 0.05)
m + geom_quantile(quantiles = q10)
#> Smoothing formula not specified. Using: y ~ x
# You can also use rqss to fit smooth quantiles
m + geom_quantile(method = "rqss")
#> Smoothing formula not specified. Using: y ~ qss(x, lambda = 1)
# Note that rqss doesn't pick a smoothing constant automatically, so
# you'll need to tweak lambda yourself
m + geom_quantile(method = "rqss", lambda = 0.1)
#> Smoothing formula not specified. Using: y ~ qss(x, lambda = 0.1)
# Set aesthetics to fixed value
m + geom_quantile(colour = "red", size = 2, alpha = 0.5)
#> Smoothing formula not specified. Using: y ~ x