On 04-02-2012, at 19:14, Alaios wrote: > Dear all > I am having a linear system of the form > A*X=B and I want to find the X by using least squares. > For example my A is of dimension [205,3] and my B is of dimension[205,1] > > I am looking for the X matrix which is of the size [3,1]. In the matlab I > was doing that by the function > > > X = LSCOV(A,B) returns the ordinary least squares solution to the > linear system of equations A*X = B, i.e., X is the N-by-1 vector that > minimizes the sum of squared errors (B - A*X)'*(B - A*X), where A is > M-by-N, and B is M-by-1 > > > for example for the matrices > > A = > > 1 2 3 > 4 5 6 > 7 8 9 > > K>> B=[1 2 3] > > B = > > 1 2 3 > > >>> lscov(A,B') > > ans = > > -0.0000 > 0 > 0.3333 > > How I can get the same in R? I know about the lm function but it looks > complicated to me how to insert formula and how to get exactly back only what > I need
?qr Example: A <- matrix(runif(15),5) b <- 1:5 solve(qr(A, LAPACK=TRUE), b) or lm(b ~ 0 + A) Berend ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
