IRIS Toolbox Reference Manual

## fisher

Approximate Fisher information matrix in frequency domain

#### Syntax

    [F,FF,Delta,Freq] = fisher(M,NPer,List,...)

#### Input arguments

- `M` [ model ] - Solved model object.

- `NPer` [ numeric ] - Length of the hypothetical range for which the Fisher information will be computed.

- `List` [ cellstr ] - List of parameters with respect to which the likelihood function will be differentiated.

#### Output arguments

- `F` [ numeric ] - Approximation of the Fisher information matrix.

- `FF` [ numeric ] - Contributions of individual frequencies to the total Fisher information matrix.

- `Delta` [ numeric ] - Kronecker delta by which the contributions in `Fi` need to be multiplied to sum up to `F`.

- `Freq` [ numeric ] - Vector of frequencies at which the Fisher information matrix is evaluated.

#### Options

- `'chkSstate='` [ `true` | `false` | cell ] - Check steady state in each iteration; works only in non-linear models.

- `'deviation='` [ `true` | `false` ] - Exclude the steady state effect at zero frequency.

- `'exclude='` [ char | cellstr | empty ] - List of measurement variables that will be excluded from the likelihood function.

- `'percent='` [ `true` | `false` ] - Report the overall Fisher matrix `F` as Hessian w.r.t. the log of variables; the interpretation for this is that the Fisher matrix describes the changes in the log-likelihood function in reponse to percent, not absolute, changes in parameters.

- `'progress='` [ `true` | `false` ] - Display progress bar in the command window.

- `'refresh='` [ `true` | `false` ] - Refresh dynamic links in each differentiation step.

- `'solve='` [ `true` | `false` ] - Re-solve model in each differentiation step.

- `'sstate='` [ `true` | `false` | cell ] - Re-compute steady state in each differentiation step; if the model is non-linear, you can pass in a cell array with opt used in the `sstate` function.

#### Description

#### Example

IRIS Toolbox. Copyright © 2007—2013 Jaromir Benes.