# Compute the DFBETAS measure of influential data

### Description

DFBETAS (standardized difference of the beta) is a measure that standardizes the absolute difference in parameter estimates between a (mixed effects) regression model based on a full set of data, and a model from which a (potentially influential) subset of data is removed. A value for DFBETAS is calculated for each parameter in the model separately. This function computes the DFBETAS based on the information returned by the influence() function.

### Usage

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### Arguments

`model` |
An object as returned by the influence() function, containing the altered estimates of a mixed effects regression model |

`parameters` |
Used to define a selection of parameters. If parameters=0 (default), DFBETAS is calculated for all parameters in the model |

`sort` |
If |

`to.sort` |
Specify on which variable the DFBETAS must be sorted. If only one variable present (either in the model, or due to the selection specified in |

`abs` |
If |

`...` |
Currently not used |

### Value

A matrix is returned, containing DFBETAS-values for each (selected) fixed parameter of the model, and separately for each evaluated set of influential data.

### Author(s)

Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis

### References

Nieuwenhuis, R., Te Grotenhuis, M., & Pelzer, B. (2012). Influence.ME: tools for detecting influential data in mixed effects models. *R Journal*, 4(2), 38???47.

Belsley, D.A., Kuh, E. & Welsch, R.E. (1980). *Regression Diagnostics. Identifying Influential Data and Source of Collinearity*. Wiley.

Snijders, T.A. & Bosker, R.J. (1999). *Multilevel Analysis, an introduction to basic and advanced multilevel modeling*. Sage.

Van der Meer, T., Te Grotenhuis, M., & Pelzer, B. (2010). *Influential Cases in Multilevel Modeling: A Methodological Comment*. American Sociological Review, 75(1), 173-178.

### See Also

`influence.mer`

, `cooks.distance.estex`

### Examples

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