> Since I believe in showing the real data, warts and all, > I avoid using contour plots Howard, I must take issue with this. Contour plots (when properly computed) do not inaccurately display bivariate data. In fact, they can be much more informative than even color (or gray-scale) dot plots, which are much more difficult for most people to readily interpret. It's not difficult to make up a series of test plots, shown in both formats, and demonstrate that the inexperienced person will more readily estimate the population frequency in a contour plot than a color-dot plot. This, ultimately, is the goal in graphical presentation of data. Of course, you will agree that dot plots are completely inappropriate. (Everyone: please stop publishing data with single-color dot plots!) You also stated that "smoothing" makes the data "look better" than it is. This is also not entirely correct--proper smoothing algorithms simply make the contour plot look like it would if you were to collect a huge number of events. In other words, proper density estimation algorithms, which are those that employ a variable kernel-width smoothing algorithm, do not distort the data presentation, and, in general, make it easier to interpret by mere humans. Dave Parks and Marty Bigos have discussed these issues at length in various chapters on data analysis (for example, in the Handbook of Experimental Immunology). The main downside of contour plots is that data outside the last contour is generally not shown. This problem has a simple solution: by showing outlying events together with contour plots: thus, the contours give you the frequency estimation that they are so good at, but the outliers will shown the low frequency events. This format combines the best qualities of both presentation styles. mr
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