Description Usage Arguments Value Author(s) References See Also Examples

Computes the percentile change, as a measure of influential data. This unstandardized measure can serve to help interpret the magnitude of the influence single or combined grouping levels exert on mixed effects models. The percentage change 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 of percentage change is calculated for each parameter in the model separately, based on the information returned by the influence() function.

1 |

`estex` |
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), percentage change are calculated for all parameters in the model |

`sort` |
If |

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

`abs` |
If |

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

Rense Nieuwenhuis, Ben Pelzer, Manfred te Grotenhuis

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.

`influence`

, `cooks.distance.estex`

,
`dfbetas.estex`

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