Dear All, I am not a statistician, and was wondering if anyone could help me with the following. Greenacre, in his Correspondence Analysis in Practice (1993, p.173) gives a method for testing the significance of an axis in CA where: $\chi^2 = \lambda \times n$ where \lambda is the the eigenvalue for the principal axis and n is the number of objects in the analysis. The value for \chi^2 is then compared to a table of critical values. The table in his book is a subset of Table 51 in Pearson and Hartley 1976, Biometrica Tables for Statisticians vol II, described as "Percentage points of the extreme roots of $|\text{\textbf{S}}\Sigma^{-1}-c\text{\textbf{I}}|=0$" Is there an easy way of doing this test in R? My main problem in that Table 51 only gives values for a maximum of a p=10, \nu = 200 table and mine are regularly much bigger than that (although it would be also nice to be able to put in the figures for lambda, n, p and \nu and get the probability back). Many thanks in advance, Kris Lockyear. Dr Kris Lockyear Institute of Archaeology 31-34 Gordon Square London phone: 020 7679 4568 email: k.lockyear at ucl.ac.uk