GNU Scientific Library – Reference Manual: Nonlinear Least-Squares Covariance Matrix

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### 39.10 Covariance matrix of best fit parameters

Function: int gsl_multifit_nlinear_covar (const gsl_matrix * J, const double epsrel, gsl_matrix * covar)
Function: int gsl_multilarge_nlinear_covar (gsl_matrix * covar, gsl_multilarge_nlinear_workspace * w)

  This function computes the covariance matrix of best-fit parameters using the Jacobian matrix J and stores it in covar. The parameter epsrel is used to remove linear-dependent columns when J is rank deficient.

  The covariance matrix is given by,

      covar = (J^T J)^{-1}

  or in the weighted case,

      covar = (J^T W J)^{-1}

  and is computed using the factored form of the Jacobian (Cholesky, QR, or SVD). Any columns of R which satisfy

      |R_{kk}| <= epsrel |R_{11}|

  are considered linearly-dependent and are excluded from the covariance matrix (the corresponding rows and columns of the covariance matrix are set to zero).

  If the minimisation uses the weighted least-squares function f_i = (Y(x, t_i) - y_i) / \sigma_i then the covariance matrix above gives the statistical error on the best-fit parameters resulting from the Gaussian errors \sigma_i on the underlying data y_i. This can be verified from the relation \delta f = J \delta c and the fact that the fluctuations in f from the data y_i are normalised by \sigma_i and so satisfy <\delta f \delta f^T> = I.

  For an unweighted least-squares function f_i = (Y(x, t_i) - y_i) the covariance matrix above should be multiplied by the variance of the residuals about the best-fit \sigma^2 = \sum (y_i - Y(x,t_i))^2 / (n-p) to give the variance-covariance matrix \sigma^2 C. This estimates the statistical error on the best-fit parameters from the scatter of the underlying data.

  For more information about covariance matrices see Fitting Overview.

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