This function adjusts the loglikelihood of fitted model objects based on
Chandler and Bate (2007). It is a
generic function for different types of models, which are listed in
**Supported models**. This section also contains links to function-specific
help pages.

adj_loglik(x, cluster = NULL, use_vcov = TRUE, use_mle = TRUE, ...)

## Arguments

x |
A supported fitted model object, see **Supported models** |

cluster |
A vector or factor indicating the cluster the corresponding
loglikelihood contribution belongs to. It is required to have the same
length as the vector returned by `logLik_vec()` . If `cluster` is not
supplied or `NULL` , then it is assumed that each observation forms its own
cluster. |

use_vcov |
A logical scalar. By default, the `vcov()` method for `x` is
used to estimate the Hessian of the independence loglikelihood, if the
function exists. Otherwise, or if `use_vcov = FALSE` , `H` is estimated
using `stats::optimHess()` inside `chandwich::adjust_loglik()` . |

use_mle |
A logical scalar. By default, the MLE from `x` is taken as
given, and is not reestimated. By setting `use_mle` to `FALSE` , the parameters
are reestimated in the function `chandwich::adjust_loglik()` using
`stats::optim()` .This feature is currently for development purposes only,
may return misleading/false results and may be removed without notice. |

... |
Further arguments to be passed to `sandwich::meatCL()` if
`cluster` is defined, if `cluster = NULL` , they are passed into
`sandwich::meat()` . |

## Value

An object of class `"chantrics"`

inheriting from class `"chandwich"`

.
See the documentation provided with `chandwich::adjust_loglik()`

.

## Details

If `use_vcov = TRUE`

, the current default, the function will test
whether a `vcov`

S3 method exists for `x`

, and will take the
variance-covariance matrix from there. Otherwise, or if `use_vcov = FALSE`

the variance-covariance matrix of the MLE is estimated inside
`chandwich::adjust_loglik()`

using `stats::optimHess()`

.

## Supported models

## Available methods

`"chantrics"`

objects have the following methods available to them:

## Examples

See the model-specific pages in the *supported models*
section.

## References

R. E. Chandler and S. Bate, Inference for clustered data using the
independence loglikelihood, Biometrika, 94 (2007), pp. 167–183.
doi: 10.1093/biomet/asm015
.

## See also