# setcov

Set parameter covariance data in identified model

## Syntax

```sys = setcov(sys0,cov) ```

## Description

`sys = setcov(sys0,cov)` sets the parameter covariance of identified model `sys0` as `cov`.

The model parameter covariance is calculated and stored automatically when a model is estimated. Therefore, you do not need to set the parameter covariance explicitly for estimated models. Use this function for analysis, such as to study how the parameter covariance affects the response of a model obtained by explicit construction.

## Input Arguments

 `sys0` Identified model. Identified model, specified as an `idtf`, `idss`, `idgrey`, `idpoly`, `idproc`, or `idnlgrey` model. You cannot set the covariance for nonlinear black-box models (`idnlarx` and `idnlhw`). `cov` Parameter covariance matrix. `cov` is one of the following: an np-by-np semi-positive definite symmetric matrix, where np is equal to the number of parameters of `sys0`.a structure with the following fields that describe the parameter covariance in a factored form:`R` — usually the Cholesky factor of inverse of covariance.`T` — transformation matrix.`Free` — logical vector of length np indicating if a parameter is free. Here np is equal to the number of parameters of `sys0`.`cov(Free,Free) = T*inv(R'*R)*T'`.

## Output Arguments

 `sys` Identified model. The values of all the properties of `sys` are the same as those in `sys0`, except for the parameter covariance values which are modified as specified by `cov`.

## Examples

collapse all

Create a transfer function model for the following system:

`$sys0=\frac{4}{{s}^{2}+2s+1}$`

```sys0 = idtf(4,[1 2 1]); np = nparams(sys0);```

`sys0` contains `np` model parameters.

Specify the covariance values for the denominator parameters only.

```cov = zeros(np); den_index = 2:3; cov(den_index,den_index) = diag([0.04 0.001]);```

`cov` is a covariance matrix with nonzero entries for the denominator parameters.

Set the covariance for `sys0`.

`sys = setcov(sys0,cov);`