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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