This fits a quantile regression to the data and draws the fitted quantiles
with lines. This is as a continuous analogue to
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(). 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
mappingif there is no plot mapping.
The data to be displayed in this layer. There are three options:
NULL, the default, the data is inherited from the plot data as specified in the call to
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.
functionwill 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
functioncan be created from a
~ head(.x, 10)).
Position adjustment, either as a string naming the adjustment (e.g.
position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.
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).
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.
FALSEnever includes, and
TRUEalways includes. It can also be a named logical vector to finely select the aesthetics to display.
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.
- geom, stat
Use to override the default connection between
conditional quantiles of y to calculate and display
formula relating y variables to x variables
List of additional arguments passed on to the modelling function defined by
geom_quantile() understands the following aesthetics (required aesthetics are in bold):
Learn more about setting these aesthetics in
These are calculated by the 'stat' part of layers and can be accessed with delayed evaluation.
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", linewidth = 2, alpha = 0.5) #> Smoothing formula not specified. Using: y ~ x