> My understanding is that this represents bivariate normal > approximation of the data which uses the kernel density function to > test for inclusion within a level set. (please correct me)
You can fit a bivariate normal distribution by computing five parameters. Two means, two standard deviations (or two variances) and one correlation (or covariance) coefficient. The bivariate normal *has* elliptical contours. A kernel density estimate is usually regarded as an estimate of an unknown density function. Often they use a normal (or Gaussian) kernel, but I wouldn't describe them as normal approximations. In general, bivariate kernel density estimates do *not* have elliptical contours. But in saying that, if the data is close to normality, then contours will be close to elliptical. Kernel density estimates do not test for inclusion, as such. (But technically, there are some exceptions to that). I'm not sure what you're trying to achieve here. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
