Description Usage Arguments Details Value References Examples

`de_mean()`

computes group- and de-meaned versions of a
variable that can be used in regression analysis to model the between-
and within-subject effect.

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`x` |
A data frame. |

`...` |
Names of variables that should be group- and de-meaned. |

`grp` |
Quoted or unquoted name of the variable that indicates the group- or cluster-ID. |

`append` |
Logical, if |

`suffix.dm, suffix.gm` |
String value, will be appended to the names of the
group-meaned and de-meaned variables of |

`de_mean()`

is intended to create group- and de-meaned variables
for complex random-effect-within-between models (see Bell et al. 2018),
where group-effects (random effects) and fixed effects correlate (see
Bafumi and Gelman 2006)). This violation of one of the
*Gauss-Markov-assumptions* can happen, for instance, when analysing panel
data. To control for correlating predictors and group effects, it is
recommended to include the group-meaned and de-meaned version of
*time-varying covariates* in the model. By this, one can fit
complex multilevel models for panel data, including time-varying,
time-invariant predictors and random effects. This approach is superior to
simple fixed-effects models, which lack information of variation in the
group-effects or between-subject effects.

A description of how to translate the
formulas described in *Bell et al. 2018* into R using `lmer()`

from lme4 or `glmmTMB()`

from glmmTMB can be found here:
for lmer()
and for glmmTMB().

For `append = TRUE`

, `x`

including the group-/de-meaned
variables as new columns is returned; if `append = FALSE`

, only the
group-/de-meaned variables will be returned.

Bafumi J, Gelman A. 2006. Fitting Multilevel Models When Predictors and Group Effects Correlate. In. Philadelphia, PA: Annual meeting of the American Political Science Association.

Bell A, Fairbrother M, Jones K. 2018. Fixed and Random Effects Models: Making an Informed Choice. Quality & Quantity. doi: 10.1007/s11135-018-0802-x

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