On Apr 21, 2012, at 19:13 , Berend Hasselman wrote:
>
> On 21-04-2012, at 16:40, peter dalgaard wrote:
>
>>
>> On Apr 21, 2012, at 15:22 , Luke Hartigan wrote:
>>
>>> Hi all,
>>>
>>> In my experience, using eigen to solve generalized eigenvalue / eigenvector
>>> problems only gives correct looking eigenvalues while the eigenvectors seem
>>> to be wrong (in comparison to results from MATLAB's 'eig' function for
>>> example).
>>
>> Could you please document that? There are many misconceptions about when
>> eigenvectors are "correct" and platform dependencies too. As far as I can
>> tell, both R and Matlab use the same LAPACK routines.
>
>
> The OP posted two matrices:
>
> A <- matrix(c(1457.738, 1053.181, 1256.953,
> 1053.181, 1213.728, 1302.838,
> 1256.953, 1302.838, 1428.269), nrow=3, byrow=TRUE)
>
> B <- matrix(c(4806.033, 1767.480, 2622.744,
> 1767.480, 3353.603, 3259.680,
> 2622.744, 3259.680, 3476.790), nrow=3, byrow=TRUE)
>
> I've tried
>
> eigen(solve(B)%*%A)
>
> (which is probably not the best way to handle this problem numerically) to
> approximate the generalized ev problem.
> And the geigen function
>
> geigen(A,B,TRUE,TRUE)
>
> The eigenvalues are identical upto the printed 9 digits but the eigenvectors
> appear to be quite different.
> Maybe this is what Luke meant.
>
> Berend
>
They look quite similar to me:
> ev <- eigen(solve(B,A) )$vectors
> ge <- geigen(A, B, TRUE , TRUE)
> ev / ge$vl
[,1] [,2] [,3]
[1,] 0.9324603 0.813422 -0.7423694
[2,] 0.9324603 0.813422 -0.7423694
[3,] 0.9324603 0.813422 -0.7423694
> ev / ge$vr
[,1] [,2] [,3]
[1,] 0.9324603 0.813422 -0.7423694
[2,] 0.9324603 0.813422 -0.7423694
[3,] 0.9324603 0.813422 -0.7423694
(and of course, eigenvectors of any sort are only defined up to a constant
multiplier)
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
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