mxFitFunctionGREML {OpenMx} | R Documentation |

## Create MxFitFunctionGREML Object

### Description

This function creates a new `MxFitFunctionGREML`

object.

### Usage

```
mxFitFunctionGREML(dV=character(0), aug=character(0),
augGrad=character(0), augHess=character(0),
autoDerivType=c("semiAnalyt","numeric"),infoMatType=c("average","expected"))
```

### Arguments

`dV` |
Vector of character strings; defaults to a character vector of length zero. If a value of non-zero length is provided, it must be a |

`aug` |
Character string; defaults to a character vector of length zero. Any elements after the first are ignored. The string should name a 1x1 |

`augGrad` |
Character string; defaults to a character vector of length zero. Any elements after the first are ignored. The string should name a |

`augHess` |
Character string; defaults to a character vector of length zero. Any elements after the first are ignored. The string should name a |

`autoDerivType` |
"Automatic derivative type." Character string, either "semiAnalyt" (default) or "numeric". See details below. |

`infoMatType` |
"Information matrix type." Character string, either "average" (default) or "expected". See details below. |

### Details

Making effective use of arguments `dV`

, `augGrad`

, and `augHess`

will usually require a custom `mxComputeSequence()`

. The derivatives of the REML loglikelihood function with respect to parameters can be internally computed from the derivatives of the 'V' matrix supplied via `dV`

. The loglikelihood's first derivatives thus computed will always be exact, but its matrix of second partial derivatives (i.e., its Hessian matrix) will be approximated by either the average or expected information matrix, per the value of argument `infoMatType`

. The average information matrix is faster to compute, but may not provide a good approximation to the Hessian if 'V' is not linear in the model's free parameters. The expected information matrix is slower to compute, but does not assume that 'V' is linear in the free parameters. Neither information matrix will be a good approximation to the Hessian unless the derivatives of 'V' evaluate to symmetric matrices the same size as 'V'. Note also that these loglikelihood derivatives do not reflect the influence of any parameter bounds or `MxConstraint`

s. Internally, the derivatives of the 'V' matrix are assumed to be symmetric, and the elements above their main diagonals are ignored.

Formerly, if any derivatives were provided via `dV`

, then derivatives had to be provided for *every* free parameter in the MxModel. Currently, users may provide derivatives of 'V' via `dV`

with respect to some or all free parameters. Note that the gradient and Hessian of the augmentation must be complete, i.e. contain derivatives of the augmentation with respect to every parameter or pair of parameters respectively.

If there are any free parameters with respect to which the user did not provide an analytic derivative of 'V', OpenMx will automatically calculate the necessary loglikelihood derivatives according to `autoDerivType`

. If `autoDerivType="semiAnalyt"`

, the GREML fitfunction backend will calculate the missing derivatives in a "semi-analytic" fashion. Specifically, the backend will numerically differentiate 'V' with respect to the relevant parameter(s), and use those numeric matrix derivatives to analytically calculate the needed loglikelihood derivatives. If `autoDerivType="numeric"`

, the needed loglikelihood derivatives will be calculated numerically, via finite-differences.

Argument `aug`

is intended to allow users to provide penalty functions or prior likelihoods in order to approximate constraints or to regularize optimization. The user is warned that careless use of this augmentation feature may undermine the validity of his/her statistical inferences.

### Value

Returns a new object of class `MxFitFunctionGREML`

.

### References

The OpenMx User's guide can be found at https://openmx.ssri.psu.edu/documentation/.

### See Also

See `MxFitFunctionGREML`

for the S4 class created by `mxFitFunctionGREML()`

. For more information generally concerning GREML analyses, including a complete example, see `mxExpectationGREML()`

.

Other fit functions:
`mxFitFunctionMultigroup`

, `mxFitFunctionML`

,
`mxFitFunctionWLS`

, `mxFitFunctionAlgebra`

,
`mxFitFunctionR`

, `mxFitFunctionRow`

More information about the OpenMx package may be found here.

### Examples

```
gff <- mxFitFunctionGREML()
str(gff)
```

*OpenMx*version 2.21.11 Index]