# geom_smooth

##### Smoothed conditional means

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 `stat_smooth()`

if you want to
display the results with a non-standard geom.

##### Usage

```
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)

##### Arguments

- mapping
Set of aesthetic mappings created by

`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.- data
The data to be displayed in this layer. There are three options:

If

`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. A`function`

can be created from a`formula`

(e.g.`~ head(.x, 10)`

).- position
Position adjustment, either as a string, or the result of a call to a position adjustment function.

- ...
Other arguments passed on to

`layer()`

. These are often aesthetics, used to set an aesthetic to a fixed value, like`colour = "red"`

or`size = 3`

. They may also be parameters to the paired geom/stat.- method
Smoothing method (function) to use, accepts either a character vector, e.g.

`"auto"`

,`"lm"`

,`"glm"`

,`"gam"`

,`"loess"`

or a function, e.g.`MASS::rlm`

or`mgcv::gam`

,`stats::lm`

, or`stats::loess`

.For

`method = "auto"`

the smoothing method is chosen based on the size of the largest group (across all panels).`stats::loess()`

is used for less than 1,000 observations; otherwise`mgcv::gam()`

is used with`formula = y ~ s(x, bs = "cs")`

. Somewhat anecdotally,`loess`

gives a better appearance, but is \(O(N^{2})\) in memory, so does not work for larger datasets.If you have fewer than 1,000 observations but want to use the same

`gam()`

model that`method = "auto"`

would use, then set`method = "gam", formula = y ~ s(x, bs = "cs")`

.- formula
Formula to use in smoothing function, eg.

`y ~ x`

,`y ~ poly(x, 2)`

,`y ~ log(x)`

- se
Display confidence interval around smooth? (

`TRUE`

by default, see`level`

to control.)- na.rm
If

`FALSE`

, the default, missing values are removed with a warning. If`TRUE`

, missing values are silently removed.- show.legend
logical. Should this layer be included in the legends?

`NA`

, the default, includes if any aesthetics are mapped.`FALSE`

never includes, and`TRUE`

always includes. It can also be a named logical vector to finely select the aesthetics to display.- inherit.aes
If

`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, stat
Use to override the default connection between

`geom_smooth()`

and`stat_smooth()`

.- n
Number of points at which to evaluate smoother.

- span
Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines.

- 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).

- method.args
List of additional arguments passed on to the modelling function defined by

`method`

.

##### Details

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.

##### Aesthetics

`geom_smooth()`

understands the following aesthetics (required aesthetics are in bold):

`x`

`y`

`alpha`

`colour`

`fill`

`group`

`linetype`

`size`

`weight`

`ymax`

`ymin`

Learn more about setting these aesthetics in `vignette("ggplot2-specs")`

.

##### Computed variables

- y
predicted value

- ymin
lower pointwise confidence interval around the mean

- ymax
upper pointwise confidence interval around the mean

- se
standard error

##### See Also

See individual modelling functions for more details:
`lm()`

for linear smooths,
`glm()`

for generalised linear smooths, and
`loess()`

for local smooths.

##### Examples

```
# NOT RUN {
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)
# Smooths 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)
# }
# NOT RUN {
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()
ggplot(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.
# }
```

*Documentation reproduced from package ggplot2, version 3.2.0, License: GPL-2 | file LICENSE*