StatLS
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Purpose
Compute parameter statistics for least squares problems.
Calling Syntax
LS = StatLS(x_k, r_k, J_k);
Inputs
| Input | Description |
|---|---|
| x_k | Optimal parameter vector, length n. |
| r_k | Residual vector, length m. |
| J_k | Jacobian matrix, length m by n. |
Outputs
Structure LS with fields:
| Output | Description |
|---|---|
| SSQ | Sum of squares: r'k * rk |
| covar | Covariance matrix: Inverse of J' * diag(1./(r'k * rk )) * J |
| sigma2 | Estimate squared standard deviation of problem, SSQ / Degrees of freedom, i.e. SSQ/(m-n) |
| Corr | Correlation matrix: Normalized Covariance matrix |
| Cov./(CovDiag * CovDiag'), where CovDiag = sqrt(diag(Cov)) | |
| StdDev | Estimated standard deviation in parameters: CovDiag * sqrt(sigma2) |
| x | =x_k, the input x |
| ConfLim | 95 % Confidence limit (roughly) assuming normal distribution of errors ConfLim = 2 * LS.StdDev |
| CoeffVar | The coefficients of variation of estimates: StdDev./xk |
