Regularized Discriminant Analysis scores, anyone?
On 02.06.2013 05:01, Matthew Fagan wrote:
Hi all, I am attempting to do Regularized Discriminant Analysis (RDA) on a large dataset, and I want to extract the RDA discriminant score matrix. But the predict function in the "klaR" package, unlike the predict function for LDA in the "MASS" package, doesn't seem to give me an option to extract the scores. Any suggestions?
There are no such scores: same as for qda, you do not follow the Fisher idea of the linear discriminant components any more: Your space is now partitioned by ellipsoid like structures based on the estimation of the inner-class covariance matrices. rda as implemented in klaR (see the reference given on the help page) is a regularization that helps to overcome problems when estimating non-singular covariance matrices for the separate classes.
i have already tried (and failed; ran out of 16 GB of memory) to do this with the "rda" package: don't know why, but the klaR package seems to be much more efficient with memory. I have included an example below:
The rda package provides a completely different regularization technique, see the reference given on the help page. Best, Uwe Ligges
library(klaR) library(MASS) data(iris) x <- rda(Species ~ ., data = iris, gamma = 0.05, lambda = 0.2) rda1<-predict(x, iris[, 1:4]) str(rda1) # This gets you an object with posterior probabilities and classes, but no discriminant scores! # if you run lda y <- lda(Species ~ ., data = iris) lda1<-predict(y, iris[, 1:4]) str(lda1) head(lda1$x) # gets you the discriminant scores for the LDA. But how to do this for RDA? # curiously, the QDA function in MASS has this same problem, although you can get around it using the rrcov package. Regards, and thank very much for any help, Matt
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