fix examples

easystats · Oct 2, 2023 · ea13b35 · ea13b35
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diff --git a/R/bayesfactor_models.R b/R/bayesfactor_models.R
@@ -55,7 +55,7 @@
 #'   random effects) and their `log(BF)`s  (Use `as.numeric()` to extract the
 #'   non-log Bayes factors; see examples), that prints nicely.
 #'
-#' @examples
+#' @examplesIf require("lme4") && require("BayesFactor") && require("rstanarm") && require("brms")
 #' # With lm objects:
 #' # ----------------
 #' lm1 <- lm(mpg ~ 1, data = mtcars)
@@ -66,81 +66,72 @@
 #' # bayesfactor_models(lm2, lm3, lm4, denominator = lm1) # same result
 #' # bayesfactor_models(lm1, lm2, lm3, lm4, denominator = lm1) # same result
 #'
-#'
 #' update(BFM, reference = "bottom")
 #' as.matrix(BFM)
 #' as.numeric(BFM)
 #'
-#'
 #' lm2b <- lm(sqrt(mpg) ~ hp, data = mtcars)
 #' # Set check_response = TRUE for transformed responses
 #' bayesfactor_models(lm2b, denominator = lm2, check_response = TRUE)
 #'
 #' \donttest{
 #' # With lmerMod objects:
 #' # ---------------------
-#' if (require("lme4")) {
-#'   lmer1 <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
-#'   lmer2 <- lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species), data = iris)
-#'   lmer3 <- lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species) + (1 | Petal.Width),
-#'     data = iris
-#'   )
-#'   bayesfactor_models(lmer1, lmer2, lmer3,
-#'     denominator = 1,
-#'     estimator = "REML"
-#'   )
-#' }
+#' lmer1 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
+#' lmer2 <- lme4::lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species), data = iris)
+#' lmer3 <- lme4::lmer(
+#'   Sepal.Length ~ Petal.Length + (Petal.Length | Species) + (1 | Petal.Width),
+#'   data = iris
+#' )
+#' bayesfactor_models(lmer1, lmer2, lmer3,
+#'   denominator = 1,
+#'   estimator = "REML"
+#' )
 #'
 #' # rstanarm models
 #' # ---------------------
 #' # (note that a unique diagnostic_file MUST be specified in order to work)
-#' if (require("rstanarm")) {
-#'   stan_m0 <- stan_glm(Sepal.Length ~ 1,
-#'     data = iris,
-#'     family = gaussian(),
-#'     diagnostic_file = file.path(tempdir(), "df0.csv")
-#'   )
-#'   stan_m1 <- stan_glm(Sepal.Length ~ Species,
-#'     data = iris,
-#'     family = gaussian(),
-#'     diagnostic_file = file.path(tempdir(), "df1.csv")
-#'   )
-#'   stan_m2 <- stan_glm(Sepal.Length ~ Species + Petal.Length,
-#'     data = iris,
-#'     family = gaussian(),
-#'     diagnostic_file = file.path(tempdir(), "df2.csv")
-#'   )
-#'   bayesfactor_models(stan_m1, stan_m2, denominator = stan_m0, verbose = FALSE)
-#' }
+#' stan_m0 <- suppressWarnings(rstanarm::stan_glm(Sepal.Length ~ 1,
+#'   data = iris,
+#'   family = gaussian(),
+#'   diagnostic_file = file.path(tempdir(), "df0.csv")
+#' ))
+#' stan_m1 <- suppressWarnings(rstanarm::stan_glm(Sepal.Length ~ Species,
+#'   data = iris,
+#'   family = gaussian(),
+#'   diagnostic_file = file.path(tempdir(), "df1.csv")
+#' ))
+#' stan_m2 <- suppressWarnings(rstanarm::stan_glm(Sepal.Length ~ Species + Petal.Length,
+#'   data = iris,
+#'   family = gaussian(),
+#'   diagnostic_file = file.path(tempdir(), "df2.csv")
+#' ))
+#' bayesfactor_models(stan_m1, stan_m2, denominator = stan_m0, verbose = FALSE)
 #'
 #'
 #' # brms models
 #' # --------------------
 #' # (note the save_pars MUST be set to save_pars(all = TRUE) in order to work)
-#' if (require("brms")) {
-#'   brm1 <- brm(Sepal.Length ~ 1, data = iris, save_pars = save_pars(all = TRUE))
-#'   brm2 <- brm(Sepal.Length ~ Species, data = iris, save_pars = save_pars(all = TRUE))
-#'   brm3 <- brm(
-#'     Sepal.Length ~ Species + Petal.Length,
-#'     data = iris,
-#'     save_pars = save_pars(all = TRUE)
-#'   )
+#' brm1 <- brms::brm(Sepal.Length ~ 1, data = iris, save_pars = save_pars(all = TRUE))
+#' brm2 <- brms::brm(Sepal.Length ~ Species, data = iris, save_pars = save_pars(all = TRUE))
+#' brm3 <- brms::brm(
+#'   Sepal.Length ~ Species + Petal.Length,
+#'   data = iris,
+#'   save_pars = save_pars(all = TRUE)
+#' )
 #'
-#'   bayesfactor_models(brm1, brm2, brm3, denominator = 1, verbose = FALSE)
-#' }
+#' bayesfactor_models(brm1, brm2, brm3, denominator = 1, verbose = FALSE)
 #'
 #'
 #' # BayesFactor
 #' # ---------------------------
-#' if (require("BayesFactor")) {
-#'   data(puzzles)
-#'   BF <- anovaBF(RT ~ shape * color + ID,
-#'     data = puzzles,
-#'     whichRandom = "ID", progress = FALSE
-#'   )
-#'   BF
-#'   bayesfactor_models(BF) # basically the same
-#' }
+#' data(puzzles)
+#' BF <- BayesFactor::anovaBF(RT ~ shape * color + ID,
+#'   data = puzzles,
+#'   whichRandom = "ID", progress = FALSE
+#' )
+#' BF
+#' bayesfactor_models(BF) # basically the same
 #' }
 #'
 #' @references

