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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 X-windows machine include: motif(), X11(), and openlook().

Now type,

• motif()
  

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")

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.

• abline(out1)

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 two-dimensional 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)

par(new=T,xaxs= "d", yaxs= "d")

First we have to tell S-Plus 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 y-axis labels.

type= "b"

This tells S-Plus 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 x-axes) we would need it.