% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/demean.R
\name{demean}
\alias{demean}
\alias{degroup}
\alias{detrend}
\title{Compute group-meaned and de-meaned variables}
\usage{
demean(
  x,
  select,
  group,
  suffix_demean = "_within",
  suffix_groupmean = "_between",
  add_attributes = TRUE,
  verbose = TRUE
)

degroup(
  x,
  select,
  group,
  center = "mean",
  suffix_demean = "_within",
  suffix_groupmean = "_between",
  add_attributes = TRUE,
  verbose = TRUE
)

detrend(
  x,
  select,
  group,
  center = "mean",
  suffix_demean = "_within",
  suffix_groupmean = "_between",
  add_attributes = TRUE,
  verbose = TRUE
)
}
\arguments{
\item{x}{A data frame.}

\item{select}{Character vector (or formula) with names of variables to select
that should be group- and de-meaned.}

\item{group}{Character vector (or formula) with the name of the variable that
indicates the group- or cluster-ID.}

\item{suffix_demean, suffix_groupmean}{String value, will be appended to the
names of the group-meaned and de-meaned variables of \code{x}. By default,
de-meaned variables will be suffixed with \code{"_within"} and
grouped-meaned variables with \code{"_between"}.}

\item{add_attributes}{Logical, if \code{TRUE}, the returned variables gain
attributes to indicate the within- and between-effects. This is only
relevant when printing \code{model_parameters()} - in such cases, the
within- and between-effects are printed in separated blocks.}

\item{verbose}{Toggle warnings and messages.}

\item{center}{Method for centering. \code{demean()} always performs
mean-centering, while \code{degroup()} can use \code{center = "median"} or
\code{center = "mode"} for median- or mode-centering, and also \code{"min"}
or \code{"max"}.}
}
\value{
A data frame with the group-/de-meaned variables, which get the suffix
\code{"_between"} (for the group-meaned variable) and \code{"_within"} (for
the de-meaned variable) by default.
}
\description{
\code{demean()} computes group- and de-meaned versions of a variable that can be
used in regression analysis to model the between- and within-subject effect.
\code{degroup()} is more generic in terms of the centering-operation. While
\code{demean()} always uses mean-centering, \code{degroup()} can also use the mode or
median for centering.
}
\details{
\subsection{Heterogeneity Bias}{
Mixed models include different levels of sources of variability, i.e.
error terms at each level. When macro-indicators (or level-2 predictors,
or higher-level units, or more general: \emph{group-level predictors that
\strong{vary} within and across groups}) are included as fixed effects (i.e.
treated as covariate at level-1), the variance that is left unaccounted for
this covariate will be absorbed into the error terms of level-1 and level-2
(\cite{Bafumi and Gelman 2006; Gelman and Hill 2007, Chapter 12.6.}):
\dQuote{Such covariates contain two parts: one that is specific to the
higher-level entity that does not vary between occasions, and one that
represents the difference between occasions, within higher-level entities}
(\cite{Bell et al. 2015}). Hence, the error terms will be correlated with
the covariate, which violates one of the assumptions of mixed models
(iid, independent and identically distributed error terms). This bias is
also called the \emph{heterogeneity bias} (\cite{Bell et al. 2015}). To
resolve this problem, level-2 predictors used as (level-1) covariates should
be separated into their "within" and "between" effects by "de-meaning" and
"group-meaning": After demeaning time-varying predictors, \dQuote{at the
higher level, the mean term is no longer constrained by Level 1 effects,
so it is free to account for all the higher-level variance associated
with that variable} (\cite{Bell et al. 2015}).
}
\subsection{Panel data and correlating fixed and group effects}{
\code{demean()} is intended to create group- and de-meaned variables
for panel regression models (fixed effects models), or for complex
random-effect-within-between models (see \cite{Bell et al. 2015, 2018}),
where group-effects (random effects) and fixed effects correlate (see
\cite{Bafumi and Gelman 2006}). This can happen, for instance, when
analyzing panel data, which can lead to \emph{Heterogeneity Bias}. To
control for correlating predictors and group effects, it is recommended
to include the group-meaned and de-meaned version of \emph{time-varying covariates}
(and group-meaned version of \emph{time-invariant covariates} that are on
a higher level, e.g. level-2 predictors) in the model. By this, one can
fit complex multilevel models for panel data, including time-varying
predictors, time-invariant predictors and random effects.
}
\subsection{Why mixed models are preferred over fixed effects models}{
A mixed models approach can model the causes of endogeneity explicitly
by including the (separated) within- and between-effects of time-varying
fixed effects and including time-constant fixed effects. Furthermore,
mixed models also include random effects, thus a mixed models approach
is superior to classic fixed-effects models, which lack information of
variation in the group-effects or between-subject effects. Furthermore,
fixed effects regression cannot include random slopes, which means that
fixed effects regressions are neglecting \dQuote{cross-cluster differences
in the effects of lower-level controls (which) reduces the precision of
estimated context effects, resulting in unnecessarily wide confidence
intervals and low statistical power} (\cite{Heisig et al. 2017}).
}
\subsection{Terminology}{
The group-meaned variable is simply the mean of an independent variable
within each group (or id-level or cluster) represented by \code{group}.
It represents the cluster-mean of an independent variable. The regression
coefficient of a group-meaned variable is the \emph{between-subject-effect}.
The de-meaned variable is then the centered version of the group-meaned
variable. De-meaning is sometimes also called person-mean centering or
centering within clusters. The regression coefficient of a de-meaned
variable represents the \emph{within-subject-effect}.
}
\subsection{De-meaning with continuous predictors}{
For continuous time-varying predictors, the recommendation is to include
both their de-meaned and group-meaned versions as fixed effects, but not
the raw (untransformed) time-varying predictors themselves. The de-meaned
predictor should also be included as random effect (random slope). In
regression models, the coefficient of the de-meaned predictors indicates
the within-subject effect, while the coefficient of the group-meaned
predictor indicates the between-subject effect.
}
\subsection{De-meaning with binary predictors}{
For binary time-varying predictors, there are two recommendations. First
is to include the raw (untransformed) binary predictor as fixed effect
only and the \emph{de-meaned} variable as random effect (random slope).
The alternative would be to add the de-meaned version(s) of binary
time-varying covariates as additional fixed effect as well (instead of
adding it as random slope). Centering time-varying binary variables to
obtain within-effects (level 1) isn't necessary. They have a sensible
interpretation when left in the typical 0/1 format (\cite{Hoffmann 2015,
chapter 8-2.I}). \code{demean()} will thus coerce categorical time-varying
predictors to numeric to compute the de- and group-meaned versions for
these variables, where the raw (untransformed) binary predictor and the
de-meaned version should be added to the model.
}
\subsection{De-meaning of factors with more than 2 levels}{
Factors with more than two levels are demeaned in two ways: first, these
are also converted to numeric and de-meaned; second, dummy variables
are created (binary, with 0/1 coding for each level) and these binary
dummy-variables are de-meaned in the same way (as described above).
Packages like \pkg{panelr} internally convert factors to dummies before
demeaning, so this behaviour can be mimicked here.
}
\subsection{De-meaning interaction terms}{ There are multiple ways to deal
with interaction terms of within- and between-effects. A classical approach
is to simply use the product term of the de-meaned variables (i.e.
introducing the de-meaned variables as interaction term in the model
formula, e.g. \code{y ~ x_within * time_within}). This approach, however,
might be subject to bias (see \cite{Giesselmann & Schmidt-Catran 2020}).
\cr \cr
Another option is to first calculate the product term and then apply the
de-meaning to it. This approach produces an estimator \dQuote{that reflects
unit-level differences of interacted variables whose moderators vary
within units}, which is desirable if \emph{no} within interaction of
two time-dependent variables is required. \cr \cr
A third option, when the interaction should result in a genuine within
estimator, is to "double de-mean" the interaction terms
(\cite{Giesselmann & Schmidt-Catran 2018}), however, this is currently
not supported by \code{demean()}. If this is required, the \code{wmb()}
function from the \pkg{panelr} package should be used. \cr \cr
To de-mean interaction terms for within-between models, simply specify
the term as interaction for the \code{select}-argument, e.g.
\code{select = "a*b"} (see 'Examples').
}
\subsection{Analysing panel data with mixed models using lme4}{
A description of how to translate the
formulas described in \emph{Bell et al. 2018} into R using \code{lmer()}
from \pkg{lme4} can be found in
\href{https://easystats.github.io/datawizard/articles/demean.html}{this vignette}.
}
}
\examples{
data(iris)
iris$ID <- sample(1:4, nrow(iris), replace = TRUE) # fake-ID
iris$binary <- as.factor(rbinom(150, 1, .35)) # binary variable

