From SAS System for Statistical Graphics, First Edition; Copyright(c) 1991 by SAS Institute Inc., Cary, NC, USA.

This material is provided "as is" by SAS Institute Inc. There are no warranties, express or implied, as to merchantability or fitness for a particular purpose regarding the materials or code contained herein. The Institute is not responsible for errors in this material as it now exists or will exist, nor does the Institute provide technical support for it. Questions or problem reports concerning this material may be addressed to the author, Michael Friendly, by electronic mail:

Michael Friendly <friendly@yorku.ca>
The **original versions** of the programs (if you should want them)
may be obtained by anonymous ftp from
FTP.SAS.COM. Login as user ANONYMOUS, change to the proper
directory with the command:
`
cd /techsup/download/stat
`
and use the get command to retrieve
the file `statgraf.sas` (204K)
or the file `statgraf.zip` (64K).
The file graphmac.doc contains some
basic documentation on the macros, but it is assumed you have
the book, which gives numerous examples of their use.
Note that some of the macros are maintained in two versions
to account for some differences among SAS versions and
operating systems.

The **current versions** of the programs may also be obtained from
http://www.datavis.ca/sasmac/,
where the current versions are maintained.
To obtain the programs individually by WWW, click the icons below.

The datasets used in the book are also available here in the ZIP file sssgdata.zip .

- biplot macro
- Implements the biplot technique (e.g., Gabriel, 1971) for plotting multivariate observations and variables together in a single display.
- boxanno macro
- Provides univariate marginal boxplot annotations for two-dimensional and three-dimensional scatterplots.
- boxplot macro
- Produces standard and notched boxplots for a single response variable with one or more grouping variables.
- contour macro
- Plots a bivariate scatterplot with a bivariate data ellipse for one or more groups with one or more confidence coefficients.
- corresp macro
- Performs correspondence analysis (also known as "dual scaling") on a table of frequencies in a two-way (or higher-way) classification. In V6 of SAS., this analysis is also performed by PROC CORRESP.
- density macro
- Calculates a nonparametric density estimate for histogram smoothing of a univariate data distribution. The program uses the Gaussian kernel and calculates an optimal window half-width (Silverman, 1986) if not specified by the user.
- dotplot macro
- Produces grouped and ungrouped dot charts of a single variable (Cleveland, 1984, 1985).
- lowess macro
- Performs robust, locally weighted scatterplot smoothing (Cleveland, 1979).
- nqplot macro
- Produces theoretical normal quantile-quantile (Q-Q) plots for single variable. Options provide a classical (mu, sigma) or robust (median, IQR) comparison line, standard error envelope, and a detrended plot.
- outlier macro
- Detects multivariate outliers. The OUTLIER macro calculates robust Mahalanobis distances by iterative multivariate trimming (Gnanadesikan & Kettenring, 1972; Gnanadesikan, 1977), and produces a chisquare Q-Q plot.
- partial macro
- Produces partial regression residual plots. Observations with high leverage and/or large studentized residuals can be individually labeled.
- scatmat macro
- Draws a scatterplot matrix for all pairs of variables. A classification variable may be used to assign the plotting symbol and/or color of each point.
- stars macro
- Draws a star plot of the multivariate observations in a data set. Each observation is depicted by a star-shaped figure with one ray for each variable, whose length is proportional to the size of that variable.
- symplot macro
- Produces a variety of diagnostic plots for assessing symmetry of a data distribution and finding a power transformation to make the data more symmetric.
- twoway macro
- Performs an exploratory analysis of two-way experimental design data with one observation per cell, including Tukey's (1949) one degree of freedom test for non-additivity. Two plots may be produced: a graphical display of the fit and residuals for the additive model, and a diagnostic plot for a power transformation for removable non-additivity.

Michael Friendly

friendly AT yorku DOTca My home page