On Thu, 17 Jun 2004, Dr. Herwig Meschke wrote: >Why not try to avoid binning (and density plot) at all? An alternative >could be a qqplot (as a log-log-plot), e.g. > >plot(ppoints(length(x4)), x4[order(x4)], log="xy") >abline(lm(log(x4[order(x4)])~log(ppoints(length(x4)))), col="red") > >If the assumptions of uniform distribution and power transformation >y=a*x**b are true, the coefficient of lm estimates the exponent b.
Thanks, this looks very cool (although I am going to have to learn what it all means ;) However, playing with the above with the following for example... x4 <- runif(100)**4 plot(ppoints(length(x4)), x4[order(x4)], log="xy") abline(lm(log(x4[order(x4)])~log(ppoints(length(x4)))), col="red") Shows (perhaps after a few repeats) that the fitted curve is dominated by the rare events, and the rare events have the highest variance, leading to potential big errors. By uniformly binning the log transformed data you group the rarest values in the bigest bin, and can therefore get better estimates of the true slope of the curve. My problem is now a technical one of working out how to do this, so isn't too fundamental. I can post up the differences in the values (and error) of the estimated curves when I get round to doing this. Thanks again for the help, Dan. > >Herwig > > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