x <- demean(iris, select = c("Sepal.Length", "Petal.Length"), group = "ID")
head(x)

x <- demean(iris, select = c("Sepal.Length", "binary", "Species"), group = "ID")
head(x)


# demean interaction term x*y
dat <- data.frame(
  a = c(1, 2, 3, 4, 1, 2, 3, 4),
  x = c(4, 3, 3, 4, 1, 2, 1, 2),
  y = c(1, 2, 1, 2, 4, 3, 2, 1),
  ID = c(1, 2, 3, 1, 2, 3, 1, 2)
)
demean(dat, select = c("a", "x*y"), group = "ID")

# or in formula-notation
demean(dat, select = ~ a + x * y, group = ~ID)
}
\references{
\itemize{
\item 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.

\item Bell A, Fairbrother M, Jones K. 2019. Fixed and Random Effects
Models: Making an Informed Choice. Quality & Quantity (53); 1051-1074

\item Bell A, Jones K. 2015. Explaining Fixed Effects: Random Effects
Modeling of Time-Series Cross-Sectional and Panel Data. Political Science
Research and Methods, 3(1), 133–153.

\item Gelman A, Hill J. 2007. Data Analysis Using Regression and
Multilevel/Hierarchical Models. Analytical Methods for Social Research.
Cambridge, New York: Cambridge University Press

\item Giesselmann M, Schmidt-Catran, AW. 2020. Interactions in fixed
effects regression models. Sociological Methods & Research, 1–28.
https://doi.org/10.1177/0049124120914934

\item Heisig JP, Schaeffer M, Giesecke J. 2017. The Costs of Simplicity:
Why Multilevel Models May Benefit from Accounting for Cross-Cluster
Differences in the Effects of Controls. American Sociological Review 82
(4): 796–827.

\item Hoffman L. 2015. Longitudinal analysis: modeling within-person
fluctuation and change. New York: Routledge
}
}
\seealso{
If grand-mean centering (instead of centering within-clusters)
is required, see \code{\link[=center]{center()}}.
}
