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.geom_smooth(mapping = NULL, data = NULL, stat = "smooth",
method = "auto", formula = y ~ x, se = TRUE, position = "identity",
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, ...)
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.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. layer. There are
three types of arguments you can use here:
color = "red"orsize = 3.geom_smooth and stat_smooth.method.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.