# [R-pkg-devel] mvrnorm, eigen, tests, and R CMD check

Facundo Muñoz f@muvie @ending from gm@il@com
Fri May 18 10:50:18 CEST 2018

```In my opinion, the underlying problem is that you are checking whether
the test reproduces exactly your pre-computed solution, while there

I believe you want to check whether the sub-spaces are the same, not
whether the bases are identical (which can depend on platform, linear
algebra library, etc.)

ƒacu.-

On 05/17/2018 09:30 PM, Kevin Coombes wrote:
> Yes; I'm pretty sure that it is exactly the repeated eigenvalues that are
> the issue. The matrices I am using are all nonsingular, and the various
> algorithms have no problem computing the eigenvalues correctly (up to
> numerical errors that I can bound and thus account for on tests by rounding
> appropriately). But an eigenvalue of multiplicity M has an M-dimensional
> eigenspace with no preferred basis. So, any M-dimensional  (unitary) change
> of basis is permitted. That's what give rise to the lack of reproducibility
> across architectures. The choice of basis appears to use different
> heuristics on 32-bit windows than on 64-bit Windows or Linux machines. As a
> result, I can't include the tests I'd like as part of a CRAN submission.
>
> On Thu, May 17, 2018, 2:29 PM William Dunlap <wdunlap at tibco.com> wrote:
>
>> Your explanation needs to be a bit more general in the case of identical
>> eigenvalues - each distinct eigenvalue has an associated subspace, whose
>> dimension is the number repeats of that eigenvalue and the eigenvectors for
>> that eigenvalue are an orthonormal basis for that subspace.  (With no
>> repeated eigenvalues this gives your 'unique up to sign'.)
>>
>> E.g., for the following 5x5 matrix with two eigenvalues of 1 and two of 0
>>
>>   > x <- tcrossprod( cbind(c(1,0,0,0,1),c(0,1,0,0,1),c(0,0,1,0,1)) )
>>   > x
>>        [,1] [,2] [,3] [,4] [,5]
>>   [1,]    1    0    0    0    1
>>   [2,]    0    1    0    0    1
>>   [3,]    0    0    1    0    1
>>   [4,]    0    0    0    0    0
>>   [5,]    1    1    1    0    3
>> the following give valid but different (by more than sign) eigen vectors
>>
>> e1 <- structure(list(values = c(4, 1, 0.999999999999999, 0,
>> -2.22044607159862e-16
>> ), vectors = structure(c(-0.288675134594813, -0.288675134594813,
>> -0.288675134594813, 0, -0.866025403784439, 0, 0.707106781186547,
>> -0.707106781186547, 0, 0, 0.816496580927726, -0.408248290463863,
>> -0.408248290463863, 0, -6.10622663543836e-16, 0, 0, 0, -1, 0,
>> -0.5, -0.5, -0.5, 0, 0.5), .Dim = c(5L, 5L))), .Names = c("values",
>> "vectors"), class = "eigen")
>> e2 <- structure(list(values = c(4, 1, 1, 0, -2.29037708937563e-16),
>>     vectors = structure(c(0.288675134594813, 0.288675134594813,
>>     0.288675134594813, 0, 0.866025403784438, -0.784437556312061,
>>     0.588415847923579, 0.196021708388481, 0, 4.46410900710223e-17,
>>     0.22654886208902, 0.566068420404321, -0.79261728249334, 0,
>>     -1.11244069540181e-16, 0, 0, 0, -1, 0, -0.5, -0.5, -0.5,
>>     0, 0.5), .Dim = c(5L, 5L))), .Names = c("values", "vectors"
>> ), class = "eigen")
>>
>> I.e.,
>>> all.equal(crossprod(e1\$vectors), diag(5), tol=0)
>> [1] "Mean relative difference: 1.407255e-15"
>>> all.equal(crossprod(e2\$vectors), diag(5), tol=0)
>> [1] "Mean relative difference: 3.856478e-15"
>>> all.equal(e1\$vectors %*% diag(e1\$values) %*% t(e1\$vectors), x, tol=0)
>> [1] "Mean relative difference: 1.110223e-15"
>>> all.equal(e2\$vectors %*% diag(e2\$values) %*% t(e2\$vectors), x, tol=0)
>> [1] "Mean relative difference: 9.069735e-16"
>>
>>> e1\$vectors
>>            [,1]       [,2]          [,3] [,4] [,5]
>> [1,] -0.2886751  0.0000000  8.164966e-01    0 -0.5
>> [2,] -0.2886751  0.7071068 -4.082483e-01    0 -0.5
>> [3,] -0.2886751 -0.7071068 -4.082483e-01    0 -0.5
>> [4,]  0.0000000  0.0000000  0.000000e+00   -1  0.0
>> [5,] -0.8660254  0.0000000 -6.106227e-16    0  0.5
>>> e2\$vectors
>>           [,1]          [,2]          [,3] [,4] [,5]
>> [1,] 0.2886751 -7.844376e-01  2.265489e-01    0 -0.5
>> [2,] 0.2886751  5.884158e-01  5.660684e-01    0 -0.5
>> [3,] 0.2886751  1.960217e-01 -7.926173e-01    0 -0.5
>> [4,] 0.0000000  0.000000e+00  0.000000e+00   -1  0.0
>> [5,] 0.8660254  4.464109e-17 -1.112441e-16    0  0.5
>>
>>
>>
>>
>>
>> Bill Dunlap
>> TIBCO Software
>> wdunlap tibco.com
>>
>> On Thu, May 17, 2018 at 10:14 AM, Martin Maechler <
>> maechler at stat.math.ethz.ch> wrote:
>>
>>>>>>>> Duncan Murdoch ....
