residmin_stat - optim_doc
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### 2.4 Statistics for residual minimization

— Function File: info = residmin_stat (f, p, settings)

> Frontend for computation of statistics for a residual-based minimization.

> settings is a structure whose fields can be set by `optimset`. With settings the computation of certain statistics is requested by setting the fields `ret_<name_of_statistic>` to `true`. The respective statistics will be returned in a structure as fields with name `<name_of_statistic>`. Depending on the requested statistic and on the additional information provided in settings, f and p may be empty. Otherwise, f is the model function of an optimization (the interface of f is described e.g. in `nonlin_residmin`, please see there), and p is a real column vector with parameters resulting from the same optimization.

> Currently, the following statistics (or general information) can be requested:

> `dfdp`: Jacobian of model function with respect to parameters.

> `covd`: Covariance matrix of data (typically guessed by applying a factor to the covariance matrix of the residuals).

> `covp`: Covariance matrix of final parameters.

> `corp`: Correlation matrix of final parameters.

> See also: curvefit_stat.

#### Further settings

The fields of the settings structure can be set with optimset.

For settings common to all frontends see Common frontend options.

##### Additional settings:

`objf_type`
  Type of objective function of the optimization; must be specified in many cases. This determines which backends to use. Currently, there are only backends for the type "wls" (weighted least squares).
`residuals`
`covd`
  Optional information on the result of optimization, residuals and covariance matrix of data, respectively.
`weights`
  Array of weights applied to the residuals in the previous optimization. Dimensions must match those of the residuals.
`dfdp`
  Can be set in the same way and has the same default as in `nonlin_residmin` (see nonlin_residmin), but alternatively may already contain the computed Jacobian of the model function at the final parameters in matrix- or structure-form.
`complex_step_derivative_f`
  Estimate Jacobian of model function with complex step derivative approximation. Use only if you know that your model function is suitable for this. No user function for the Jacobian (`dfdp`) must be specified.

#### Structure based parameter handling

Please see Parameter structures.

#### Backend information

Please see Residual optimization and choose backend from menu under `Statistics backends'.