rd cleanup

R/bayesfactor_models.R

@@ -9,13 +9,13 @@
 #' @param ... Fitted models (see details), all fit on the same data, or a single
 #'   `BFBayesFactor` object (see 'Details'). Ignored in `as.matrix()`,
 #'   `update()`. If the following named arguments are present, they are passed
-#'   to [insight::get_loglikelihood] (see details):
+#'   to [`insight::get_loglikelihood()`] (see details):
 #'   - `estimator` (defaults to `"ML"`)
 #'   - `check_response`  (defaults to `FALSE`)
 #' @param denominator Either an integer indicating which of the models to use as
 #'   the denominator, or a model to be used as a denominator. Ignored for
 #'   `BFBayesFactor`.
-#' @param object,x A [bayesfactor_models()] object.
+#' @param object,x A [`bayesfactor_models()`] object.
 #' @param subset Vector of model indices to keep or remove.
 #' @param reference Index of model to reference to, or `"top"` to
 #'   reference to the best model, or `"bottom"` to reference to the worst
@@ -26,9 +26,9 @@
 #' implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
 #'
 #' @details
-#' If the passed models are supported by \pkg{insight} the DV of all models will be tested for equality
-#' (else this is assumed to be true), and the models' terms will be extracted (allowing for follow-up
-#' analysis with `bayesfactor_inclusion`).
+#' If the passed models are supported by **insight** the DV of all models will
+#' be tested for equality (else this is assumed to be true), and the models'
+#' terms will be extracted (allowing for follow-up analysis with `bayesfactor_inclusion`).
 #'
 #' - For `brmsfit` or `stanreg` models, Bayes factors are computed using the \CRANpkg{bridgesampling} package.
 #'   - `brmsfit` models must have been fitted with `save_pars = save_pars(all = TRUE)`.
@@ -44,9 +44,9 @@
 #' testing is substantially larger than for estimation (the default of 4000
 #' samples may not be enough in many cases). A conservative rule of thumb is to
 #' obtain 10 times more samples than would be required for estimation
-#' (\cite{Gronau, Singmann, & Wagenmakers, 2017}). If less than 40,000 samples
+#' (_Gronau, Singmann, & Wagenmakers, 2017_). If less than 40,000 samples
 #' are detected, `bayesfactor_models()` gives a warning.
-#' \cr \cr
+#'
 #' See also [the Bayes factors vignette](https://easystats.github.io/bayestestR/articles/bayes_factors.html).
 #'
 #' @inheritSection bayesfactor_parameters Interpreting Bayes Factors

R/ci.R

@@ -4,36 +4,37 @@
 #' (SI) for Bayesian and frequentist models. The Documentation is accessible
 #' for:
 #'
-#' \itemize{
-#'  \item [Bayesian models](https://easystats.github.io/bayestestR/articles/credible_interval.html)
-#'  \item [Frequentist models](https://easystats.github.io/parameters/reference/ci.default.html)
-#' }
+#' - [Bayesian models](https://easystats.github.io/bayestestR/articles/credible_interval.html)
+#' - [Frequentist models](https://easystats.github.io/parameters/reference/ci.default.html)
 #'
-#' @param x A `stanreg` or `brmsfit` model, or a vector representing a posterior distribution.
-#' @param method Can be ['ETI'][eti] (default), ['HDI'][hdi], ['BCI'][bci], ['SPI'][spi] or ['SI'][si].
+#' @param x A `stanreg` or `brmsfit` model, or a vector representing a posterior
+#' distribution.
+#' @param method Can be ["ETI"][eti] (default), ["HDI"][hdi], ["BCI"][bci],
+#' ["SPI"][spi] or ["SI"][si].
 #' @param ci Value or vector of probability of the CI (between 0 and 1)
-#'   to be estimated. Default to `.95` (`95%`).
+#'   to be estimated. Default to `0.95` (`95%`).
 #' @inheritParams hdi
 #' @inheritParams si
 #' @inherit hdi seealso
 #' @family ci
 #'
 #' @return A data frame with following columns:
-#'   \itemize{
-#'     \item `Parameter` The model parameter(s), if `x` is a model-object. If `x` is a vector, this column is missing.
-#'     \item `CI` The probability of the credible interval.
-#'     \item `CI_low`, `CI_high` The lower and upper credible interval limits for the parameters.
-#'   }
+#'
+#' - `Parameter` The model parameter(s), if `x` is a model-object. If `x` is a
+#'   vector, this column is missing.
+#' - `CI` The probability of the credible interval.
+#' - `CI_low`, `CI_high` The lower and upper credible interval limits for the parameters.
 #'
 #' @note When it comes to interpretation, we recommend thinking of the CI in terms of
 #'   an "uncertainty" or "compatibility" interval, the latter being defined as
-#'   \dQuote{Given any value in the interval and the background assumptions,
-#'   the data should not seem very surprising} (\cite{Gelman & Greenland 2019}).
-#'   \cr \cr
-#'   There is also a [`plot()`-method](https://easystats.github.io/see/articles/bayestestR.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
+#'   "Given any value in the interval and the background assumptions,
+#'   the data should not seem very surprising" (_Gelman & Greenland 2019_).
 #'
-#' @references Gelman A, Greenland S. Are confidence intervals better termed "uncertainty intervals"? BMJ 2019;l5381. 10.1136/bmj.l5381
+#'   There is also a [`plot()`-method](https://easystats.github.io/see/articles/bayestestR.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
 #'
+#' @references
+#' Gelman A, Greenland S. Are confidence intervals better termed "uncertainty
+#' intervals"? BMJ 2019;l5381. 10.1136/bmj.l5381
 #'
 #' @examplesIf require("rstanarm", quietly = TRUE)
 #' library(bayestestR)
@@ -138,9 +139,12 @@ ci <- function(x, ...) {
       )
     )
   } else {
-    insight::format_error(
-      "`method` should be 'ETI' (for equal-tailed interval),'HDI' (for highest density interval), 'BCI' (for bias corrected and accelerated bootstrap intervals), 'SPI' (for shortest probability interval) or 'SI' (for support interval)."
-    )
+    insight::format_error(paste0(
+      "`method` should be 'ETI' (for equal-tailed interval), ",
+      "'HDI' (for highest density interval), 'BCI' (for bias corrected and ",
+      "accelerated bootstrap intervals), 'SPI' (for shortest probability ",
+      "interval) or 'SI' (for support interval)."
+    ))
   }
 }
 
