use pcls to solve least square fitting with constraints
Baoqiang Cao <bqcaomail <at> gmail.com> writes:
I have a least square fitting problem with linear inequality constraints. pcls seems capable of solving it so I tried it, unfortunately, it is stuck with the following error:
M <- list() M$y = Dmat[,1] M$X = Cmat M$Ain = as.matrix(Amat) M$bin = rep(0, dim(Amat)[1]) M$p=qr.solve(as.matrix(Cmat), Dmat[,1]) M$w = rep(1, length(M$y)) M$C = matrix(0,0,0) p<-pcls(M)
Error in t(qr.qty(qra, t(M$X))[(j + 1):k, ]) : error in evaluating the argument 'x' in selecting a method for function 't'
This is not a reproducible example, but ... You appear to be missing the S, sp, and off elements of the M list? See the examples in ?pcls ...