geom_smooth(mapping = NULL, data = NULL, stat = "smooth", position = "identity", ..., method = "auto", formula = y ~ x, se = TRUE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)stat_smooth(mapping = NULL, data = NULL, geom = "smooth", position = "identity", ..., method = "auto", formula = y ~ x, se = TRUE, n = 80, span = 0.75, fullrange = FALSE, level = 0.95, method.args = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
aes
or
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 mapping
if there is no plot mapping.NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot
.
A 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.
A function
will 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.layer
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.loess
. For datasets
with 1000 or more observations defaults to gam, see gam
for more details.y ~ x
,
y ~ poly(x, 2)
, y ~ log(x)
FALSE
(the default), removes missing values with
a warning. If TRUE
silently removes missing values.NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.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. borders
.geom_smooth
and stat_smooth
.method
.Aids the eye in seeing patterns in the presence of overplotting.
geom_smooth
and stat_smooth
are effectively aliases: they
both use the same arguments. Use geom_smooth
unless you want to
display the results with a non-standard geom.
Calculation is performed by the (currently undocumented)
predictdf
generic and its methods. For most methods the standard
error bounds are computed using the predict
method - the
exceptions are loess
which uses a t-based approximation, and
glm
where the normal confidence interval is constructed on the link
scale, and then back-transformed to the response scale.
geom_smooth
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
linetype
size
weight
ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth()# Use span to control the "wiggliness" of the default loess smoother # The span is the fraction of points used to fit each local regression: # small numbers make a wigglier curve, larger numbers make a smoother curve. ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(span = 0.3)# Instead of a loess smooth, you can use any other modelling function: ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(method = "lm", se = FALSE)ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(method = "lm", formula = y ~ splines::bs(x, 3), se = FALSE)# Smoothes are automatically fit to each group (defined by categorical # aesthetics or the group aesthetic) and for each facet ggplot(mpg, aes(displ, hwy, colour = class)) + geom_point() + geom_smooth(se = FALSE, method = "lm")ggplot(mpg, aes(displ, hwy)) + geom_point() + geom_smooth(span = 0.8) + facet_wrap(~drv)binomial_smooth <- function(...) { geom_smooth(method = "glm", method.args = list(family = "binomial"), ...) } # To fit a logistic regression, you need to coerce the values to # a numeric vector lying between 0 and 1. ggplot(rpart::kyphosis, aes(Age, Kyphosis)) + geom_jitter(height = 0.05) + binomial_smooth()Warning message: Computation failed in `stat_smooth()`: y values must be 0 <= y <= 1ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis) - 1)) + geom_jitter(height = 0.05) + binomial_smooth()ggplot(rpart::kyphosis, aes(Age, as.numeric(Kyphosis) - 1)) + geom_jitter(height = 0.05) + binomial_smooth(formula = y ~ splines::ns(x, 2))# But in this case, it's probably better to fit the model yourself # so you can exercise more control and see whether or not it's a good model