de_mean: Compute group-meaned and de-meaned variables

Description Usage Arguments Details Value References Examples

View source: R/de_mean.R


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.


de_mean(x, ..., grp, append = TRUE, = "_dm", = "_gm")



A data frame.


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


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


Logical, if TRUE (the default) and x is a data frame, x including the new variables as additional columns is returned; if FALSE, only the new variables are returned.,

String value, will be appended to the names of the group-meaned and de-meaned variables of x. By default, de-meaned variables will be suffixed with "_dm" and grouped-meaned variables with "_gm".


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


efc$ID <- sample(1:4, nrow(efc), replace = TRUE) # fake-ID
de_mean(efc, c12hour, barthtot, grp = ID, append = FALSE)

sjmisc documentation built on Jan. 10, 2020, 9:07 a.m.