On September 10, 2003 04:03 pm, Kevin S. Van Horn wrote: > > Your method looks like a naive reimplementation of integration, and > won't work so well for distributions that have the great majority of the > probability mass concentrated in a small fraction of the sample space. > I was hoping for something that would retain the adaptability of > integrate().
Yesterday, I've suggested to use approxfun(). Did you consider my suggestion? Below is an example. N <- 500 x <- rexp(N) y <- rank(x)/(N+1) empCDF <- approxfun(x,y) xvals <- seq(0,4,.01) plot(xvals,empCDF(xvals),type="l", xlab="Quantile",ylab="Cumulative Distribution Function") lines(xvals,pexp(xvals),lty=2) legend(2,.4,c("Empirical CDF","Exact CDF"),lty=1:2) It's possible to tune in some parameters in approxfun() to better match your personal preferences. Have a look at help(approxfun) for details. HTH, Jerome Asselin ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help