docs

R/find_parameters_mfx.R

@@ -10,15 +10,17 @@
 #' models with zero-inflated and/or dispersion formula, or to models with
 #' instrumental variables (so called fixed-effects regressions), or models with
 #' marginal effects from **mfx**. See details in section _Model Components_ .May
-#' be abbreviated. Note that the *conditional* component is also called *count*
-#' or *mean* component, depending on the model. There are three convenient
-#' shortcuts: `component = "all"` returns all possible parameters. If `component
-#' = "location"`, location parameters such as `conditional`, `zero_inflated`,
-#' `smooth_terms`, or `instruments` are returned (everything that are fixed or
-#' random effects - depending on the `effects` argument - but no auxiliary
-#' parameters). For `component = "distributional"` (or `"auxiliary"`),
-#' components like `sigma`, `dispersion`, `beta` or `precision` (and other
-#' auxiliary parameters) are returned.
+#' be abbreviated. Note that the *conditional* component also refers to the
+#' *count* or *mean* component - names may differ, depending on the modeling
+#' package. There are three convenient shortcuts:
+#' - `component = "all"` returns all possible parameters.
+#' - If `component = "location"`, location parameters such as `conditional`,
+#'   `zero_inflated`, `smooth_terms`, or `instruments` are returned (everything
+#'   that are fixed or random effects - depending on the `effects` argument -
+#'   but no auxiliary parameters).
+#' - For `component = "distributional"` (or `"auxiliary"`), components like
+#'   `sigma`, `dispersion`, `beta` or `precision` (and other auxiliary
+#'   parameters) are returned.
 #' @param ... Currently not used.
 #' @inheritParams find_parameters
 #' @inheritParams find_predictors

R/find_predictors.R

@@ -50,11 +50,16 @@
 #' **Special models**
 #'
 #' Some model classes also allow rather uncommon options. These are:
-#' - `mhurdle`: `"infrequent_purchase"`, `"ip"`, and `"auxiliary"`
-#' - `BGGM`: `"correlation"` and `"intercept"`
-#' - `BFBayesFactor`, `glmx`: `"extra"`
-#' - `averaging`:`"conditional"` and `"full"`
-#' - `mjoint`: `"survival"`
+#' - **mhurdle**: `"infrequent_purchase"`, `"ip"`, and `"auxiliary"`
+#' - **BGGM**: `"correlation"` and `"intercept"`
+#' - **BFBayesFactor**, **glmx**: `"extra"`
+#' - **averaging**:`"conditional"` and `"full"`
+#' - **mjoint**: `"survival"`
+#' - **mfx**: `"precision"`, `"marginal"`
+#'
+#' For models of class `brmsfit` (package **brms**), `component`, even more
+#' options are possible for the `component` argument, which are not all
+#' documented in detail here.
 #'
 #' @section Parameters, Variables, Predictors and Terms:
 #' There are four functions that return information about the variables in a