On Mon, 28 Jun 2004, Fred wrote: > I want to know if there is some easy and reliable way > to estimate the number of dominant eigenvalues > when applying PCA on sample covariance matrix.
The short answer is `no' since it depends what you want to do PCA for (and there are many possible uses). > Assume x-axis is the number of eigenvalues (1, 2, ....,n), and y-axis is the > corresponding eigenvalues (a1,a2,..., an) arranged in desceding order. > So this x-y plot will be a decreasing curve. Someone mentioned using the elbow > (knee) method > to find the point that the maximal curvature of this curve occurs. > The number at this point would be the number of dominant eigenvalues. It's not a curve! If you joins the points by line it is piecewise linear and has curvature nowhere. See ?screeplot and its references, since the plot is called a `scree plot'. It's a well known technique in all good textbooks on PCA. > But I could not find any reference papers on this idea. > Does anyone has tried this method or knows more details on this? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
