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rplotcovariancemeannormal-distribution

R superimposing bivariate normal density (ellipses) on scatter plot

There are similar questions on the website, but I could not find an answer to this seemingly very simple problem. I fit a mixture of two gaussians on the Old Faithful Dataset:

if(!require("mixtools")) { install.packages("mixtools");  require("mixtools") }
data_f <- faithful
plot(data_f$waiting, data_f$eruptions)
data_f.k2 = mvnormalmixEM(as.matrix(data_f), k=2, maxit=100, epsilon=0.01) 
data_f.k2$mu # estimated mean coordinates for the 2 multivariate Gaussians
data_f.k2$sigma # estimated covariance matrix

I simply want to super-impose two ellipses for the two Gaussian components of the model described by the mean vectors data_f.k2$mu and the covariance matrices data_f.k2$sigma. To get something like:

enter image description here

For those interested, here is the MatLab solution that created the plot above.

Solution

  • You can use the ellipse-function from package mixtools. The initial problem was that this function swaps x and y from your plot. I'll try to figure this out and update the answe. (I'll leave the colors to somebody else...)

    plot( data_f$eruptions,data_f$waiting)
for (i in 1: length(data_f.k2$mu))  ellipse(data_f.k2$mu[[i]],data_f.k2$sigma[[i]])