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Function List:

C++ API

: x = lscov (A, b)
: x = lscov (A, b, V)
: x = lscov (A, b, V, alg)
: [x, stdx, mse, S] = lscov (…)

Compute a generalized linear least squares fit.

Estimate x under the model b = Ax + w, where the noise w is assumed to follow a normal distribution with covariance matrix {\sigma^2} V.

If the size of the coefficient matrix A is n-by-p, the size of the vector/array of constant terms b must be n-by-k.

The optional input argument V may be a n-by-1 vector of positive weights (inverse variances), or a n-by-n symmetric positive semidefinite matrix representing the covariance of b. If V is not supplied, the ordinary least squares solution is returned.

The alg input argument, a guidance on solution method to use, is currently ignored.

Besides the least-squares estimate matrix x (p-by-k), the function also returns stdx (p-by-k), the error standard deviation of estimated x; mse (k-by-1), the estimated data error covariance scale factors (\sigma^2); and S (p-by-p, or p-by-p-by-k if k > 1), the error covariance of x.

Reference: Golub and Van Loan (1996), Matrix Computations (3rd Ed.), Johns Hopkins, Section 5.6.3

See also: ols, gls, lsqnonneg.

Package: octave