I am trying to simulate draws from a multivariate normal using mvrnorm, and
am getting the following error message:
 
    Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : 
            non-conformable arrays

I do not understand why I am getting this message, since the vector of means
I am giving to the function is 13 by 1 and the variance matrix I am giving
to the function is 13 by 13...these matrices are therefore conformable,
right?
 
Is it possible that this is actually a problem with the
positive-definiteness of the variance matrix?  The matrix I am attempting to
use has eigenvalues that are close to zero (but positive).
 
Here's a transcript of the commands I've tried:
 
> mvrnorm(n = 1000,B,V)
Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : 
        non-conformable arrays
> mvrnorm(n = 1000,t(B),V)
Error in mu + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*% t(X) : 
        non-conformable arrays

> V
               [,1]          [,2]          [,3]         [,4]          [,5]
 [1,]  2.632324e+05 -1.286920e-01 -9.461830e-02  6.27173e-02 -2.625664e+00
 [2,] -1.286920e-01  1.164268e+00 -7.496500e-03  4.95040e-03 -1.557751e-01
 [3,] -9.461830e-02 -7.496500e-03  5.761000e-03 -6.66370e-03  6.366440e-02
 [4,]  6.271730e-02  4.950400e-03 -6.663700e-03  8.23150e-03 -6.252150e-02
 [5,] -2.625664e+00 -1.557751e-01  6.366440e-02 -6.25215e-02  1.753953e+02
 [6,]  1.017806e+01  8.797900e-02 -1.028916e+01  9.75542e+00 -8.614497e+03
 [7,] -1.286920e-01  1.164268e+00 -7.496500e-03  4.95040e-03 -1.557751e-01
 [8,]  3.930000e-07  2.060000e-09  1.150000e-10 -1.39000e-10  5.550000e-09
 [9,] -1.950000e-07  3.680000e-09 -2.050000e-11  8.86000e-12 -6.110000e-09
[10,]  4.530000e-07  4.610000e-10  9.250000e-12 -1.50000e-11  1.310000e-08
[11,]  1.170000e-06 -6.710000e-09  6.740000e-11 -1.68000e-10 -3.400000e-09
[12,] -9.600000e-07  3.970000e-09 -9.520000e-11  2.12000e-10 -3.430000e-10
[13,] -4.970000e-07 -2.070000e-10 -3.100000e-11  5.69000e-11 -2.270000e-09
               [,6]          [,7]         [,8]         [,9]        [,10]
 [1,]  1.017806e+01 -1.286920e-01  3.93000e-07 -1.95000e-07  4.53000e-07
 [2,]  8.797900e-02  1.164268e+00  2.06000e-09  3.68000e-09  4.61000e-10
 [3,] -1.028916e+01 -7.496500e-03  1.15000e-10 -2.05000e-11  9.25000e-12
 [4,]  9.755420e+00  4.950400e-03 -1.39000e-10  8.86000e-12 -1.50000e-11
 [5,] -8.614497e+03 -1.557751e-01  5.55000e-09 -6.11000e-09  1.31000e-08
 [6,]  4.980406e+05  8.797900e-02 -4.67000e-07  3.50000e-07 -7.68000e-07
 [7,]  8.797900e-02  1.164268e+00  2.06000e-09  3.68000e-09  4.61000e-10
 [8,] -4.670000e-07  2.060000e-09  2.15049e-02  9.00950e-03  9.58560e-03
 [9,]  3.500000e-07  3.680000e-09  9.00950e-03  1.58849e-02  1.65230e-03
[10,] -7.680000e-07  4.610000e-10  9.58560e-03  1.65230e-03  1.53024e-02
[11,]  5.610000e-07 -6.710000e-09  4.26370e-03 -9.08500e-04  6.03300e-03
[12,] -2.720000e-07  3.970000e-09 -3.98220e-03  4.55100e-04 -5.55290e-03
[13,]  9.530000e-08 -2.070000e-10 -1.38413e-02 -9.42850e-03 -1.12002e-02
             [,11]        [,12]        [,13]
 [1,]  1.17000e-06 -9.60000e-07 -4.97000e-07
 [2,] -6.71000e-09  3.97000e-09 -2.07000e-10
 [3,]  6.74000e-11 -9.52000e-11 -3.10000e-11
 [4,] -1.68000e-10  2.12000e-10  5.69000e-11
 [5,] -3.40000e-09 -3.43000e-10 -2.27000e-09
 [6,]  5.61000e-07 -2.72000e-07  9.53000e-08
 [7,] -6.71000e-09  3.97000e-09 -2.07000e-10
 [8,]  4.26370e-03 -3.98220e-03 -1.38413e-02
 [9,] -9.08500e-04  4.55100e-04 -9.42850e-03
[10,]  6.03300e-03 -5.55290e-03 -1.12002e-02
[11,]  9.29045e-02 -8.00488e-02 -2.21646e-02
[12,] -8.00488e-02  7.19209e-02  1.82813e-02
[13,] -2.21646e-02  1.82813e-02  1.76818e-02

> B
          [,1]      [,2]     [,3]      [,4]     [,5]      [,6]      [,7]
[1,] -22.02731 0.4982429 2.651566 -2.356899 31.65277 -3302.719 0.4982429
          [,8]       [,9]     [,10]    [,11]     [,12]      [,13]
[1,] 0.3675237 -0.2562356 0.4549983 4.901309 -4.464062 -0.0836918

> t(B)
               [,1]
 [1,]   -22.0273100
 [2,]     0.4982429
 [3,]     2.6515660
 [4,]    -2.3568990
 [5,]    31.6527700
 [6,] -3302.7190000
 [7,]     0.4982429
 [8,]     0.3675237
 [9,]    -0.2562356
[10,]     0.4549983
[11,]     4.9013090
[12,]    -4.4640620
[13,]    -0.0836918

 
Thanks very much,
 
Stu Jordan
Princeton University

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