Simplify docs

NAMESPACE

@@ -659,7 +659,7 @@ S3method(get_parameters,glimML)
 S3method(get_parameters,glmgee)
 S3method(get_parameters,glmm)
 S3method(get_parameters,glmmTMB)
-S3method(get_parameters,glmmadmb)
+S3method(get_parameters,glmmTMBadmb)
 S3method(get_parameters,glmx)
 S3method(get_parameters,hglm)
 S3method(get_parameters,htest)

R/find_parameters_bayesian.R

@@ -8,8 +8,6 @@
 #'
 #' @param parameters Regular expression pattern that describes the parameters that
 #' should be returned.
-#' @param effects Should parameters for fixed effects, random effects
-#' or both be returned? Only applies to mixed models. May be abbreviated.
 #' @param ... Currently not used.
 #' @inheritParams find_parameters
 #' @inheritParams find_parameters.betamfx

R/find_parameters_mfx.R

@@ -2,27 +2,31 @@
 #' @name find_parameters.betamfx
 #'
 #' @description Returns the names of model parameters, like they typically
-#'     appear in the `summary()` output.
+#' appear in the `summary()` output.
 #'
-#' @param component Which type of parameters to return, such as parameters for the
-#'   conditional model, the zero-inflated part of the model, the dispersion
-#'   term, the instrumental variables or marginal effects be returned? Applies
-#'   to 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**. 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.
+#' @param component Which type of parameters to return, such as parameters for
+#' the conditional model, the zero-inflated part of the model, the dispersion
+#' term, the instrumental variables or marginal effects be returned? Applies to
+#' 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 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
 #'
+#' @inheritSection find_predictors Model components
+#'
 #' @return A list of parameter names. The returned list may have following
 #' elements:
 #'

R/find_parameters_mixed.R

@@ -15,13 +15,16 @@
 #'   are fixed or random effects - depending on the `effects` argument -
 #'   but no auxiliary parameters). For `component = "distributional"` (or
 #'   `"auxiliary"`), components like `sigma` or `dispersion` (and
-#'   other auxiliary parameters) are returned.
+#'   other auxiliary parameters) are returned. See section _Model Component_
+#'   for details.
 #' @param ... Currently not used.
 #' @inheritParams find_parameters
 #' @inheritParams find_parameters.betamfx
 #' @inheritParams find_parameters.BGGM
 #' @inheritParams find_predictors
 #'
+#' @inheritSection find_predictors Model components
+#'
 #' @return A list of parameter names. The returned list may have following
 #' elements, usually returned based on the combination of the `effects` and
 #' `component` arguments:
@@ -47,7 +50,10 @@
 #' @export
 find_parameters.glmmTMB <- function(x, effects = "all", component = "all", flatten = FALSE, ...) {
   effects <- validate_argument(effects, c("all", "fixed", "random"))
-  component <- validate_argument(component, c("all", "conditional", "zi", "zero_inflated", "dispersion"))
+  component <- validate_argument(
+    component,
+    c("all", "conditional", "zi", "zero_inflated", "dispersion", "location", "distributional", "auxiliary")
+  )
 
   # installed
   check_if_installed("lme4")
@@ -151,7 +157,10 @@ find_parameters.nlmerMod <- function(x, effects = "all", component = "all", flat
 #' @export
 find_parameters.hglm <- function(x, effects = "all", component = "all", flatten = FALSE, ...) {
   effects <- validate_argument(effects, c("all", "fixed", "random"))
-  component <- validate_argument(component, c("all", "conditional", "dispersion"))
+  component <- validate_argument(
+    component,
+    c("all", "conditional", "dispersion", "location", "distributional", "auxiliary")
+  )
 
   fe <- x$fixef
   re <- x$ranef

R/find_parameters_zi.R

@@ -9,6 +9,8 @@
 #' @inheritParams find_parameters.betamfx
 #' @inheritParams find_predictors
 #'
+#' @inheritSection find_predictors Model components
+#'
 #' @return A list of parameter names. The returned list may have following
 #' elements:
 #'

R/find_predictors.R

@@ -7,8 +7,9 @@
 #' the original variable names from the data that was used to fit the model.
 #'
 #' @param x A fitted model.
-#' @param effects Should variables for fixed effects, random effects
-#' or both be returned? Only applies to mixed models. May be abbreviated.
+#' @param effects Should variables for fixed effects (`"fixed"`), random effects
+#' (`"random"`) or both (`"all"`) be returned? Only applies to mixed models. May
+#' be abbreviated.
 #' @param component Indicates which model component should be returned. E.g.,
 #' should all predictor variables, predictor variables for the conditional
 #' model, the zero-inflated part of the model, the dispersion term or the
@@ -49,11 +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

R/get_parameters.R

@@ -8,7 +8,7 @@
 #' - [Estimated marginal means][get_parameters.emmGrid] (**emmeans**)
 #' - [Generalized additive models][get_parameters.gamm] (**mgcv**, **VGAM**, ...)
 #' - [Marginal effects models][get_parameters.betamfx] (**mfx**)
-#' - [Mixed models][get_parameters.glmm] (**lme4**, **glmmTMB**, **GLMMadaptive**, ...)
+#' - [Mixed models][get_parameters.glmmTMB] (**lme4**, **glmmTMB**, **GLMMadaptive**, ...)
 #' - [Zero-inflated and hurdle models][get_parameters.zeroinfl] (**pscl**, ...)
 #' - [Models with special components][get_parameters.betareg] (**betareg**, **MuMIn**, ...)
 #' - [Hypothesis tests][get_parameters.htest] (`htest`)