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. > [[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.
