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 <istazahn@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 <natemiller77@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 <istazahn@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 <natemiller77@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.
> >
> >
>

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