R help - May 2013 - Generate positive definite matrix with constraints

Hi, I have a question for my simulation problem:

I would like to generate a positive (or semi def positive) covariance
matrix, non singular, in wich the spectral decomposition returns me the same
values for all dimensions but differs only in eigenvectors.

Ex.
 sigma
          [,1]   [,2]
[1,]  5.05 4.95
[2,]  4.95 5.05

 > eigen(sigma)
$values
[1] 10.0  0.1

$vectors
                    [,1]                [,2]
[1,] 0.7071068 -0.7071068
[2,] 0.7071068  0.7071068

(In theory: Using the spectral decomposition, the matrix ? can be re-written
as
? = 5 ( 1, 1)  1    + 0.05 (1, -1)   1
                       1                            -1       )
This because I would generate another covariance matrix in wich variables
are more than 2.
  Thank you



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Sorry in the previuos message I've inverted:  I would like generate ...
"same eigenvectors and different eigenvalues" 





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