diff --git a/R/p_map.R b/R/p_map.R
@@ -6,40 +6,41 @@
 #' Testing* framework. It corresponds to the density value at the null (e.g., 0)
 #' divided by the density at the Maximum A Posteriori (MAP).
 #'
-#' @details Note that this method is sensitive to the density estimation `method` (see the section in the examples below).
-#' \subsection{Strengths and Limitations}{
-#' **Strengths:** Straightforward computation. Objective property of the posterior distribution.
-#' \cr \cr
-#' **Limitations:** Limited information favoring the null hypothesis. Relates on density approximation. Indirect relationship between mathematical definition and interpretation. Only suitable for weak / very diffused priors.
-#' }
+#' @details Note that this method is sensitive to the density estimation `method`
+#' (see the section in the examples below).
+#'
+#' ## Strengths and Limitations
+#'
+#' **Strengths:** Straightforward computation. Objective property of the posterior
+#' distribution.
+#'
+#' **Limitations:** Limited information favoring the null hypothesis. Relates
+#' on density approximation. Indirect relationship between mathematical
+#' definition and interpretation. Only suitable for weak / very diffused priors.
 #'
 #' @inheritParams hdi
 #' @inheritParams density_at
 #' @inheritParams pd
 #'
-#' @examples
+#' @examplesIf require("rstanarm") && require("emmeans") && require("brms") && require("BayesFactor")
 #' library(bayestestR)
 #'
 #' p_map(rnorm(1000, 0, 1))
 #' p_map(rnorm(1000, 10, 1))
 #' \donttest{
-#' library(rstanarm)
 #' model <- suppressWarnings(
-#'   stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
+#'   rstanarm::stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
 #' )
 #' p_map(model)
 #'
-#' library(emmeans)
 #' p_map(suppressWarnings(
-#'   emtrends(model, ~1, "wt", data = mtcars)
+#'   emmeans::emtrends(model, ~1, "wt", data = mtcars)
 #' ))
 #'
-#' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
 #' p_map(model)
 #'
-#' library(BayesFactor)
-#' bf <- ttestBF(x = rnorm(100, 1, 1))
+#' bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
 #' p_map(bf)
 #'
 #' # ---------------------------------------
@@ -49,9 +50,9 @@
 #' for (iteration in 1:250) {
 #'   x <- rnorm(1000, 1, 1)
 #'   result <- data.frame(
-#'     "Kernel" = as.numeric(p_map(x, method = "kernel")),
-#'     "KernSmooth" = as.numeric(p_map(x, method = "KernSmooth")),
-#'     "logspline" = as.numeric(p_map(x, method = "logspline"))
+#'     Kernel = as.numeric(p_map(x, method = "kernel")),
+#'     KernSmooth = as.numeric(p_map(x, method = "KernSmooth")),
+#'     logspline = as.numeric(p_map(x, method = "logspline"))
 #'   )
 #'   data <- rbind(data, result)
 #' }
@@ -64,10 +65,9 @@
 #' }
 #' @seealso [Jeff Mill's talk](https://www.youtube.com/watch?v=Ip8Ci5KUVRc)
 #'
-#' @references \itemize{
-#'   \item Makowski D, Ben-Shachar MS, Chen SHA, Lüdecke D (2019) Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology 2019;10:2767. \doi{10.3389/fpsyg.2019.02767}
-#'   \item Mills, J. A. (2018). Objective Bayesian Precise Hypothesis Testing. University of Cincinnati.
-#' }
+#' @references
+#' - Makowski D, Ben-Shachar MS, Chen SHA, Lüdecke D (2019) Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology 2019;10:2767. \doi{10.3389/fpsyg.2019.02767}
+#' - Mills, J. A. (2018). Objective Bayesian Precise Hypothesis Testing. University of Cincinnati.
 #'
 #' @export
 p_map <- function(x, null = 0, precision = 2^10, method = "kernel", ...) {

