geom_smooth {animint2}  R Documentation 
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 nonstandard geom.
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
)
mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
position 
Position adjustment, either as a string, or the result of a call to a position adjustment function. 
... 
other arguments passed on to 
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 
na.rm 
If 
show.legend 
logical. Should this layer be included in the legends?

inherit.aes 
If 
geom, stat 
Use to override the default connection between

n 
number of points to evaluate smoother at 
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 
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 tbased approximation, and
glm
where the normal confidence interval is constructed on the link
scale, and then backtransformed to the response scale.
geom_smooth
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
linetype
size
weight
predicted value
lower pointwise confidence interval around the mean
upper pointwise confidence interval around the mean
standard error
See individual modelling functions for more details:
lm
for linear smooths,
glm
for generalised linear smooths,
loess
for local smooths
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()
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