Return the coefficients of a polynomial
p= polyfit (x, y, n) [p, s] = polyfit (x, y, n) [p, s, mu] = polyfit (x, y, n)
degree (scalar) or logical vector
The polynomial coefficients are returned in a row vector.
The optional output s is a structure
Triangular factor R from the QR decomposition.
The Vandermonde matrix used to compute the polynomial coefficients.
The unscaled covariance matrix, formally equal to the inverse of x'*x, but computed in a way minimizing roundoff error propagation.
The degrees of freedom.
The norm of the residuals.
The values of the polynomial for each value of x.
Return the coefficients of a polynomial p(x) of degree n that minimizes the least-squares-error of the fit to the points [x, y]. If n is a logical vector, it is used as a mask to selectively force the corresponding polynomial coefficients to be used or ignored.
The second output may be used by moc_polyval to calculate the statistical error limits of the predicted values. In particular, the standard deviation of pcoefficients is given by sqrt (diag (s.C)/s.df)*s.normr.
When the third output, mu, is present the coefficients, p, are associated with a polynomial in xhat = (x-mu(1))/mu(2). Where mu(1) = mean (x), and mu(2) = stdev (x). This linear transformation of x improves the numerical stability of the fit.