lda

This is a problem relating to collinearity of data.

You are failing to give credit for lda, which is from library MASS.

I have tried using lda on a data matrix. Lda computed the predicted classes, but
gave a warning that variables were collinear.
Can anyone comment on this? I am not sure how collinearity of data would affect the
lda analysis.

It assumes that the data are from multivariate normal distributions with a
common covariance matrix. Such data cannot be collinear. Or, in the
extension of Fisher's scheme, there are many linear combinations maximizing
the quotient of variances.

What would be the correct procedure to tackle this problem?

Remove the collinearity.

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 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._

Thank you to all who replied to my question.
Collinearity of data is no longer a problem in my lda analysis. I have removed the
redundant variables.


Peter



-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !)  To: r-help-request at stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._