# [R] Regularized Discriminant Analysis scores, anyone?

Uwe Ligges ligges at statistik.tu-dortmund.de
Tue Jun 4 00:00:47 CEST 2013

```On 02.06.2013 17:57, Matthew Fagan wrote:
> Thank you Dr. Ligges, i very much appreciate the quick reply.  i
> wondered if that was the case, based on the math as I (poorly)
> understood it.  However i remain confused.   page 107 from the "rrcov"
> package PDF makes me think I can derive LDA-style discriminant scores
> for a QDA:
>
> library(rrcov)
> data(iris)
> qda1<-QdaClassic(x=iris[,1:4], grouping=iris[,5])
> pred_qda<-predict(qda1, iris[,1:4])
> plotdat<-pred_qda at x
> plot(plotdat[,1], plotdat[,2])
> plot(plotdat[,2], plotdat[,3])
>
> pred_qda\$x looks like QDA discriminant scores.   No doubt you are right,
> but if you have a moment, I'd love to know what these scores are and
> what they summarize.
>
> In addition, I have run into this nice set of lengthy R code to manually
> calculate discriminant scores for a QDA:
> https://cs.uwaterloo.ca/~a2curtis/courses/2005/ML-classification.pdf
>
> None of this means i can calculate discriminant scores for a RDA, of
> course, but QDA is my back-up choice.
>
> Bottom line: am i am completely misinterpreting what I am seeing here,
> mathematically?  Or is this just the result of different ways of
> implementing QDA in R?

What you see in your code above is the result of the formula on page 2
of the cited paper. And you need one vector for each class - choosing
the max value or deciding on the classification.
This corresponds to the posterior probabilities.

You originally asked for the coefficients of the discriminant components
(i.e. direction in the space that separates the classes according to
Fisher's criterion in the best way) given in the output of lda() (and
here you will have max(dimension, number of classes - 1) of them). These
are very different from the scores you are talking about now and do not
exists for neither QDA nor RDA.

Best,
Uwe Ligges

> Regards, and thanks again,
> Matt
>
>
> On 6/2/2013 10:39 AM, Uwe Ligges wrote:
>>
>>
>> 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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