View source: R/geom-quantile.R
geom_quantile | R Documentation |
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
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
... |
Other arguments passed on to
|
lineend |
Line end style (round, butt, square). |
linejoin |
Line join style (round, mitre, bevel). |
linemitre |
Line mitre limit (number greater than 1). |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
geom , stat |
Use to override the default connection between
|
quantiles |
conditional quantiles of y to calculate and display |
formula |
formula relating y variables to x variables |
method |
Quantile regression method to use. Available options are |
method.args |
List of additional arguments passed on to the modelling
function defined by |
geom_quantile()
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
group
linetype
linewidth
weight
Learn more about setting these aesthetics in vignette("ggplot2-specs")
.
These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.
after_stat(quantile)
Quantile of distribution.
m <-
ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
m + geom_quantile()
m + geom_quantile(quantiles = 0.5)
q10 <- seq(0.05, 0.95, by = 0.05)
m + geom_quantile(quantiles = q10)
# You can also use rqss to fit smooth quantiles
m + geom_quantile(method = "rqss")
# 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)
# Set aesthetics to fixed value
m + geom_quantile(colour = "red", linewidth = 2, alpha = 0.5)
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