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Version 1.4.0 (2001-12-19)
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# let's create a variable x that is a random sample of size 500
# from a standard normal distribution and plot the histogram of x
> x <- rnorm(500)
> hist(x)
# now let's create a variable y that is equal to exp(x) plus a normal
# random variable with mean 2.5 and variance 3 and plot y on x
> y <- exp(x) + rnorm(500, 2.5, sqrt(3))
> plot(x,y)
# we can add a loess smooth (see the Cleveland book) to this plot
# using the loess.smooth() function in the modreg package. To use the
# loess.smooth() function we first load the modreg library
> library(modreg)
# to see what functions are in the modreg package we can type
> library(help=modreg)
modreg Modern Regression: Smoothing and Local Methods
Description:
Package: modreg
Version: 1.4.0
Priority: base
Title: Modern Regression: Smoothing and Local Methods
Author: Brian Ripley.
Maintainer: R Core Team
Description: Modern regression: smoothing and local methods
License: GPL Version 2 or later.
Index:
ksmooth Kernel Regression Smoother
loess Local Polynomial Regression Fitting
loess.control Set Parameters for Loess
loess.smooth Simple Smoothing via Loess
plot.ppr Plot Ridge Functions for Projection Pursuit
Regression Fit
ppr Projection Pursuit Regression
predict.loess Predict Loess Curve or Surface
predict.smooth.spline Predict from Smoothing Spline Fit
scatter.smooth Scatter Plot with Smooth Curve Fitted by Loess
smooth.spline Fit a Smoothing Spline
supsmu Friedmans's SuperSmoother
# OK, to get help on the loess.smooth() function we could type
> ?loess.smooth
scatter.smooth package:modreg R Documentation
Scatter Plot with Smooth Curve Fitted by Loess
Description:
Plot and add a smooth curve computed by `loess' to a scatter plot.
Usage:
scatter.smooth(x, y, span = 2/3, degree = 1,
family = c("symmetric", "gaussian"),
xlab = deparse(substitute(x)), ylab = deparse(substitute(y)),
ylim = range(y, prediction$y), evaluation = 50, ...)
loess.smooth(x, y, span = 2/3, degree = 1,
family = c("symmetric", "gaussian"), evaluation=50, ...)
Arguments:
x: x coordinates for scatter plot.
y: y coordinates for scatter plot.
span: smoothness parameter for `loess'.
degree: degree of local polynomial used.
family: if `"gaussian"' fitting is by least-squares, and if
`family="symmetric"' a re-descending M estimator is used.
xlab: label for x axis.
ylab: label for y axis.
ylim: the y limits of the plot.
evaluation: number of points at which to evaluate the smooth curve.
...: graphical parameters.
Details:
`loess.smooth' is an auxiliary function.
Value:
x: x coordinates for scatter plot.
y: y coordinates for scatter plot.
span: smoothness parameter for `loess'.
degree: degree of local polynomial used.
family: if `"gaussian"' fitting is by least-squares, and if
`family="symmetric"' a re-descending M estimator is used.
xlab: label for x axis.
ylab: label for y axis.
ylim: the y limits of the plot.
evaluation: number of points at which to evaluate the smooth curve.
...: graphical parameters.
Details:
`loess.smooth' is an auxiliary function.
Value:
None.
Author(s):
B.D. Ripley
See Also:
`loess'
Examples:
data(cars)
attach(cars)
scatter.smooth(speed, dist)
detach()
# OK, so let's use this on our simulated data, since we already have
# scatterplot displayed we can use the lines() function to add a line
# corresponding to the loess smooth
> lines(loess.smooth(x,y))
Warning message:
k-d tree limited by memory. ncmax= 500
# We'll ignore the warning message for now. To get a thicker line that
# is red we could type
> lines(loess.smooth(x,y), lwd=4, col="red")
Warning message:
k-d tree limited by memory. ncmax= 500
# We can also change the span to reduce the amount of smoothing
> lines(loess.smooth(x,y, span=.3), lwd=4, col="blue")
Warning message:
k-d tree limited by memory. ncmax= 500
# Let's work with a real dataset-- the Swiss cantons data. First we
# have to load the dataframe
> data(swiss)
# let's look at the first 3 rows of the dataframe to see what
# variables are in the dataframe
> swiss[1:3,]
Fertility Agriculture Examination Education Catholic
Courtelary 80.2 17.0 15 12 9.96
Delemont 83.1 45.1 6 9 84.84
Franches-Mnt 92.5 39.7 5 5 93.40
Infant.Mortality
Courtelary 22.2
Delemont 22.2
Franches-Mnt 20.2
# now let's plot Fertility on Education
> plot(swiss$Education, swiss$Fertility)
# we could also do accomplish the same thing by first attaching the
# swiss dataframe and omitting the name of the dataframe in the call
# to the plot() function.
> attach(swiss)
> plot(Education, Fertility)
# let's add a loess smooth
> plot(Education, Fertility)
> lines(loess.smooth(Education, Fertility), lwd=2, col="red")
# we can also use R much like a pocket calculator. for instance if we
# want to calculate the natural logarithm of a number (say 3.42)
# we can do this by typing
> log(3.42)
[1] 1.229641
# similarly we can raise the number e to some power with the exp()
# function
> exp(1.229641)
[1] 3.420002
# to add a bunch of numbers we could type
> 238.4 + 8243 + 37.2 + 832.8 + 237
[1] 9588.4
# you get the picture.
# R also has facilities for evaluating a number of probability
# (density) functions, distribution functions, and quantile functions,
# and for drawing pseudo-random samples from a number of standard
# probability distributions.
#
# to take a pseudo-random sample of size 300 from a binomial
# distribution with parameters n=10 and pi=.85 and assign it to a
# variable z we could type
> z <- rbinom(300, 10, .85)
# to evaluate the binomial probability function with parameters
# n=10 and pi = .85 at the point 4 we could type
> dbinom(4, 10, .85)
[1] 0.001248655
# evaluating the distribution function of the same distribution at the
# point 4 gives us
> pbinom(4, 10, .85)
[1] 0.001383235
# Section 8 of the "An Introducation to R" available at CRAN
# http://cran.r-project.org/ provides a wealth of information about
# about probability distributions in R