95% confidence limits in scatter plot

Question

I need to build some data points that are defined as

c (x, y, stdev_x, stdev_y)

as a scatter plot with a representation of their 95% confidence limits, for examples showing a point and one outline around it. Ideally, I would like to characterize an oval around a point, but I don’t know how to do it. I was thinking about creating patterns and building them by adding stat_density2d (), but I would need to limit the number of outlines to 1 and couldn't figure out how to do this.

require(ggplot2) n=10000 d <- data.frame(id=rep("A", n), se=rnorm(n, 0.18,0.02), sp=rnorm(n, 0.79,0.06) ) g <- ggplot (d, aes(se,sp)) + scale_x_continuous(limits=c(0,1))+ scale_y_continuous(limits=c(0,1)) + theme(aspect.ratio=0.6) g + geom_point(alpha=I(1/50)) + stat_density2d() 

Answer 1

Just found the stat_ellipse() function here (and here ) and this will take care of it perfectly.

 g + geom_point(alpha=I(1/10)) + stat_ellipse(aes(group=id), color="black") 

Various data, of course:

Answer 2

First, I saved your entire plot as an object (changed limits).

 g <- ggplot (d, aes(se,sp, group=id)) + scale_x_continuous(limits=c(0,0.5))+ scale_y_continuous(limits=c(0.5,1)) + theme(aspect.ratio=0.6) + geom_point(alpha=I(1/50)) + stat_density2d() 

With the ggplot_build() function, save all the information used for the chart. The contours are stored in the data[[2]] object.

 gg<-ggplot_build(g) str(gg$data) head(gg$data[[2]]) level xy piece group PANEL 1 10 0.1363636 0.7390318 1 1-1 1 2 10 0.1355521 0.7424242 1 1-1 1 3 10 0.1347814 0.7474747 1 1-1 1 4 10 0.1343692 0.7525253 1 1-1 1 5 10 0.1340186 0.7575758 1 1-1 1 6 10 0.1336037 0.7626263 1 1-1 1 

Only 12 contour lines, but to save only the external line, you should only multiply group=="1-1" and replace the original information.

 gg$data[[2]]<-subset(gg$data[[2]],group=="1-1") 

Then use ggplot_gtable() and grid.draw() to get your plot.

 p1<-ggplot_gtable(gg) grid.draw(p1) 

Answer 3

latticeExtra provides a panel.ellipse lattice function that calculates and outputs a confidence ellipsoid from two-dimensional data, possibly grouped by a third variable.

here I draw levels 0.65 and 0.95, suing your data.

 library(latticeExtra) xyplot(sp~se,data=d,groups=id, par.settings = list(plot.symbol = list(cex = 1.1, pch=16)), panel = function(x,y,...){ panel.xyplot(x, y,alpha=0.2) panel.ellipse(x, y, lwd = 2, col="green", robust=FALSE, level=0.65,...) panel.ellipse(x, y, lwd = 2, col="red", robust=TRUE, level=0.95,...) }) 

Answer 4

It looks like the stat_ellipse function you found is really a great solution, but here is a different (non-ggplot), write-only, using the dataEllipse from the car package.

 # some sample data n=10000 g=4 d <- data.frame(ID = unlist(lapply(letters[1:g], function(x) rep(x, n/g)))) d$x <- unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))) d$y <- unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))) # plot points with 95% normal-probability contour # default settings... library(car) with(d, dataEllipse(x, y, ID, level=0.95, fill=TRUE, fill.alpha=0.1)) 

 # with a little more effort... # random colours with alpha-blending d$col <- unlist(lapply(1:g, function (x) rep(rgb(runif(1), runif(1), runif(1), runif(1)),n/g))) # plot points first with(d, plot(x,y, col=col, pch=".")) # then ellipses over the top with(d, dataEllipse(x, y, ID, level=0.95, fill=TRUE, fill.alpha=0.1, plot.points=FALSE, add=TRUE, col=unique(col), ellipse.label=FALSE, center.pch="+")) 

Answer 5

I don't know anything about the ggplot2 library, but you can draw ellipses using a graph. Does this plot look something like what you ask?

 library(plotrix) n=10 d <- data.frame(x=runif(n,0,2),y=runif(n,0,2),seX=runif(n,0,0.1),seY=runif(n,0,0.1)) plot(d$x,d$y,pch=16,ylim=c(0,2),xlim=c(0,2)) draw.ellipse(d$x,d$y,d$seX,d$seY)