1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
|
% Generated by roxygen2 (4.0.1): do not edit by hand
\name{stat_smooth}
\alias{stat_smooth}
\title{Add a smoother.}
\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{
\item{method}{smoothing method (function) to use, eg. lm, glm, gam, loess,
rlm. For datasets with n < 1000 default is \code{\link{loess}}. For datasets
with 1000 or more observations defaults to gam, see \code{\link[mgcv]{gam}}
for more details.}
\item{formula}{formula to use in smoothing function, eg. \code{y ~ x},
\code{y ~ poly(x, 2)}, \code{y ~ log(x)}}
\item{se}{display confidence interval around smooth? (TRUE by default, see
level to control}
\item{fullrange}{should the fit span the full range of the plot, or just
the data}
\item{level}{level of confidence interval to use (0.95 by default)}
\item{n}{number of points to evaluate smoother at}
\item{na.rm}{If \code{FALSE} (the default), removes missing values with
a warning. If \code{TRUE} silently removes missing values.}
\item{...}{other arguments are passed to smoothing function}
\item{mapping}{The aesthetic mapping, usually constructed with
\code{\link{aes}} or \code{\link{aes_string}}. Only needs to be set
at the layer level if you are overriding the plot defaults.}
\item{data}{A layer specific dataset - only needed if you want to override
the plot defaults.}
\item{geom}{The geometric object to use display the data}
\item{position}{The position adjustment to use for overlappling points
on this layer}
}
\value{
a data.frame with additional columns
\item{y}{predicted value}
\item{ymin}{lower pointwise confidence interval around the mean}
\item{ymax}{upper pointwise confidence interval around the mean}
\item{se}{standard error}
}
\description{
Aids the eye in seeing patterns in the presence of overplotting.
}
\details{
Calculation is performed by the (currently undocumented)
\code{predictdf} generic function and its methods. For most methods
the confidence bounds are computed using the \code{\link{predict}}
method - the exceptions are \code{loess} which uses a t-based
approximation, and for \code{glm} where the normal confidence interval
is constructed on the link scale, and then back-transformed to the response
scale.
}
\section{Aesthetics}{
\Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "smooth")}
}
\examples{
\donttest{
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))
}
}
\seealso{
\code{\link{lm}} for linear smooths,
\code{\link{glm}} for generalised linear smooths,
\code{\link{loess}} for local smooths
}
|
