Hi Nate, I infer from the stat_density2d documentation that the calculation is carried out by the kde2d function in the MASS package. Refer to ?kde2d for details.
Best, Ista On Mon, Jan 28, 2013 at 3:56 PM, Nathan Miller <natemille...@gmail.com> wrote: > Hi Ista, > > Thanks. That does look pretty nice and I hadn't realized that was possible. > Do you know how to extract information regarding those curves? I'd like to > be able to report something about what portion of the data they encompass or > really any other feature about them in a figure legend. I'll look into > stat_density2d and see if I can determine how they are set. > > Thanks for your help, > > Nate > > > On Mon, Jan 28, 2013 at 12:37 PM, Ista Zahn <istaz...@gmail.com> wrote: >> >> Hi Nate, >> >> You can make it less busy using the bins argument. This is not >> documented, except in the examples to stat_contour, but try >> >> ggplot(data=data, aes(x, y, colour=(factor(level)), fill=level))+ >> geom_point()+ >> stat_density2d(bins=2) >> >> HTH, >> Ista >> >> On Mon, Jan 28, 2013 at 2:43 PM, Nathan Miller <natemille...@gmail.com> >> wrote: >> > Thanks Ista, >> > >> > I have played a bit with stat_density2d as well. It doesn't completely >> > capture what I am looking for and ends up being quite busy at the same >> > time. >> > I'm looking for a way of helping those looking that the figure to see >> > the >> > broad patterns of where in the x/y space the data from different groups >> > are >> > distributed. Using the 95% CI type idea is so that I don't end up >> > arbitrarily drawing circles around each set of points. I appreciate your >> > direction though. >> > >> > Nate >> > >> > >> > On Mon, Jan 28, 2013 at 10:50 AM, Ista Zahn <istaz...@gmail.com> wrote: >> >> >> >> Hi Nathan, >> >> >> >> This only fits some of your criteria, but have you looked at >> >> ?stat_density2d? >> >> >> >> Best, >> >> Ista >> >> >> >> On Mon, Jan 28, 2013 at 12:53 PM, Nathan Miller >> >> <natemille...@gmail.com> >> >> wrote: >> >> > Hi all, >> >> > >> >> > I have been looking for means of add a contour around some points in >> >> > a >> >> > scatterplot as a means of representing the center of density for of >> >> > the >> >> > data. I'm imagining something like a 95% confidence estimate drawn >> >> > around >> >> > the data. >> >> > >> >> > So far I have found some code for drawing polygons around the data. >> >> > These >> >> > look nice, but in some cases the polygons are strongly influenced by >> >> > outlying points. Does anyone have a thought on how to draw a contour >> >> > which >> >> > is more along the lines of a 95% confidence space? >> >> > >> >> > I have provided a working example below to illustrate the drawing of >> >> > the >> >> > polygons. As I said I would rather have three "ovals"/95% contours >> >> > drawn >> >> > around the points by "level" to capture the different density >> >> > distributions >> >> > without the visualization being heavily influenced by outliers. >> >> > >> >> > I have looked into the code provided here from Hadley >> >> > >> >> > https://groups.google.com/forum/?fromgroups=#!topic/ggplot2/85q4SQ9q3V8 >> >> > using the mvtnorm package and the dmvnorm function, but haven't been >> >> > able >> >> > to get it work for my data example. The calculated densities are >> >> > always >> >> > zero (at this step of Hadley's code: dgrid$dens <- >> >> > dmvnorm(as.matrix(dgrid), ex_mu, ex_sigma) ) >> >> > >> >> > I appreciate any assistance. >> >> > >> >> > Thanks, >> >> > Nate >> >> > >> >> > x<-c(seq(0.15,0.4,length.out=30),seq(0.2,0.6,length.out=30), >> >> > seq(0.4,0.6,length.out=30)) >> >> > >> >> > >> >> > y<-c(0.55,x[1:29]+0.2*rnorm(29,0.4,0.3),x[31:60]*rnorm(30,0.3,0.1),x[61:90]*rnorm(30,0.4,0.25)) >> >> > data<-data.frame(level=c(rep(1, 30),rep(2,30), rep(3,30)), x=x,y=y) >> >> > >> >> > >> >> > find_hull <- function(data) data[chull(data$x, data$y), ] >> >> > hulls <- ddply(data, .(level), find_hull) >> >> > >> >> > fig1 <- ggplot(data=data, aes(x, y, colour=(factor(level)), >> >> > fill=level))+geom_point() >> >> > fig1 <- fig1 + geom_polygon(data=hulls, alpha=.2) >> >> > fig1 >> >> > >> >> > [[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.