diff --git a/R/p_significance.R b/R/p_significance.R
@@ -19,7 +19,7 @@
 #'
 #' @note 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}.
 #'
-#' @examples
+#' @examplesIf require("rstanarm")
 #' library(bayestestR)
 #'
 #' # Simulate a posterior distribution of mean 1 and SD 1
@@ -34,13 +34,11 @@
 #' \donttest{
 #' # rstanarm models
 #' # -----------------------------------------------
-#' if (require("rstanarm")) {
-#'   model <- rstanarm::stan_glm(mpg ~ wt + cyl,
-#'     data = mtcars,
-#'     chains = 2, refresh = 0
-#'   )
-#'   p_significance(model)
-#' }
+#' model <- rstanarm::stan_glm(mpg ~ wt + cyl,
+#'   data = mtcars,
+#'   chains = 2, refresh = 0
+#' )
+#' p_significance(model)
 #' }
 #' @export
 p_significance <- function(x, ...) {

diff --git a/R/p_to_bf.R b/R/p_to_bf.R
@@ -17,33 +17,31 @@
 #' and sample size: The 3p(sqrt(n)) rule. Preprint available on ArXiv:
 #' https://psyarxiv.com/egydq
 #'
-#' @examples
-#' if (requireNamespace("parameters", quietly = TRUE)) {
-#'   data(iris)
-#'   model <- lm(Petal.Length ~ Sepal.Length + Species, data = iris)
-#'   p_to_bf(model)
+#' @examplesIf require("parameters")
+#' data(iris)
+#' model <- lm(Petal.Length ~ Sepal.Length + Species, data = iris)
+#' p_to_bf(model)
 #'
-#'   # Examples that demonstrate comparison between
-#'   # BIC-approximated and pseudo BF
-#'   # --------------------------------------------
-#'   m0 <- lm(mpg ~ 1, mtcars)
-#'   m1 <- lm(mpg ~ am, mtcars)
-#'   m2 <- lm(mpg ~ factor(cyl), mtcars)
+#' # Examples that demonstrate comparison between
+#' # BIC-approximated and pseudo BF
+#' # --------------------------------------------
+#' m0 <- lm(mpg ~ 1, mtcars)
+#' m1 <- lm(mpg ~ am, mtcars)
+#' m2 <- lm(mpg ~ factor(cyl), mtcars)
 #'
-#'   # In this first example, BIC-approximated BF and
-#'   # pseudo-BF based on p-values are close...
+#' # In this first example, BIC-approximated BF and
+#' # pseudo-BF based on p-values are close...
 #'
-#'   # BIC-approximated BF, m1 against null model
-#'   bic_to_bf(BIC(m1), denominator = BIC(m0))
+#' # BIC-approximated BF, m1 against null model
+#' bic_to_bf(BIC(m1), denominator = BIC(m0))
 #'
-#'   # pseudo-BF based on p-values - dropping intercept
-#'   p_to_bf(m1)[-1, ]
+#' # pseudo-BF based on p-values - dropping intercept
+#' p_to_bf(m1)[-1, ]
 #'
-#'   # The second example shows that results from pseudo-BF are less accurate
-#'   # and should be handled wit caution!
-#'   bic_to_bf(BIC(m2), denominator = BIC(m0))
-#'   p_to_bf(anova(m2), n_obs = nrow(mtcars))
-#' }
+#' # The second example shows that results from pseudo-BF are less accurate
+#' # and should be handled wit caution!
+#' bic_to_bf(BIC(m2), denominator = BIC(m0))
+#' p_to_bf(anova(m2), n_obs = nrow(mtcars))
 #'
 #' @return A data frame with the p-values and pseudo-Bayes factors (against the null).
 #'

