
stat_smooth(mapping = NULL, data = NULL, geom = "smooth",
position = "identity", method = "auto",
formula = y ~ x, se = TRUE, n = 80, fullrange = FALSE,
level = 0.95, na.rm = FALSE, ...)
loess
. For datasets with 1000 or more
observations defaults to gam, see
y ~ x
, y ~ poly(x, 2)
, y ~ log(x)
FALSE
(the default), removes
missing values with a warning. If TRUE
silently
removes missing values.aes
or aes_string
. Only
needs to be set at the layer level if you are overriding
the plot defaults.predictdf
generic function and its methods. For
most methods the confidence bounds are computed using the
predict
method - the exceptions are
loess
which uses a t-based approximation, and for
glm
where the normal confidence interval is
constructed on the link scale, and then back-transformed
to the response scale.lm
for linear smooths, glm
for generalised linear smooths, loess
for
local smoothsc <- ggplot(mtcars, aes(qsec, wt))
c + stat_smooth()
c + stat_smooth() + geom_point()
# Adjust parameters
c + stat_smooth(se = FALSE) + geom_point()
c + stat_smooth(span = 0.9) + geom_point()
c + stat_smooth(level = 0.99) + geom_point()
c + stat_smooth(method = "lm") + geom_point()
library(splines)
library(MASS)
c + stat_smooth(method = "lm", formula = y ~ ns(x,3)) +
geom_point()
c + stat_smooth(method = rlm, formula= y ~ ns(x,3)) + geom_point()
# The default confidence band uses a transparent colour.
# This currently only works on a limited number of graphics devices
# (including Quartz, PDF, and Cairo) so you may need to set the
# fill colour to a opaque colour, as shown below
c + stat_smooth(fill = "grey50", size = 2, alpha = 1)
c + stat_smooth(fill = "blue", size = 2, alpha = 1)
# The colour of the line can be controlled with the colour aesthetic
c + stat_smooth(fill="blue", colour="darkblue", size=2)
c + stat_smooth(fill="blue", colour="darkblue", size=2, alpha = 0.2)
c + geom_point() +
stat_smooth(fill="blue", colour="darkblue", size=2, alpha = 0.2)
# Smoothers for subsets
c <- ggplot(mtcars, aes(y=wt, x=mpg)) + facet_grid(. ~ cyl)
c + stat_smooth(method=lm) + geom_point()
c + stat_smooth(method=lm, fullrange = TRUE) + geom_point()
# Geoms and stats are automatically split by aesthetics that are factors
c <- ggplot(mtcars, aes(y=wt, x=mpg, colour=factor(cyl)))
c + stat_smooth(method=lm) + geom_point()
c + stat_smooth(method=lm, aes(fill = factor(cyl))) + geom_point()
c + stat_smooth(method=lm, fullrange=TRUE, alpha = 0.1) + geom_point()
# Use qplot instead
qplot(qsec, wt, data=mtcars, geom=c("smooth", "point"))
# Example with logistic regression
data("kyphosis", package="rpart")
qplot(Age, Kyphosis, data=kyphosis)
qplot(Age, data=kyphosis, facets = . ~ Kyphosis, binwidth = 10)
qplot(Age, Kyphosis, data=kyphosis, position="jitter")
qplot(Age, Kyphosis, data=kyphosis, position=position_jitter(height=0.1))
qplot(Age, as.numeric(Kyphosis) - 1, data = kyphosis) +
stat_smooth(method="glm", family="binomial")
qplot(Age, as.numeric(Kyphosis) - 1, data=kyphosis) +
stat_smooth(method="glm", family="binomial", formula = y ~ ns(x, 2))
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