@@ -157,7 +161,6 @@ ci.numeric <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...
 ci.data.frame <- ci.numeric
 
 
-
 #' @export
 ci.draws <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...) {
   .ci_bayesian(.posterior_draws_to_df(x), ci = ci, method = method, verbose = verbose, BF = BF, ...)
@@ -185,7 +188,6 @@ ci.emmGrid <- function(x, ci = NULL, ...) {
 ci.emm_list <- ci.emmGrid
 
 
-
 #' @rdname ci
 #' @export
 ci.sim.merMod <- function(x,
@@ -207,7 +209,6 @@ ci.sim.merMod <- function(x,
 }
 
 
-
 #' @rdname ci
 #' @export
 ci.sim <- function(x,
@@ -227,7 +228,6 @@ ci.sim <- function(x,
 }
 
 
-
 #' @rdname ci
 #' @export
 ci.stanreg <- function(x,
@@ -285,22 +285,17 @@ ci.brmsfit <- function(x,
   )
 }
 
-
 #' @export
 ci.stanfit <- ci.stanreg
 
-
 #' @export
 ci.blavaan <- ci.stanreg
 
-
 #' @rdname ci
 #' @export
 ci.BFBayesFactor <- ci.numeric
 
 
-
-
 #' @rdname ci
 #' @export
 ci.MCMCglmm <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, ...) {
@@ -338,22 +333,17 @@ ci.bcplm <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, ...) {
   ci(insight::get_parameters(x), ci = ci, method = method, verbose = verbose, ...)
 }
 
-
 #' @export
 ci.blrm <- ci.bcplm
 
-
 #' @export
 ci.mcmc <- ci.bcplm
 
-
 #' @export
 ci.mcmc.list <- ci.bcplm
 
-
 #' @export
 ci.BGGM <- ci.bcplm
 
-
 #' @export
 ci.get_predicted <- ci.data.frame

R/describe_posterior.R

@@ -92,7 +92,7 @@
 #'
 #'   # emmeans estimates
 #'   # -----------------------------------------------
-#'   describe_posterior(suppressWarnings(emtrends(model, ~1, "wt")))
+#'   describe_posterior(emtrends(model, ~1, "wt"))
 #' }
 #'
 #' # BayesFactor objects

R/equivalence_test.R

@@ -89,9 +89,7 @@
 #' plot(test)
 #'
 #' library(emmeans)
-#' equivalence_test(
-#'   suppressWarnings(emtrends(model, ~1, "wt", data = mtcars))
-#' )
+#' equivalence_test(emtrends(model, ~1, "wt", data = mtcars))
 #'
 #' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)

R/estimate_density.R

@@ -82,9 +82,7 @@
 #' head(estimate_density(model))
 #'
 #' library(emmeans)
-#' head(estimate_density(suppressWarnings(
-#'   emtrends(model, ~1, "wt", data = mtcars)
-#' )))
+#' head(estimate_density(emtrends(model, ~1, "wt", data = mtcars)))
 #'
 #' # brms models
 #' # -----------------------------------------------

