stat_smooth {ggplot2} | R Documentation |
Aids the eye in seeing patterns in the presence of overplotting.
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, ...)
method |
smoothing method (function) to use, eg. lm,
glm, gam, loess, rlm. For datasets with n < 1000 default
is |
formula |
formula to use in smoothing function, eg.
|
se |
display confidence interval around smooth? (TRUE by default, see level to control |
fullrange |
should the fit span the full range of the plot, or just the data |
level |
level of confidence interval to use (0.95 by default) |
n |
number of points to evaluate smoother at |
na.rm |
If |
... |
other arguments are passed to smoothing function |
mapping |
The aesthetic mapping, usually constructed
with |
data |
A layer specific dataset - only needed if you want to override the plot defaults. |
geom |
The geometric object to use display the data |
position |
The position adjustment to use for overlappling points on this layer |
Calculation is performed by the (currently undocumented)
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.
a data.frame with additional columns
y |
predicted value |
ymin |
lower pointwise confidence interval around the mean |
ymax |
upper pointwise confidence interval around the mean |
se |
standard error |
stat_smooth
understands the following aesthetics (required aesthetics are in bold):
x
y
lm
for linear smooths, glm
for generalised linear smooths, loess
for
local smooths
c <- 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))