R help - Jun 2013 - Regularized Discriminant Analysis scores, anyone?

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?

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:

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

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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