Hello, I am switching from SPSS-Windows to R-Linux. My university is very SPSS-oriented so maybe that's the cause of my problems. I am a beginner in R and my assignments are SPSS-oriented, so I hope I don't annoy anyone with my questions... Right now I've got 2 problems: -I have to evaluate discriminant functions I have calculated with lda(MASS). I can't find a measure that evaluates their significance (Wilk's lambda in my textbook (Stevens,(2002),"Applied multivariate statistics for the social sciences")and in SPSS). Is there a Wilk's lambda for discriminant functions in R? or can I use an alternative measure? or am I thinking in the wrong direction? I have searched the help-archive to find similar questions to mine but no answer to them. -My second problem: to check the assumption of multivariate homoscedasticity I have to test if the variance-covariance matrices for my variables are homogene. My textbook suggests Box's M test. I can't find this statistic in R. Again I have found similar questions in the help-archives, but no answers. Is there a way to calculate Box's M in R? Or is there an alternative way to check for multivariate homoscedasticity? Any suggestion would be greatly appreciated! Cheers, Janke ten Holt
evaluation of discriminant functions+multivariate homoscedasticity
2 messages · Janke ten Holt, Brian Ripley
These topics are not much used by trained statisticians. In particular, the tests such as 1) are so sensitive to multivariate normality as to be almost no practical use. Even if the assumptions of multivariate normality hold, the standard arguments of the robustniks hold here too: the departure from the homogeneity assumptions hurts you before statistical signifcance is reached, and it is better to act as if the homoscedasticity does not hold (so use QDA or a regularized version of it). But then multivariate normality almost never comes close to holding true outside simulation experiments. We do teach LDA and QDA, but mainly to point out that logistic discrimination is a much safer procedure. If you want to do statistics like SPSS does, I suggest you use SPSS. R is not a substitute for SPSS -- in particular it lacks a lot of legacy material that the much older packages have. But as R is highly programmable, you can add these tests if you want to.
On Tue, 20 Jan 2004, Janke ten Holt wrote:
Hello, I am switching from SPSS-Windows to R-Linux. My university is very SPSS-oriented so maybe that's the cause of my problems. I am a beginner in R and my assignments are SPSS-oriented, so I hope I don't annoy anyone with my questions... Right now I've got 2 problems: -I have to evaluate discriminant functions I have calculated with lda(MASS). I can't find a measure that evaluates their significance (Wilk's lambda in my textbook (Stevens,(2002),"Applied multivariate statistics for the social sciences")and in SPSS). Is there a Wilk's lambda for discriminant functions in R? or can I use an alternative measure? or am I thinking in the wrong direction? I have searched the help-archive to find similar questions to mine but no answer to them. -My second problem: to check the assumption of multivariate homoscedasticity I have to test if the variance-covariance matrices for my variables are homogene. My textbook suggests Box's M test. I can't find this statistic in R. Again I have found similar questions in the help-archives, but no answers. Is there a way to calculate Box's M in R? Or is there an alternative way to check for multivariate homoscedasticity? Any suggestion would be greatly appreciated! Cheers, Janke ten Holt
Brian D. Ripley, ripley at stats.ox.ac.uk 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