I don't really understand this. You seem willing only to reveal the bit of the process where you are convinced the problem is. This may not be helpful if the problem lies elsewhere.
I take it you are using the lda() function from MASS, though you don't say. Let's look at a little example: require(MASS) fm <- lda(Species ~ ., iris) nd <- iris[sample(150, 50),] pfm <- predict(fm, nd) This works fine for me. The result, pfm, is a list three components, "class", "posterior" and "x", all of which look entirely reasonable, and in particular have the right sizes. So what are you doing, really? Bill Venables http://www.cmis.csiro.au/bill.venables/ -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Gareth Campbell Sent: Tuesday, 2 September 2008 9:19 AM To: R Help Subject: [R] LDA predictions I've made an LDA model on some data from one source. I have some new data that I want to see if I can "place" to the sources in the LDA model. I used the predict function as follows: predict(wak.insitu.ld, wak.alr.alluvial) where wak.insitu.ld is an LDA model generated from some data and wak.alr.alluvial is new data of similar origin. When I look at the results, there is 86 observations which is the number in the original model, NOT in the new data (nrow=53). Am I doing this correctly. When I plot the predicted values, the plot is exactly the same as the original model plot. Now I am certain it's not doing whata I thought it was. thanks. -- Gareth Campbell PhD Candidate The University of Auckland P +649 815 3670 M +6421 256 3511 E [EMAIL PROTECTED] [EMAIL PROTECTED] [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
