docs

R/methods_aod.R

@@ -19,7 +19,7 @@
 #' `"conditional"` or `"full"` (for `MuMIn::model.avg()`) or `"all"`. See section
 #' _Model components_ for an overview of possible options for `component`.
 #' @inheritParams model_parameters.default
-#' @inheritParams model_parameters.stanreg
+#' @inheritParams model_parameters.brmsfit
 #' @inheritParams simulate_model
 #'
 #' @seealso [insight::standardize_names()] to rename columns into a consistent,

R/methods_base.R

@@ -1,3 +1,4 @@
+#' @rdname model_parameters.brmsfit
 #' @export
 model_parameters.data.frame <- function(model,
                                         as_draws = FALSE,

R/methods_brms.R

@@ -1,4 +1,69 @@
+#' @title Parameters from Bayesian Models
+#' @name model_parameters.brmsfit
+#'
+#' @description
+#' Model parameters from Bayesian models. This function internally calls
+#' [`bayestestR::describe_posterior()`] to get the relvant information for
+#' the output.
+#'
+#' @param model Bayesian model (including SEM from **blavaan**. May also be
+#' a data frame with posterior samples, however, `as_draws` must be set to
+#' `TRUE` (else, for data frames `NULL` is returned).
+#' @param ci Credible Interval (CI) level. Default to `0.95` (`95%`). See
+#' [bayestestR::ci()] for further details.
+#' @param group_level Logical, for multilevel models (i.e. models with random
+#' effects) and when `effects = "all"` or `effects = "random"`,
+#' include the parameters for each group level from random effects. If
+#' `group_level = FALSE` (the default), only information on SD and COR
+#' are shown.
+#' @param component Which type of parameters to return, such as parameters for the
+#' conditional model, the zero-inflation part of the model, the dispersion
+#' term, or other auxiliary parameters be returned? Applies to models with
+#' zero-inflation and/or dispersion formula, or if parameters such as `sigma`
+#' should be included. 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`, or `smooth_terms`, 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`, or `beta`
+#' (and other auxiliary parameters) are returned.
+#' @param as_draws Logical, if `TRUE` and `model` is of class `data.frame`,
+#' the data frame is treated as posterior samples and handled similar to
+#' Bayesian models. All arguments in `...` are passed to
+#' `model_parameters.draws()`.
+#' @inheritParams model_parameters.default
+#' @inheritParams bayestestR::describe_posterior
 #' @inheritParams insight::get_parameters
+#'
+#' @seealso [insight::standardize_names()] to rename columns into a consistent,
+#' standardized naming scheme.
+#'
+#' @note When `standardize = "refit"`, columns `diagnostic`, `bf_prior` and
+#' `priors` refer to the *original* `model`. If `model` is a data frame,
+#' arguments `diagnostic`, `bf_prior` and `priors` are ignored.
+#'
+#' There is also a
+#' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html)
+#' implemented in the [**see**-package](https://easystats.github.io/see/).
+#'
+#' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
+#'
+#' @inheritSection model_parameters.zcpglm Model components
+#'
+#' @examples
+#' \donttest{
+#' library(parameters)
+#' if (require("rstanarm")) {
+#'   model <- suppressWarnings(stan_glm(
+#'     Sepal.Length ~ Petal.Length * Species,
+#'     data = iris, iter = 500, refresh = 0
+#'   ))
+#'   model_parameters(model)
+#' }
+#' }
+#' @return A data frame of indices related to the model's parameters.
 #' @export
 model_parameters.brmsfit <- function(model,
                                      centrality = "median",

R/methods_glmmTMB.R

@@ -55,7 +55,7 @@
 #'   up computation of the summary table.
 #'
 #' @inheritParams model_parameters.default
-#' @inheritParams model_parameters.stanreg
+#' @inheritParams model_parameters.brmsfit
 #' @inheritParams simulate_model
 #'
 #' @inheritSection model_parameters.zcpglm Model components

R/methods_hclust.R

@@ -3,7 +3,9 @@
 #' Format cluster models obtained for example by [kmeans()].
 #'
 #' @param model Cluster model.
-#' @inheritParams model_parameters.default
+#' @param data A data frame.
+#' @param clusters A vector with clusters assignments (must be same length as
+#' rows in data).
 #' @param ... Arguments passed to or from other methods.
 #'
 #' @examplesIf require("factoextra", quietly = TRUE) && require("dbscan", quietly = TRUE) && require("cluster", quietly = TRUE) && require("fpc", quietly = TRUE)
@@ -123,9 +125,6 @@ model_parameters.hclust <- function(model, data = NULL, clusters = NULL, ...) {
 }
 
 
-
-
-#' @inheritParams n_clusters
 #' @export
 model_parameters.pvclust <- function(model, data = NULL, clusters = NULL, ci = 0.95, ...) {
   if (is.null(data)) {

R/methods_rstanarm.R

@@ -1,65 +1,3 @@
-#' Parameters from Bayesian Models
-#'
-#' Parameters from Bayesian models.
-#'
-#' @param model Bayesian model (including SEM from **blavaan**. May also be
-#'   a data frame with posterior samples, however, `as_draws` must be set to
-#'   `TRUE` (else, for data frames `NULL` is returned).
-#' @param ci Credible Interval (CI) level. Default to `0.95` (`95%`). See
-#'   [bayestestR::ci()] for further details.
-#' @param group_level Logical, for multilevel models (i.e. models with random
-#'   effects) and when `effects = "all"` or `effects = "random"`,
-#'   include the parameters for each group level from random effects. If
-#'   `group_level = FALSE` (the default), only information on SD and COR
-#'   are shown.
-#' @param component Which type of parameters to return, such as parameters for the
-#'   conditional model, the zero-inflation part of the model, the dispersion
-#'   term, or other auxiliary parameters be returned? Applies to models with
-#'   zero-inflation and/or dispersion formula, or if parameters such as `sigma`
-#'   should be included. 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`, or `smooth_terms`, 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`, or `beta`
-#'   (and other auxiliary parameters) are returned.
-#' @param as_draws Logical, if `TRUE` and `model` is of class `data.frame`,
-#'   the data frame is treated as posterior samples and handled similar to
-#'   Bayesian models. All arguments in `...` are passed to
-#'   `model_parameters.draws()`.
-#' @inheritParams model_parameters.default
-#' @inheritParams bayestestR::describe_posterior
-#' @inheritParams insight::get_parameters
-#'
-#' @seealso [insight::standardize_names()] to rename columns into a consistent,
-#' standardized naming scheme.
-#'
-#' @note When `standardize = "refit"`, columns `diagnostic`, `bf_prior` and
-#' `priors` refer to the *original* `model`. If `model` is a data frame,
-#' arguments `diagnostic`, `bf_prior` and `priors` are ignored.
-#'
-#' There is also a
-#' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html)
-#' implemented in the [**see**-package](https://easystats.github.io/see/).
-#'
-#' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
-#'
-#' @inheritSection model_parameters.zcpglm Model components
-#'
-#' @examples
-#' \donttest{
-#' library(parameters)
-#' if (require("rstanarm")) {
-#'   model <- suppressWarnings(stan_glm(
-#'     Sepal.Length ~ Petal.Length * Species,
-#'     data = iris, iter = 500, refresh = 0
-#'   ))
-#'   model_parameters(model)
-#' }
-#' }
-#' @return A data frame of indices related to the model's parameters.
 #' @export
 model_parameters.stanreg <- function(model,
                                      centrality = "median",