>>>>>>>>     on Thu, 17 May 2018 12:13:01 -0400 writes:
>>>     > On 17/05/2018 11:53 AM, Martin Maechler wrote:
>>>     >>>>>>> Kevin Coombes ... on Thu, 17
>>>     >>>>>>> May 2018 11:21:23 -0400 writes:
>>>
>>>     >>    [..................]
>>>
>>>     >> > [3] Should the documentation (man page) for "eigen" or
>>>     >> > "mvrnorm" include a warning that the results can change
>>>     >> > from machine to machine (or between things like 32-bit and
>>>     >> > 64-bit R on the same machine) because of difference in
>>>     >> > linear algebra modules? (Possibly including the statement
>>>     >> > that "set.seed" won't save you.)
>>>
>>>     >> The problem is that most (young?) people do not read help
>>>     >> pages anymore.
>>>     >>
>>>     >> help(eigen) has contained the following text for years,
>>>     >> and in spite of your good analysis of the problem you
>>>     >> seem to not have noticed the last semi-paragraph:
>>>     >>
>>>     >>> Value:
>>>     >>>
>>>     >>> The spectral decomposition of ‘x’ is returned as a list
>>>     >>> with components
>>>     >>>
>>>     >>> values: a vector containing the p eigenvalues of ‘x’,
>>>     >>> sorted in _decreasing_ order, according to ‘Mod(values)’
>>>     >>> in the asymmetric case when they might be complex (even
>>>     >>> for real matrices).  For real asymmetric matrices the
>>>     >>> vector will be complex only if complex conjugate pairs
>>>     >>> of eigenvalues are detected.
>>>     >>>
>>>     >>> vectors: either a p * p matrix whose columns contain the
>>>     >>> eigenvectors of ‘x’, or ‘NULL’ if ‘only.values’ is
>>>     >>> ‘TRUE’.  The vectors are normalized to unit length.
>>>     >>>
>>>     >>> Recall that the eigenvectors are only defined up to a
>>>     >>> constant: even when the length is specified they are
>>>     >>> still only defined up to a scalar of modulus one (the
>>>     >>> sign for real matrices).
>>>     >>
>>>     >> It's not a warning but a "recall that" .. maybe because
>>>     >> the author already assumed that only thorough users would
>>>     >> read that and for them it would be a recall of something
>>>     >> they'd have learned *and* not entirely forgotten since
>>>     >> ;-)
>>>     >>
>>>
>>>     > I don't think you're really being fair here: the text in
>>>     > ?eigen doesn't make clear that eigenvector values are not
>>>     > reproducible even within the same version of R, and
>>>     > there's nothing in ?mvrnorm to suggest it doesn't give
>>>     > reproducible results.
>>>
>>> Ok, I'm sorry ... I definitely did not want to be unfair.
>>>
>>> I've always thought the remark in eigen was sufficient,  but I'm
>>> probably wrong and we should add text explaining that it
>>> practically means that eigenvectors are only defined up to sign
>>> switches (in the real case) and hence results depend on the
>>> underlying {Lapack + BLAS} libraries and therefore are platform
>>> dependent.
>>>
>>> Even further, we could consider (optionally, by default FALSE)
>>> using defining a deterministic scheme for postprocessing the current
>>> output of eigen such that at least for the good cases where all
>>> eigenspaces are 1-dimensional, the postprocessing would result
>>> in reproducible signs, by e.g., ensuring the first non-zero
>>> entry of each eigenvector to be positive.
>>>
>>> MASS::mvrnorm()  and  mvtnorm::rmvnorm() both use "eigen",
>>> whereas mvtnorm::rmvnorm()  *does* have  method = "chol" which
>>> AFAIK does not suffer from such problems.
>>>
>>> OTOH, the help page of MASS::mvrnorm() mentions the Cholesky
>>> alternative but prefers eigen for better stability (without
>>> saying more).
>>>
>>> In spite of that, my personal recommendation would be to use
>>>
>>>   mvtnorm::rmvnorm(.., method = "chol")
>>>
>>> { or the 2-3 lines of R code to the same thing without an extra package,
>>>   just using rnorm(), chol() and simple matrix operations }
>>>
>>> because in simulations I'd expect the var-cov matrix  Sigma to
>>> be far enough away from singular for chol() to be stable.
>>>
>>> Martin
>>>
>>> ______________________________________________
>>> R-package-devel at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-package-devel
>>>
>>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-package-devel at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-package-devel

[[alternative HTML version deleted]]

```