fix docs

R/contrs.R

@@ -5,34 +5,35 @@
 #' @inheritParams stats::contr.sum
 #'
 #' @details
-#' In effects coding, unlike treatment/dummy coding ([stats::contr.treatment()]), each
-#' contrast sums to 0. In regressions models, this results in an intercept that
-#' represents the (unweighted) average of the group means. In ANOVA settings,
-#' this also guarantees that lower order effects represent _main_ effects (and
-#' not _simple_ or _conditional_ effects, as is the case when using R's default
-#' [stats::contr.treatment()]).
+#' In effects coding, unlike treatment/dummy coding
+#' ([stats::contr.treatment()]), each contrast sums to 0. In regressions models,
+#' this results in an intercept that represents the (unweighted) average of the
+#' group means. In ANOVA settings, this also guarantees that lower order effects
+#' represent _main_ effects (and not _simple_ or _conditional_ effects, as is
+#' the case when using R's default [stats::contr.treatment()]).
 #' \cr\cr
-#' Deviation coding (`contr.deviation`) is a type of effects coding.
-#' With deviation coding, the coefficients for factor variables are interpreted
-#' as the difference of each factor level from the base level
-#' (this is the same interpretation as with treatment/dummy coding).
-#' For example, for a factor `group` with levels "A", "B", and "C", with `contr.devation`,
-#' the intercept represents the overall mean (average of the group means for the 3 groups),
+#' Deviation coding (`contr.deviation`) is a type of effects coding. With
+#' deviation coding, the coefficients for factor variables are interpreted as
+#' the difference of each factor level from the base level (this is the same
+#' interpretation as with treatment/dummy coding). For example, for a factor
+#' `group` with levels "A", "B", and "C", with `contr.devation`, the intercept
+#' represents the overall mean (average of the group means for the 3 groups),
 #' and the coefficients `groupB` and `groupC` represent the differences between
 #' the A group mean and the B and C group means, respectively.
 #' \cr\cr
-#' Sum coding ([stats::contr.sum()]) is another type of effects coding.
-#' With sum coding, the coefficients for factor variables are interpreted
-#' as the difference of each factor level from **the grand (across-groups) mean**.
-#' For example, for a factor `group` with levels "A", "B", and "C", with `contr.sum`,
-#' the intercept represents the overall mean (average of the group means for the 3 groups),
-#' and the coefficients `group1` and `group2` represent the differences the
+#' Sum coding ([stats::contr.sum()]) is another type of effects coding. With sum
+#' coding, the coefficients for factor variables are interpreted as the
+#' difference of each factor level from **the grand (across-groups) mean**. For
+#' example, for a factor `group` with levels "A", "B", and "C", with
+#' `contr.sum`, the intercept represents the overall mean (average of the group
+#' means for the 3 groups), and the coefficients `group1` and `group2` represent
+#' the differences the
 #' **A** and **B** group means from the overall mean, respectively.
 #'
 #' @seealso [stats::contr.sum()]
 #'
 #' @examples
-#' \dontrun{
+#' if (FALSE) {
 #' data("mtcars")
 #'
 #' mtcars <- data_modify(mtcars, cyl = factor(cyl))
@@ -62,7 +63,7 @@
 #' #> 8         -1.000 0.000 1.000   # 3rd level - 1st level
 #'
 #' ## With Interactions -----------------------------------------
-#' mtcars <- data_modify(mtcars, am = factor(am))
+#' mtcars <- data_modify(mtcars, am = C(am, contr = contr.deviation))
 #' mtcars <- data_arrange(mtcars, select = c("cyl", "am"))
 #'
 #' mm <- unique(model.matrix(~ cyl * am, data = mtcars))