diff --git a/R/point_estimate.R b/R/point_estimate.R
@@ -21,7 +21,7 @@
 #'
 #' @note 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}.
 #'
-#' @examples
+#' @examplesIf require("rstanarm") && require("emmeans") && require("brms") && require("BayesFactor")
 #' library(bayestestR)
 #'
 #' point_estimate(rnorm(1000))
@@ -34,31 +34,27 @@
 #' \donttest{
 #' # rstanarm models
 #' # -----------------------------------------------
-#' library(rstanarm)
 #' model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
 #' point_estimate(model, centrality = "all", dispersion = TRUE)
 #' point_estimate(model, centrality = c("median", "MAP"))
 #'
 #'
 #' # emmeans estimates
 #' # -----------------------------------------------
-#' library(emmeans)
 #' point_estimate(
-#'   emtrends(model, ~1, "wt", data = mtcars),
+#'   emmeans::emtrends(model, ~1, "wt", data = mtcars),
 #'   centrality = c("median", "MAP")
 #' )
 #'
 #' # brms models
 #' # -----------------------------------------------
-#' library(brms)
 #' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
 #' point_estimate(model, centrality = "all", dispersion = TRUE)
 #' point_estimate(model, centrality = c("median", "MAP"))
 #'
 #' # BayesFactor objects
 #' # -----------------------------------------------
-#' library(BayesFactor)
-#' bf <- ttestBF(x = rnorm(100, 1, 1))
+#' bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
 #' point_estimate(bf, centrality = "all", dispersion = TRUE)
 #' point_estimate(bf, centrality = c("median", "MAP"))
 #' }

diff --git a/R/rope.R b/R/rope.R
@@ -95,7 +95,7 @@
 #'   methods for model selection. Statistics and Computing, 27(3), 711–735.
 #'   \doi{10.1007/s11222-016-9649-y}
 #'
-#' @examples
+#' @examplesIf require("rstanarm") && require("emmeans") && require("brms") && require("BayesFactor")
 #' library(bayestestR)
 #'
 #' rope(x = rnorm(1000, 0, 0.01), range = c(-0.1, 0.1))

diff --git a/R/si.R b/R/si.R
@@ -42,7 +42,7 @@
 #' Note that if the level of requested support is higher than observed in the data, the
 #' interval will be `[NA,NA]`.
 #'
-#' @examplesIf requireNamespace("logspline", quietly = TRUE)
+#' @examplesIf require("logspline") && require("rstanarm") && require("brms") && require("emmeans")
 #' library(bayestestR)
 #'
 #' prior <- distribution_normal(1000, mean = 0, sd = 1)

diff --git a/R/spi.R b/R/spi.R
@@ -23,7 +23,7 @@
 #' @references
 #' Liu, Y., Gelman, A., & Zheng, T. (2015). Simulation-efficient shortest probability intervals. Statistics and Computing, 25(4), 809–819. https://doi.org/10.1007/s11222-015-9563-8
 #'
-#' @examplesIf requireNamespace("quadprog", quietly = TRUE)
+#' @examplesIf require("quadprog") && require("rstanarm")
 #' library(bayestestR)
 #'
 #' posterior <- rnorm(1000)