[paper] finalize

R/estimate_grouplevel.R

@@ -5,32 +5,31 @@
 #' which can be useful to add the random effects to the original data.
 #'
 #' @param model A mixed model with random effects.
-#' @param type If `"random"` (default), the coefficients are the ones estimated
-#'   natively by the model (as they are returned by, for instance,
-#'   `lme4::ranef()`). They correspond to the deviation of each individual group
-#'   from their fixed effect. As such, a coefficient close to 0 means that the
-#'   participants' effect is the same as the population-level effect (in other
-#'   words, it is "in the norm"). If `"total"`, it will return the sum of the
-#'   random effect and its corresponding fixed effects, which corresponds to
-#'   `coef()` (see `?coef.merMod`). Note that `type = "total"` currently don't
-#'   have uncertainty indices (such as SE and CI) for models from *lme4* or
-#'   *glmmTMB*, as these are not computable. However, for Bayesian models, it is
-#'   possible to compute them.
+#' @param type If `"random"` (default), the coefficients correspond to the
+#'   conditional estimates of  the random effects (as they are returned by
+#'   `lme4::ranef()`). They typically correspond to the deviation of each
+#'   individual group from their fixed effect. As such, a coefficient close to 0
+#'   means that the participants' effect is the same as the population-level
+#'   effect (in other words, it is "in the norm"). If `"total"`, it will return
+#'   the sum of the random effect and its corresponding fixed effects, which
+#'   corresponds to `coef()` (see `?coef.merMod`). Note that `type = "total"`
+#'   currently does not return uncertainty indices (such as SE and CI) for
+#'   models from *lme4* or *glmmTMB*, as these are not computable. However, for
+#'   Bayesian models, it is possible to compute them.
 #' @param ... Other arguments passed to or from other methods.
 #'
-#' @examplesIf all(insight::check_if_installed(c("see", "lme4"), quietly = TRUE))
+#' @examplesIf all(insight::check_if_installed(c("see", "lme4"), quietly = TRUE)) && packageVersion("insight") > "1.1.0" && packageVersion("parameters") > "0.24.1"
 #' # lme4 model
 #' data(mtcars)
 #' model <- lme4::lmer(mpg ~ hp + (1 | carb), data = mtcars)
 #' random <- estimate_grouplevel(model)
+#'
+#' # Show group-specific effects
 #' random
 #'
 #' # Visualize random effects
 #' plot(random)
 #'
-#' # Show group-specific effects
-#' estimate_grouplevel(model)
-#'
 #' # Reshape to wide data so that it matches the original dataframe...
 #' reshaped <- reshape_grouplevel(random, indices = c("Coefficient", "SE"))
 #'
@@ -75,7 +74,7 @@
   # Clean
   row.names(random) <- NULL
   random$Effects <- NULL
   if ("Component" %in% names(random) && insight::n_unique(random$Component) == 1 && unique(random$Component) == "conditional") {
     random$Component <- NULL
   }
 
@@ -87,22 +86,22 @@
     # Save brms name (just in case)
     random$Name <- random$Parameter
     # Filter out non-random effects
     random <- random[grepl("^r_", random$Parameter), ]
     # Remove Group from Level
     random$Level <- sapply(1:nrow(random), function(i) gsub(paste0("^", random$Group[i], "\\."), "", random$Level[i]))
     # Find the group name (what follows "r_" and before the first "[" or "__")
     random$Group <- gsub("^r_(.*?)(\\[.*|__.*)", "\\1", random$Name)
     # Keep Parameter what's in between [ and ]
     random$Parameter <- gsub("^r_.*?\\[(.*?)\\].*", "\\1", random$Name)
     # Remove Level from it
     random$Parameter <- sapply(1:nrow(random), function(i) gsub(paste0("^", random$Level[i], "\\,"), "", random$Parameter[i]))
     # remove temporary name column
     random$Name <- NULL
   }
 
   # Sort
   if ("Component" %in% names(random)) {
     random <- random[order(random$Component, random$Group, datawizard::coerce_to_numeric(random$Level), random$Parameter), ]
   } else {
     random <- random[order(random$Group, datawizard::coerce_to_numeric(random$Level), random$Parameter), ]
   }