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
The data to be displayed in this layer. There are three options:
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Other arguments passed on to
Line end style (round, butt, square).
Line join style (round, mitre, bevel).
Line mitre limit (number greater than 1).
logical. Should this layer be included in the legends?
Use to override the default connection between
conditional quantiles of y to calculate and display
formula relating y variables to x variables
Quantile regression method to use. Currently only supports
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
quantile of distribution
#>#> #>#>#> #>#> #>#>#> #>#>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", size = 2, alpha = 0.5)#>