Home Online help Statistical Packages SPlus Graphics
Graphics
Splus has a powerful graphics
environment. Before you can begin to plot things in Splus, you need to
open a graphics device. Opening a graphics device is as simple as typing the
name of the device and open and close parentheses. Three graphical devices that Splus can use on an Xwindows
machine include: motif(), X11(), and openlook().
Now type,
A new big graphics output window should open. We will only barely
scratch the surface of graphics for now, but this list provides a
basic introduction to several types.
 hist(x)
 Makes a frequency histogram of object x. This
command takes several subarguments:
 boxplot(x)
 Creates a boxplot for a vector or for each vector
within a data frame.
 stem(x)
 Creates a stem and leaf plot for a variable. This type of
plot is useful for clustering. Note that this does not use the
graphics device.
 plot(x,y)
 Plots variable x against variable y.
 abline()
 Plots horizontal, vertical, and regression lines in a plot
 pairs (object)
 Gives the plot for each pair of vectors
within a data frame.
 brush (object)
 Gives all bivariate and multivariate plots
for a data frame and allows you to rotate the data and other fun
stuff.
 identify(x,y)
 Interactively identifies points in a plot.
Plot
The most important of these graphics tools we will deal with today is
plot(). Here is the syntax of a basic plotting command (with a
couple added perks):
 plot(sd,ud,
xlab= "Social Democratization",
ylab= Ünion Density")
 title("Unionization and Social Democracy")
The xlab and ylab subcommands identify the labels for the
x and y axes. The title(titlename) command allows you to
input a title for the plot. There are many other options, including
varying the color (col) and the the plotting character (
pch). Type ?plot to see your options.
Adding lines: abline() and lowess() smoothing
You can look graphically represent the fit of your model graphically
using the abline() command. This command allows you to plot a
fitted line through a plot of points. For example, if you have a model
with one dependent and one independent variable, you can use it to
look visually at how well the model fits the data.^{3}.
Building on the graph we just made, lets use the abline line
command.
That's all there is to it. You should note that we can only do this
simple function with a bivariate regression. Multiple variables make a
single twodimensional plot impossible.
The second additional line you can add is a smoothed line, one that
conforms more exactly to the data than the simple
abline(). We'll use a smoother called lowess(). A lowess
smoother uses "robust local linear fits"  several regressions,
each within a particular window.
To activate this we will need the following commands:
 par(new=T,xaxs="d",yaxs="d")
 plot(lowess(sd,ud),
xlab= "",
ylab= "",
type= "b",
lty=2,
pch=2,
axes=F)
Notice several differences:
 par(new=T,xaxs= "d", yaxs= "d")
 First we have to tell
SPlus not to delete the current plot when it makes the new one  to
overlay the new plot  and to lock in the x and y axes so that we use
the same scale.
 xlab and ylab
 We tell them to be blank so as not to overwrite
new text onto our current x and yaxis labels.
 type= "b"
 This tells SPlus to plot a line, as well as points
 lty=2
 This uses a different line type from the abline command
to make it easier to distinguish the two lines.
 pch=2
 Similarly, we can change the plotting character to
distinguish these points from the points in the first plot.
 axes=F
 This is not as important because the two plots are on
the same scale, but if they were not (if we were using two xaxes) we
would need it.
