Introduction | Example Data Sets | References ## AbstractThis paper provides a suite of datasets from standard multivariate distributions and simple high-dimensional geomtric shapes that can be used to familiarize new users of grand tour visualizations. It contains Quicktime and Gif animations of 1-D, 2-D, 3-D, 4-D and 5-D grand tours, links to starting XGobi or XLispStat on the calibration data sets, and C code for generating a grand tour.The purpose of the paper is two-fold: providing code for the grand tour that others could pick up and modify (it is not easy to code this version which is why there are very few implementations currently available), and secondly, provide a variety of training datasets to help new users get a visual sense for high-dimensional data. ## IntroductionThe grand tour is a method for viewing multivariate data "from all sides". As originally proposed by Asimov (1985) it is a movie of data projections, where the viewer is shown a continuous sequence of
d-dimensional projections of the p-dimensional data.
The dimension of the projection can be 1, 2, 3, ... , p.
Currently there are implementations of grand tours available in XGobi
(Swayne, Cook and Buja, 1997), XLispStat (Tierney, 1991)and ExplorN
(Carr, Wegman and Luo, 1996).
Here are some examples of a grand tour running on a small seven dimensional
dataset. This is the primeval form of the grand tour,
These examples illustrate tours implemented using the algorithm in
Buja, Cook, Asimov, Hurley (1997). They are geodesic tours that
contain no "within-projection-plane" spin, which is optimal for
viewing tours where ## Example Data Sets
If you have your web browser set
up to recognize quicktime movies then you can simply click the If you have your web browser set
up to recognize files with a .xgobi extension then you can simply
click the If you have your web browser
set up to recognize files with a .xli extension as XLispStat,
then you can simply click the Compile C code to compute arbitrary dimension projection vectors for composing a grand tour and display results in S/S-Plus. ## Samples from Standard Multivariate Distributions Multivariate Normal Distributions
Samples from Long-Tailed Distributions
Samples from Skewed Distributions
## Simple Geometric Shapes
## Challenge Data SetsThese data sets can be viewed on line through an applet with the button, or downloaded to view using XGobi or XLispStat.
## AcknowledgementsThis work began with the writing of code to run a grand tour with arbitrary dimensional projections for use in the C2 Virtual Reality Lab at Iowa State University. It is possible as a result of the work in Buja, Cook, Asimov and Hurley (1997) which describes the algorithm. The work here can be viewed as an adjunct to that paper.Thanks to Dr Sigbert Klinke for valuable feedback on the material in this paper. The author was supported by National Science Foundation grants DMS9632662 and DMS9214497. ## ReferencesAsimov, D. (1985) The Grand Tour: A Tool for Viewing Multidimensional
Data, Buja, A., Cook, D., Asimov, D., Hurley, C. (1997) Dynamic
Projections in High-Dimensional Visualization: Theory and
Computational Methods, Carr, D. B. and Wegman, E. J. and Luo, Q. (1996) ExplorN: Design
Considerations Past and Present, Swayne, D. F., Cook, D., Buja, A. (1998) XGobi: Interactive Dynamic
Graphics in the X Window System, Tierney, L. (1991), |

This paper is a revision of the paper that can be found at http://www.stat.ucla.edu/journals/jss/v02/i06/

Dianne Cook, Dept of Statistics, ISU, 325 Snedecor Hall, Ames, IA 50011-1210

Tel: (515) 294 8865, Fax: (515) 294 4040

email: dicook@iastate.edu

http://www.public.iastate.edu/~dicook/

Last modified: Tue Sep 12 05:47:40 CDT 2000