R: Add a smoother.

stat_smooth {ggplot2}

R Documentation

Add a smoother.

Description

Aids the eye in seeing patterns in the presence of overplotting.

Usage

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, ...)

Arguments

`mapping`	The aesthetic mapping, usually constructed with `aes` or `aes_string`. Only needs to be set at the layer level if you are overriding the plot defaults.
`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
`method`	smoothing method (function) to use, eg. lm, glm, gam, loess, rlm. For datasets with n < 1000 default is `loess`. For datasets with 1000 or more observations defaults to gam, see `gam` for more details.
`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
`n`	number of points to evaluate smoother at
`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)
`na.rm`	If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`...`	other arguments are passed to smoothing function

Details

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.

Value

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

Aesthetics

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

Examples


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"))


## Not run: 
# 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))

## End(Not run)

[Package ggplot2 version 1.0.1 Index]