R/eti.R

@@ -31,7 +31,7 @@
 #' eti(model, ci = c(0.80, 0.89, 0.95))
 #'
 #' library(emmeans)
-#' eti(suppressWarnings(emtrends(model, ~1, "wt", data = mtcars)))
+#' eti(emtrends(model, ~1, "wt", data = mtcars))
 #'
 #' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)

R/hdi.R

@@ -31,22 +31,24 @@
 #' from each tail of the distribution and always include the median, the HDI is
 #' *not* equal-tailed and therefore always includes the mode(s) of posterior
 #' distributions. While this can be useful to better represent the credibility
-#' mass of a distribution, the HDI also has some limitations. See [spi()] for
+#' mass of a distribution, the HDI also has some limitations. See [`spi()`] for
 #' details.
-#' \cr \cr
+#'
 #' The [`95%` or `89%` Credible Intervals (CI)](https://easystats.github.io/bayestestR/articles/credible_interval.html)
-#' are two reasonable ranges to characterize the uncertainty related to the estimation (see [here](https://easystats.github.io/bayestestR/articles/credible_interval.html) for a discussion about the differences between these two values).
-#' \cr
+#' are two reasonable ranges to characterize the uncertainty related to the
+#' estimation (see [here](https://easystats.github.io/bayestestR/articles/credible_interval.html)
+#' for a discussion about the differences between these two values).
+#'
 #' The `89%` intervals (`ci = 0.89`) are deemed to be more stable than, for
-#' instance, `95%` intervals (\cite{Kruschke, 2014}). An effective sample size
+#' instance, `95%` intervals (_Kruschke, 2014_). An effective sample size
 #' of at least 10.000 is recommended if one wants to estimate `95%` intervals
-#' with high precision (\cite{Kruschke, 2014, p. 183ff}). Unfortunately, the
+#' with high precision (_Kruschke, 2014, p. 183ff_). Unfortunately, the
 #' default number of posterior samples for most Bayes packages (e.g., `rstanarm`
 #' or `brms`) is only 4.000 (thus, you might want to increase it when fitting
 #' your model). Moreover, 89 indicates the arbitrariness of interval limits -
 #' its only remarkable property is being the highest prime number that does not
-#' exceed the already unstable `95%` threshold (\cite{McElreath, 2015}).
-#' \cr
+#' exceed the already unstable `95%` threshold (_McElreath, 2015_).
+#'
 #' However, `95%` has some [advantages
 #' too](https://easystats.github.io/blog/posts/bayestestr_95/). For instance, it
 #' shares (in the case of a normal posterior distribution) an intuitive
@@ -55,17 +57,17 @@
 #' makes analyses more conservative (i.e., the probability of covering 0 is
 #' larger for the `95%` CI than for lower ranges such as `89%`), which is a good
 #' thing in the context of the reproducibility crisis.
-#' \cr \cr
+#'
 #' A `95%` equal-tailed interval (ETI) has `2.5%` of the distribution on either
 #' side of its limits. It indicates the 2.5th percentile and the 97.5h
 #' percentile. In symmetric distributions, the two methods of computing credible
 #' intervals, the ETI and the [HDI][hdi], return similar results.
-#' \cr \cr
+#'
 #' This is not the case for skewed distributions. Indeed, it is possible that
 #' parameter values in the ETI have lower credibility (are less probable) than
 #' parameter values outside the ETI. This property seems undesirable as a summary
 #' of the credible values in a distribution.
-#' \cr \cr
+#'
 #' On the other hand, the ETI range does change when transformations are applied
 #' to the distribution (for instance, for a log odds scale to probabilities):
 #' the lower and higher bounds of the transformed distribution will correspond
@@ -96,9 +98,7 @@
 #' bayestestR::hdi(model, ci = c(0.80, 0.90, 0.95))
 #'
 #' library(emmeans)
-#' bayestestR::hdi(suppressWarnings(
-#'   emtrends(model, ~1, "wt", data = mtcars)
-#' ))
+#' bayestestR::hdi(emtrends(model, ~1, "wt", data = mtcars))
 #'
 #' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)

R/p_direction.R

@@ -106,9 +106,7 @@
 #' # emmeans
 #' # -----------------------------------------------
 #' if (require("emmeans")) {
-#'   p_direction(suppressWarnings(
-#'     emtrends(model, ~1, "wt", data = mtcars)
-#'   ))
+#'   p_direction(emtrends(model, ~1, "wt", data = mtcars))
 #' }
 #'
 #' # brms models

R/point_estimate.R

@@ -44,7 +44,7 @@
 #' # -----------------------------------------------
 #' library(emmeans)
 #' point_estimate(
-#'   suppressWarnings(emtrends(model, ~1, "wt", data = mtcars)),
+#'   emtrends(model, ~1, "wt", data = mtcars),
 #'   centrality = c("median", "MAP")
 #' )
 #'