The Visual Design and Control of Trellis Display

Abstract Trellis display is a framework for the visualization of data. Its most prominent aspect is an overall visual design, reminiscent of a garden trelliswork, in which panels are laid out into rows, columns, and pages. On each panel of the trellis, a subset of the data is graphed by a display method such as a scatterplot, curve plot, boxplot, 3-D wireframe, normal quantile plot, or dot plot. Each panel shows the relationship of certain variables conditional on the values of other variables. A number of display methods employed in the visual design of Trellis display enable it to succeed in uncovering the structure of data even when the structure is quite complicated. For example, Trellis display provides a powerful mechanism for understanding interactions in studies of how a response depends on explanatory variables. Three examples demonstrate this; in each case, we make important discoveries not appreciated in the original analyses. Several control methods are also essential to Trellis display. A con...

[1]  F. J. Anscombe,et al.  Computing in statistical science through APL , 1981, Springer series in statistics.

[2]  Cuthbert Daniel,et al.  Applications of Statistics to Industrial Experimentation: Daniel/Applications , 1976 .

[3]  Matthew O. Ward,et al.  Exploring N-dimensional databases , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[4]  Peter L. Brooks,et al.  Visualizing data , 1997 .

[5]  David R. Brillinger,et al.  The collected works of John W. Tukey , 1984 .

[6]  Dean P. Foster,et al.  Fitting Equations to Data , 1998 .

[7]  Allan R. Wilks,et al.  The new S language: a programming environment for data analysis and graphics , 1988 .

[8]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[9]  Edward R. Tufte,et al.  The Visual Display of Quantitative Information , 1986 .

[10]  J. W. Gorman,et al.  Fitting Equations to Data. , 1973 .

[11]  Peter J. Diggle,et al.  Computing in statistical science through APL , 1981, Springer Series in Statistics.

[12]  Karl A. Fox,et al.  Methods of Correlation and Regression Analysis. , 1960 .

[13]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[14]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[15]  H. K. Hayes,et al.  Statistical Determination of Barley Varietal Adaptation1 , 1934 .

[16]  J. I.  The Design of Experiments , 1936, Nature.

[17]  Ted Mihalisin,et al.  Visualization and analysis of multi-variate data: a technique for all fields , 1991, Proceeding Visualization '91.

[18]  Steven K. Feiner,et al.  Worlds within worlds: metaphors for exploring n-dimensional virtual worlds , 1990, UIST '90.

[19]  References , 1971 .

[20]  Ted Mihalisin,et al.  Visualizing multivariate functions, data, and distributions , 1991, IEEE Computer Graphics and Applications.

[21]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[22]  Karl A. Fox,et al.  Methods of Correlation and Regression Analysis. , 1930 .

[23]  W. Cleveland.  The Collected Works of John W. Tukey, Volume V, Graphics 1965-1985. , 1989 .