docs

R/demean.R

@@ -167,12 +167,13 @@
 #' `demean()` can also handle cross-classified designs, where the data has two
 #' or more groups at the higher (i.e. second) level. In such cases, the
 #' `by`-argument can identify two or more variables that represent the
-#' group- or cluster-ID. The de-meaned variables for cross-classified designs
-#' are simply subtracting all group means from each individual value
-#' (see _Guo et al. 2024_ for details). Note that de-meaning for cross-classified
-#' designs is *not* equivalent to de-meaning of nested data structures from models
-#' with three or more levels, i.e. de-meaning is supposed to work for models
-#' like `y ~ x + (1|group1) + (1|group2)`, but *not* for models like
+#'  cross-classified group- or cluster-IDs. The de-meaned variables for
+#' cross-classified designs are simply subtracting all group means from each
+#' individual value, i.e. _fully cluster-mean-centering_ (see _Guo et al. 2024_
+#' for details). Note that de-meaning for cross-classified designs is *not*
+#' equivalent to de-meaning of nested data structures from models with three or
+#' more levels, i.e. de-meaning is supposed to work for models like
+#' `y ~ x + (1|group1) + (1|group2)`, but *not* for models like
 #' `y ~ x + (1|group1/group2)`.
 #'
 #' @section Analysing panel data with mixed models using lme4: