# Extracting Variance-Covariance Matrices

### Description

Extracting (approximate) variance-covariance matrices for fixed-effect parameters, variance components, ratios of variance components to error variance, or the response variable.

### Usage

1 2 3 |

### Arguments

`object` |
An object of class |

`what` |
A character vector (only the first element will be used) specifying what variance-covariance matrices are requested. |

`drop` |
A logical scalar, indicating whether zero variance components should be dropped from the results. |

`beta.correction` |
A logical scalar, only applicable when |

`...` |
Place holder. |

### Details

For fixed-effect parameters, the results is the plug-in estimate variance of generalized least squares estimates when `beta.correction=FALSE`

; Otherwise, the Kackar and Harville (1984) correction will be used (default). For ratios of variance components to error variance, the result is the Hessian matrix. For response variable, the result is the plug-in estimate of the marginal variance. For variance components, the result is the plug-in estimate of inverse expected information matrix from the restricted likelihood.

### Value

A numeric matrix of the requested variance-covariance.

### Author(s)

Long Qu

### References

Raghu N. Kackar and David A. Harville (1984) Approximations for standard errors of estimators of fixed and random effect in mixed linear models. *Journal of the American Statistical Association* 79, 853–862

### See Also

`varComp`

for the varComp object;
`KR.varComp`

for testing fixed effect parameters accounting for uncertainty in variance parameter estimates.