Hi Andy Great and thanks a lot! Yes, it is the package from Prof. Wehrens. So I just run the PLS like a Logistic Regression, coding the endogenous variable as binary. So no need of specifying a binary-link function (as we have to when using glm)? And yes of course: I need the LVs which give the best error rate. What do you mean by "discretize the predictions in {0, 1}"? Does this mean I assign a prediction either a 0 (if predicted values <=0.5) or a 1 if the predicted value is >0.5? I need to dive into the package tomorrow, so that I better understand the material, but is there any way of calculating e.g. a leaving-one-out cross-validation error?
Thanks and best regards Christoph On Wed, 2003-09-10 at 21:50, Liaw, Andy wrote: > Do you mean the pls.pcr package by Prof. Wehrens? This is what I do: > > o Code the two groups as 0s and 1s (numeric, not factor). > > o Run PLS as usual. Cases with predicted values > 0.5 get > classified as 1s, otherwise as 0s. > > o Note that you need to modify the code inside the mvr() > function a bit if you want to use the built-in selection > of number of LVs: It selects the number that gives the > best MSE, but what you really want is the number that > gives the best error rate. One trick is to discretize > the predictions in {0, 1}, then the "MSE" will be error > rate. > > There are better ways to do this, but this works fairly well. > > HTH, > Andy > > > -----Original Message----- > > From: Christoph Lehmann [mailto:[EMAIL PROTECTED] > > Sent: Wednesday, September 10, 2003 1:38 PM > > To: [EMAIL PROTECTED] > > Subject: [R] PLS LDA > > > > > > Dear R experts > > I saw and downloaded the fresh pls package for R. Is there > > any way of using this pls package for PLS discriminant > > analysis? If not, is there any other package available. > > > > I need a way of classifying objects into e.g. two groups, > > where nbr_observations << nbr_variables > > > > many thanks for your kind help > > > > Christoph > > -- > > Christoph Lehmann <[EMAIL PROTECTED]> > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo> /r-help > > > > ------------------------------------------------------------------------------ > Notice: This e-mail message, together with any attachments,...{{dropped}} > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help -- Christoph Lehmann <[EMAIL PROTECTED]> ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help