imxRobustSE {OpenMx} | R Documentation |

## imxRobustSE

### Description

This is an internal function exported for those people who know what they are doing.

### Usage

```
imxRobustSE(model, details = FALSE, dependencyModels = character(0))
```

### Arguments

`model` |
An OpenMx model object that has been run. |

`details` |
Logical. whether to return the full parameter covariance matrix. |

`dependencyModels` |
Passed to |

### Details

This function computes robust standard errors via a sandwich estimator. The "bread" of the sandwich is the numerically computed inverse Hessian of the likelihood function. This is what is typically used for standard errors throughout OpenMx. The "meat" of the sandwich is proportional to the covariance matrix of the numerically computed row derivatives of the likelihood function (i.e. row gradients).

When `details=FALSE`

, only the standard errors are returned.

When `details=TRUE`

,
a list with five named elements is returned. Element `SE`

is the
vector of standard errors that is also returned when `details=FALSE`

.
Element `cov`

is the full robust covariance matrix of the parameter
estimates; the square root of the diagonal of `cov`

gives the
standard errors. Element `bread`

is the aforementioned
"bread"–the naive (non-robust) covariance matrix of the parameter
estimates. Element `meat`

is the aforementioned "meat," proportional
to the covariance matrix of the row gradients. Element `TIC`

is the model's Takeuchi Information Criterion, which is a generalization
of AIC calculated from the "bread," the "meat," and the loglikelihood
at the maximum-likelihood solution.

This function does not work correctly with multigroup models in which the groups themselves contain subgroups. This function also does not correctly handle multilevel data.

*OpenMx*version 2.21.11 Index]