Computes empirical likelihood displacement for model diagnostics and outlier detection.

## Usage

```
# S4 method for class 'EL'
eld(object, control = NULL)
# S4 method for class 'GLM'
eld(object, control = NULL)
```

## Arguments

- object
An object that inherits from EL.

- control
An object of class ControlEL constructed by

`el_control()`

. Defaults to`NULL`

and inherits the`control`

slot in`object`

.

## Value

An object of class ELD.

## Details

Let \(L(\theta)\) be the empirical log-likelihood function based
on the full sample with \(n\) observations. The maximum empirical
likelihood estimate is denoted by \(\hat{\theta}\). Consider a reduced
sample with the \(i\)th observation deleted and the corresponding
estimate \(\hat{\theta}_{(i)}\). The empirical likelihood displacement is
defined by
$$\textrm{ELD}_i = 2\{L(\hat{\theta}) - L(\hat{\theta}_{(i)})\}.$$
If \(\textrm{ELD}_i \) is large, then the \(i\)th observation is an
influential point and can be inspected as a possible outlier. `eld`

computes \(\textrm{ELD}_i \) for \(i = 1, \dots, n \).

## References

Lazar NA (2005).
“Assessing the Effect of Individual Data Points on Inference From
Empirical Likelihood.”
*Journal of Computational and Graphical Statistics*, **14**(3),
626–642. doi:10.1198/106186005X59568
.

Zhu H, Ibrahim JG, Tang N, Zhang H (2008).
“Diagnostic Measures for Empirical Likelihood of General Estimating
Equations.” *Biometrika*, **95**(2), 489–507.
doi:10.1093/biomet/asm094
.

## See also

EL, ELD, `el_control()`

, `plot()`