Evgeniy Kachalin wrote: > Marc Schwartz (via MN) пишет: > >>>Marc Schwartz (via MN) пишет: > > >>>So plotmeans is incapable of: boxplot(numerical~fact1+fact2). Is there >>>any way further? >> >> >>I think that somehow we are talking past each other here. >> >>plotmeans() does what it is designed to do, which is to simplify the >>process of plotting group-wise point estimates and user defined error >>bars/intervals around the point estimates. >> >>In your case, these intervals would be standard deviations around each >>of the group means as you have indicated. >> >>Review the examples in ?plotmeans. >> >>As Martin and others have pointed out, you need to remove boxplots from >>the equation here, as they were not designed to plot means and standard >>deviations. >> > > > Again, what I'm talking about: plotmeans is incapable of analyzing the > formula. For example, I have two factors: A - a, b, c, and B - d, e, f. > > If i plot: boxplot(num~A+B) what do I get? Eight boxes: ad, ae, af, ba, > be, bf, cd, ce, cf. If I plot: plotmeans(num~A+B) - what do I get? > Nothing. Because plotmeans cannot combine two factors in various > combination. Is there a simple way to do it? > > Anyway... That's wrong way, all what is neccessary is to have a boxplot > with mean istead of median. Is there simple way to do it? > > Statistical software like Statistica 7.0 offers any possible combination > of what "Boxplot" could mean. Is it possible to have only one > modification to R's boxplot? > > Thank you for kind answers. > Also please tell me, where should I send replies: to conference adress > or to those who answer me directly. >
library(Hmisc) library(lattice) ?panel.bpplot bwplot(...., panel=panel.bpplot) By default, panel.bpplot shows the mean (dot) and median (line) plus several quantiles. To bother Martin in a friendly way, I think that means can be useful additions - not that they are so useful by themselves, but that when they differ a lot from the median, non-statisticians gain further information about asymmetry. Also, even though the simple box plot is elegant, I sometimes think it has a high ink to information ratio. I have gained a lot from seeing outer quantiles on the plot, and I don't like to show outer points for fear of someone labeling them outliers. For describing raw data distributions, I never find standard deviations useful, however. -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html