On Fri, Mar 9, 2012 at 9:28 PM, Michael <comtech....@gmail.com> wrote: > Thanks a lot Mike! >
Michael if you don't mind. (Though admittedly it leads to some degree of confusion in a conversation like this) > Could you please explain your code a bit? Which part? > > My imagination is that for each bin, I am plotting a line which is the > quantile of the y-values in that bin? Oh, so you want a qqnorm()-esque line? How is that like a scatterplot? ....yes, that's something else entirely (and not clear from your first post -- to my ear the "quantile" is a statistic tied to the [e]cdf) This is actually much easier in ggplot (and certainly doable in base as well) Try this, DAT <- data.frame(x = runif(1000, 0, 20), y = rnorm(1000)) # Not so volatile this time DAT$xbin <- with(DAT, cut(x, seq(0, 20, 5))) library(ggplot2) p <- ggplot(DAT) + facet_wrap( ~ xbin) + stat_qq(aes(sample = y)) print(p) If this isn't what you want, please spend some time to show an example of the sort of graph you desire (it can be a bit of code or a link to a picture or even a hand sketch hosted somewhere online) Out on a limb, I think you might really be thinking of something more like this: p <- ggplot(DAT) + facet_wrap( ~ xbin) + geom_step(aes(x = seq_along(y), y = sort(y))) and see this for more: http://had.co.nz/ggplot2/geom_step.html Michael Weylandt > > I ran your program but couldn't figure out the meaning of the dots in your > plot? > > Thanks again! > > On Fri, Mar 9, 2012 at 7:07 PM, R. Michael Weylandt > <michael.weyla...@gmail.com> wrote: >> >> That doesn't really seem to make sense to me as a graphical >> representation (transforming adjacent y values differently), but if >> you really want to do so, here's what I'd do if I understand your goal >> (the preprocessing is independent of the graphics engine): >> >> DAT <- data.frame(x = runif(1000, 0, 20), y = rcauchy(1000)^2) # Nice >> and volatile! >> >> # split y based on some x binning and assign empirical quantiles of each >> group >> >> DAT$yquant <- with(DAT, ave(y, cut(x, seq(0, 20, 5)), FUN = >> function(x) ecdf(x)(x))) >> >> # BASE >> plot(yquant ~ x, data = DAT) >> >> # ggplot2 >> library(ggplot2) >> >> p <- ggplot(DAT, aes(x = x, y = yquant)) + geom_point() >> print(p) >> >> Michael Weylandt >> >> PS -- I see Josh Wiley just responded pointing out your requirements >> #1 and #2 are incompatible: I've used 1 here. >> >> On Fri, Mar 9, 2012 at 7:37 PM, Michael <comtech....@gmail.com> wrote: >> > Hi all, >> > >> > I am trying hard to do the following and have already spent a few hours >> > in >> > vain: >> > >> > I wanted to do the scatter plot. >> > >> > But given the high dispersion on those dots, I would like to bin the >> > x-axis >> > and then for each bin of the x-axis, plot the quantiles of the y-values >> > of >> > the data points in each bin: >> > >> > 1. Uniform bin size on the x-axis; >> > 2. Equal number of observations in each bin; >> > >> > How to do that in R? I guess for the sake of prettyness, I'd better do >> > it >> > in ggplot2? >> > >> > Thank you! >> > >> > [[alternative HTML version deleted]] >> > >> > ______________________________________________ >> > R-help@r-project.org mailing list >> > https://stat.ethz.ch/mailman/listinfo/r-help >> > PLEASE do read the posting guide >> > http://www.R-project.org/posting-guide.html >> > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
