[R] LDA and RDA: different training errors

[Uwe Ligges]

dominic senn wrote:
> Hello
> 
> I try to fit a LDA and RDA model to the same data, which has two classes.
> The problem now is that the training errors of the LDA model and the
> training error of the RDA model with alpha=0 are not the same. In my
> understanding this should be the case. Am I wrong? Can someone explain what
> the reason for this difference could be?


I assume lda from MASS?
If you are using rda() from package "rda", I do not know, since the help 
page is not very specific in telling which parameter means what (but I 
guess one of them should be 1).

If you choose rda() from package "klaR", the help page tells you that
gamma=0, lambda=1
should produce identical results to LDA. (lambda=1 means that the pooled 
covariance matrix is weighted with 1 while the specific covariance 
matrices are weigthed with 0.


Uwe Ligges


> Here my code:
> 
> LDA model:
> ===========
> % x is a dataframe
> tmp = lda(response ~ ., data=x)
> tmp.hat = predict(tmp)
> tab = table(x$response, tmp.hat$class)
> lda.training.err = 1 - sum(tab[row(tab)==col(tab)])/sum(tab)
> 
> RDA model:
> ===========
> % x is converted into a matrix without the response
> % variable. This matrix is then transposed
> tmp = rda(x, y, alpha=0, delta=0)
> rda.training.err =  tmp$error / dim(x)[2]                         
> 
> % The training error provided by rda.cv() is also different
> % from the training errors provided by lda() or rda()
> tmp.cv = rda.cv(tmp, x=x, y=y, nfold=10)
> tmp.cv$err / dim(x)[2] / 10
> 
> 
> Thanks a lot!
> 
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