Scott Zeger <[EMAIL PROTECTED]> commented: First, "diamond graphs" were developed as part of the Multi-center Aids Cohort Study, a seminal study of HIV infection in the U.S. in which these authors have been key co-investigators. The graphs were created to better address a real scientific objective and that usually bodes well for their longer-term value. I've invented a couple of graphic techniques myself. They were devised to deal with problems of actual practical interest at the time. "That usually bodes well for their longer-term value"? No, I am these days glad that I never published them, because R is chock full of *better* methods than mine.
As yet I have not had a chance to see the actual article. (Living in the Southern Hemisphere has advantages, but also disadvantages, like the time it takes periodicals to arrive.) The one example of a diamond graph I've seen did make a certain pattern in the data easy to spot, but it made it harder to spot than other graphs would have. Amongst other things, it would be very interesting to see some sort of 2d density plot with log(diastolic) and log(systolic) as axes. Perhaps this was already done in the article. First Lispstat and now R have impressed on my mind the importance of moving beyond paper. The possibility of displaying the same data in _several_ ways, simultaneously or in quick succession, means that computer graphics can be a qualitatively different medium from paper. Just this afternoon I was talking with a 4th-year CS student who is working on a project to try to find features which will enable him to find patterns in a certain kind of data. Using R, I generated some synthetic data in a couple of lines of code. Then I plotted it several different ways, scratched my head a bit, rummaged through a list of smoothing functions found using help.search, and tried something, plotted it, changed a scale factor, tried again, settled on a scale factor that seemed to work well, switched back to thinking about calculations, and in about 15 minutes, there was a technique for finding interesting change points in the data. I confused him a bit because I was switching plots faster than he could follow, so I spent the next 45 minutes explaining what I'd done. The point was that *changing* plots was qualitatively different from looking at a single plot. Now, the data displayed in the one example in the press release seemed to be (diastolic pressure bucket) x (systolic pressure bucket) -> count. As noted above, that suggests a 2d density estimate as an interesting thing. It also suggests a scatter plot (possibly with rugs). Most importantly, it suggests BOTH of them, and several others as well (such as hexbin), each of which may provide some insight that the others don't. It's very VERY hard for any one graph, especially one with a cramped dynamic range, to beat that. The real competition for the diamond graph is not some other graph, but a wide choice of graphs that can be quickly flicked through and creatively combined. This also means that a new graphic technique, if it _is_ good, is even _better_ when it can be freely creatively combined with other graphic techniques. Having diamond graphs locked out of R is bad *for* diamond graphs. In fact, the Johns Hopkins Department of Biostatistics faculty and graduates are active participants in and enthusiastic supporters of open source software development. For recent examples, see: http://www.biostat.jhsph.edu/biostat/research/software.shtml Not only that, at least one of them, R/qtl, is an R package. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help