I am testing 'qr' with an admittedly contrived matrix and I am getting
different results than I am from another package. The matrix that I am using is:
x <- matrix(seq(.1, by=.1, length.out=12), 4)
So the whole test is:
x <- matrix(seq(.1, by=.1, length.out=12), 4)
qr(x)
And the output from 'R' is:
$qr
[,1] [,2] [,3]
[1,] -0.5477226 -1.2780193 -2.008316e+00
[2,] 0.3651484 -0.3265986 -6.531973e-01
[3,] 0.5477226 -0.3781696 -1.650163e-16
[4,] 0.7302967 -0.9124744 8.078153e-01
$rank
[1] 2
$qraux
[1] 1.182574 1.156135 1.589436
$pivot
[1] 1 2 3
attr(,"class")
[1] "qr"
The differences that I see is in the last value of qraux. I was expecting
1.83205 not 1.589436. Also the last row of the decomposition shows:
[4,] 0.7302967 -0.9124744 8.078153e-01
I was expecting
0.73030 -0.91247 -0.55470
So again it is the last element of the array. For the linear algebra gurus out
there is this to be expected from the contrived matrix? Is there a "better"
matrix that I can use to test that will more or less give consistent agreed
upon results for a QR decomposition?
Thank you.
Kevin
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