ggplot2 (version 1.0.0)

stat_smooth: 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

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

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

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)

n

number of points to evaluate smoother at

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

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

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

[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "smooth")} 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"))

# 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))

lm for linear smooths, glm for generalised linear smooths, loess for local smooths

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.