[R] Regularized Discriminant Analysis scores, anyone?
ligges at statistik.tu-dortmund.de
Sun Jun 2 16:39:30 CEST 2013
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
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.
> x <- rda(Species ~ ., data = iris, gamma = 0.05, lambda = 0.2)
> rda1<-predict(x, iris[, 1:4])
> # 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])
> 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,
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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