I have a question about converting a matlab function into R, and I was hoping that someone could help.
The standard QR decomposition used in both matlab and R is referred to as qr(). To my understanding, the standard way of performing a qr decomposition in both languages is:
Matlab:
[Q,R] = qr(A)
satisfying QR=A
R:
z <- qr(A)
Q <- qr.Q(z)
R <- qr.R(z)
Both of which provide me with the same results, unfortunately, this is not what I need. What I need is this:
Matlab: [Q,R,e] = qr(A,0) which produces an economy-size decomposition in which e is a permutation vector so that A(:,e) = Q*R.
R: No clue
I have tried comparing [Q,R,E] = qr(A) with
z <- qr(A);
Q <- qr.Q(z);
R <- qr.R(z);
E <- diag(ncol(A))[z$pivot]
and results seem identical for variables Q and E (but different for R). So depending on the defined inputs/outputs there will be different results (which makes sense).
So my question is: Is there a way in R that can mimic this [Q,R,e]=qr(A,0) in Matlab?
I have tried digging into the matlab function but it leads to a long and torturous road of endless function definitions and I was hoping for a better solution.
Any help would be much appreciated, and if I've missed something obvious, I apologize.
I think the difference comes down to the numerical library underlying the calculations. By default, R's qr
function uses the (very old) LINPACK routines, but if I do
z <- qr(X,LAPACK=T)
then R uses LAPACK and the results seem to match MATLAB's (which is probably also using LAPACK underneath). Either way we see the expected relationship with X
:
z <- qr(X,LAPACK=F)
all.equal(X[,z$pivot], qr.Q(z)%*%qr.R(z), check.attributes=FALSE)
# [1] TRUE
z <- qr(X,LAPACK=T)
all.equal(X[,z$pivot], qr.Q(z)%*%qr.R(z), check.attributes=FALSE)
# [1] TRUE
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With