# iv4Options

Option set for `iv4`

## Syntax

`opt = iv4Options`

opt = iv4Options(Name,Value)

## Description

creates
the default options set for `opt`

= iv4Options`iv4`

.

creates
an option set with the options specified by one or more `opt`

= iv4Options(`Name,Value`

)`Name,Value`

pair
arguments.

## Input Arguments

### Name-Value Arguments

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

`InitialCondition`

— Handling of initial conditions

`'auto'`

(default) | `'zero'`

| `'estimate'`

Handling of initial conditions during estimation, specified as one of the following values:

`'zero'`

— The initial condition is set to zero.`'estimate'`

— The initial condition is treated as an independent estimation parameter.`'auto'`

— The software chooses the initial condition handling method based on the estimation data.

`Focus`

— Error to be minimized

`'prediction'`

(default) | `'simulation'`

Error to be minimized in the loss function during estimation,
specified as the comma-separated pair consisting of `'Focus'`

and
one of the following values:

`'prediction'`

— The one-step ahead prediction error between measured and predicted outputs is minimized during estimation. As a result, the estimation focuses on producing a good predictor model.`'simulation'`

— The simulation error between measured and simulated outputs is minimized during estimation. As a result, the estimation focuses on making a good fit for simulation of model response with the current inputs.

The `Focus`

option can be interpreted as a
weighting filter in the loss function. For more information, see Loss Function and Model Quality Metrics.

`WeightingFilter`

— Weighting prefilter

`[]`

(default) | vector | matrix | cell array | linear system

Weighting prefilter applied to the loss function to be minimized
during estimation. To understand the effect of `WeightingFilter`

on
the loss function, see Loss Function and Model Quality Metrics.

Specify `WeightingFilter`

as one of the following
values:

`[]`

— No weighting prefilter is used.Passbands — Specify a row vector or matrix containing frequency values that define desired passbands. You select a frequency band where the fit between estimated model and estimation data is optimized. For example,

`[wl,wh]`

where`wl`

and`wh`

represent lower and upper limits of a passband. For a matrix with several rows defining frequency passbands,`[w1l,w1h;w2l,w2h;w3l,w3h;...]`

, the estimation algorithm uses the union of the frequency ranges to define the estimation passband.Passbands are expressed in

`rad/TimeUnit`

for time-domain data and in`FrequencyUnit`

for frequency-domain data, where`TimeUnit`

and`FrequencyUnit`

are the time and frequency units of the estimation data.SISO filter — Specify a single-input-single-output (SISO) linear filter in one of the following ways:

A SISO LTI model

`{A,B,C,D}`

format, which specifies the state-space matrices of a filter with the same sample time as estimation data.`{numerator,denominator}`

format, which specifies the numerator and denominator of the filter as a transfer function with same sample time as estimation data.This option calculates the weighting function as a product of the filter and the input spectrum to estimate the transfer function.

Weighting vector — Applicable for frequency-domain data only. Specify a column vector of weights. This vector must have the same length as the frequency vector of the data set,

`Data.Frequency`

. Each input and output response in the data is multiplied by the corresponding weight at that frequency.

`EnforceStability`

— Control whether to enforce stability of model

`false`

(default) | `true`

Control whether to enforce stability of estimated model, specified
as the comma-separated pair consisting of `'EnforceStability'`

and
either `true`

or `false`

.

**Data Types: **`logical`

`EstimateCovariance`

— Control whether to generate parameter covariance data

`true`

(default) | `false`

Controls whether parameter covariance data is generated, specified as
`true`

or `false`

.

If `EstimateCovariance`

is `true`

, then use
`getcov`

to fetch the covariance matrix
from the estimated model.

`Display`

— Specify whether to display the estimation progress

`'off'`

(default) | `'on'`

Specify whether to display the estimation progress, specified as one of the following values:

`'on'`

— Information on model structure and estimation results are displayed in a progress-viewer window.`'off'`

— No progress or results information is displayed.

`InputOffset`

— Removal of offset from time-domain input data during estimation

`[]`

(default) | vector of positive integers | matrix

Removal of offset from time-domain input data during estimation,
specified as the comma-separated pair consisting of `'InputOffset'`

and
one of the following:

A column vector of positive integers of length

*Nu*, where*Nu*is the number of inputs.`[]`

— Indicates no offset.*Nu*-by-*Ne*matrix — For multi-experiment data, specify`InputOffset`

as an*Nu*-by-*Ne*matrix.*Nu*is the number of inputs, and*Ne*is the number of experiments.

Each entry specified by `InputOffset`

is
subtracted from the corresponding input data.

`OutputOffset`

— Removal of offset from time-domain output data during estimation

`[]`

(default) | vector | matrix

Removal of offset from time-domain output data during estimation,
specified as the comma-separated pair consisting of `'OutputOffset'`

and
one of the following:

A column vector of length

*Ny*, where*Ny*is the number of outputs.`[]`

— Indicates no offset.*Ny*-by-*Ne*matrix — For multi-experiment data, specify`OutputOffset`

as a*Ny*-by-*Ne*matrix.*Ny*is the number of outputs, and*Ne*is the number of experiments.

Each entry specified by `OutputOffset`

is
subtracted from the corresponding output data.

`Advanced`

— Additional advanced options

structure

Additional advanced options, specified as a structure with the following fields:

`MaxSize`

— Specifies the maximum number of elements in a segment when input-output data is split into segments.`MaxSize`

must be a positive integer.**Default:**`250000`

`StabilityThreshold`

— Specifies thresholds for stability tests.`StabilityThreshold`

is a structure with the following fields:`s`

— Specifies the location of the right-most pole to test the stability of continuous-time models. A model is considered stable when its right-most pole is to the left of`s`

.**Default:**`0`

`z`

— Specifies the maximum distance of all poles from the origin to test stability of discrete-time models. A model is considered stable if all poles are within the distance`z`

from the origin.**Default:**`1+sqrt(eps)`

## Output Arguments

`opt`

— Options set for `iv4`

`iv4Options`

option set

Option set for `iv4`

, returned
as an `iv4Options`

option set.

## Examples

### Create Default Options Set for ARX Model Estimation Using 4-Stage Instrument Variable Method

opt = iv4Options;

### Specify Options for ARX Model Estimation Using 4-Stage Instrument Variable Method

Create an options set for `iv4`

using the `'backcast'`

algorithm to initialize the state. Set `Display`

to `'on'`

.

opt = iv4Options('InitialCondition','backcast','Display','on');

Alternatively, use dot notation to set the values of `opt`

.

opt = iv4Options; opt.InitialCondition = 'backcast'; opt.Display = 'on';

## Compatibility Considerations

### Renaming of Estimation and Analysis Options

The names of some estimation and analysis options were changed in R2018a. Prior names still work. For details, see the R2018a release note Renaming of Estimation and Analysis Options.

**Introduced in R2012a**

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