Code style

R/bayesfactor_models.R

@@ -611,7 +611,7 @@ as.matrix.bayesfactor_models <- function(x, ...) {
 
   # Else... Get marginal likelihood
   if (verbose) {
-    message("Computation of Marginal Likelihood: estimating marginal likelihood, please wait...")
+    insight::format_alert("Computation of Marginal Likelihood: estimating marginal likelihood, please wait...")
   }
   # Should probably allow additional arguments such as reps or cores to for bridge_sampler
   bridgesampling::bridge_sampler(mod, silent = TRUE)

R/bayesfactor_parameters.R

@@ -199,7 +199,7 @@ bayesfactor_pointnull <- function(posterior,
                                   verbose = TRUE,
                                   ...) {
   if (length(null) > 1 && verbose) {
-    message("'null' is a range - computing a ROPE based Bayes factor.")
+    insight::format_alert("`null` is a range - computing a ROPE based Bayes factor.")
   }
 
   bayesfactor_parameters(

R/describe_posterior.R

@@ -499,8 +499,6 @@ describe_posterior.default <- function(posteriors, ...) {
     row.names(out) <- NULL
   }
 
-
-
   # Prepare output
   attr(out, "ci_method") <- ci_method
   out

R/map_estimate.R

@@ -1,16 +1,22 @@
 #' Maximum A Posteriori probability estimate (MAP)
 #'
-#' Find the **Highest Maximum A Posteriori probability estimate (MAP)** of a posterior, i.e., the value associated with the highest probability density (the "peak" of the posterior distribution). In other words, it is an estimation of the *mode* for continuous parameters. Note that this function relies on [estimate_density], which by default uses a different smoothing bandwidth (`"SJ"`) compared to the legacy default implemented the base R [density] function (`"nrd0"`).
+#' Find the **Highest Maximum A Posteriori probability estimate (MAP)** of a
+#' posterior, i.e., the value associated with the highest probability density
+#' (the "peak" of the posterior distribution). In other words, it is an estimation
+#' of the *mode* for continuous parameters. Note that this function relies on
+#' [`estimate_density()`], which by default uses a different smoothing bandwidth
+#' (`"SJ"`) compared to the legacy default implemented the base R [`density()`]
+#' function (`"nrd0"`).
 #'
 #' @inheritParams hdi
 #' @inheritParams estimate_density
 #'
 #' @return A numeric value if `x` is a vector. If `x` is a model-object,
 #' returns 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 `MAP_Estimate` The MAP estimate for the posterior or each model parameter.
-#'   }
+#'
+#' - `Parameter`: The model parameter(s), if `x` is a model-object. If `x` is a
+#'   vector, this column is missing.
+#' - `MAP_Estimate`: The MAP estimate for the posterior or each model parameter.
 #'
 #' @examples
 #' \dontrun{

R/mediation.R

@@ -179,7 +179,7 @@ mediation.stanmvreg <- function(model, treatment, mediator, response = NULL, cen
   # check for binary response. In this case, user should rescale variables
   modelinfo <- insight::model_info(model)
   if (any(sapply(modelinfo, function(i) i$is_binomial, simplify = TRUE))) {
-    message("One of moderator or outcome is binary, so direct and indirect effects may be on different scales. Consider rescaling model predictors, e.g. with `effectsize::standardize()`.")
+    insight::format_alert("One of moderator or outcome is binary, so direct and indirect effects may be on different scales. Consider rescaling model predictors, e.g. with `effectsize::standardize()`.")
   }
 
   # model responses
@@ -355,7 +355,7 @@ print.bayestestR_mediation <- function(x, digits = 3, ...) {
   )
 
   if (any(prop_mediated_ori$Estimate < 0)) {
-    message("\nDirect and indirect effects have opposite directions. The proportion mediated is not meaningful.")
+    insight::format_alert("\nDirect and indirect effects have opposite directions. The proportion mediated is not meaningful.")
   }
 }
 

R/p_direction.R

@@ -459,7 +459,7 @@ p_direction.get_predicted <- function(x, ...) {
   if ("iterations" %in% names(attributes(x))) {
     out <- p_direction(as.data.frame(t(attributes(x)$iterations)), ...)
   } else {
-    stop("No iterations present in the output.", call. = FALSE)
+    insight::format_error("No iterations present in the output.")
   }
   attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(x))
   out

R/plot.R

@@ -46,42 +46,49 @@ plot.bayestestR_eti <- function(x, ...) {
   NextMethod()
 }
 
+
 #' @export
 plot.bayestestR_si <- function(x, ...) {
   insight::check_if_installed("see", "to plot support intervals")
   NextMethod()
 }
 
+
 #' @export
 plot.bayesfactor_parameters <- function(x, ...) {
   insight::check_if_installed("see", "to plot Savage-Dickey Bayes factor")
   NextMethod()
 }
 
+
 #' @export
 plot.bayesfactor_models <- function(x, ...) {
   insight::check_if_installed("see", "to plot models' Bayes factors")
   NextMethod()
 }
 
+
 #' @export
 plot.estimate_density <- function(x, ...) {
   insight::check_if_installed("see", "to plot densities")
   NextMethod()
 }
 
+
 #' @export
 plot.estimate_density_df <- function(x, ...) {
   insight::check_if_installed("see", "to plot models' densities")
   NextMethod()
 }
 
+
 #' @export
 plot.p_significance <- function(x, ...) {
   insight::check_if_installed("see", "to plot practical significance")
   NextMethod()
 }
 
+
 #' @export
 plot.describe_posterior <- function(x, stack = FALSE, ...) {
   insight::check_if_installed("see", "to plot posterior samples")
@@ -91,6 +98,6 @@ plot.describe_posterior <- function(x, stack = FALSE, ...) {
     graphics::plot(estimate_density(model), stack = stack, ...) +
       ggplot2::labs(title = "Posterior Samples", x = NULL, y = NULL)
   } else {
-    warning(insight::format_message("Could not find model-object. Try ' plot(estimate_density(model))' instead."), call. = FALSE)
+    insight::format_warning("Could not find model-object. Try `plot(estimate_density(model))` instead.")
   }
 }

R/point_estimate.R

@@ -2,13 +2,22 @@
 #'
 #' Compute various point-estimates, such as the mean, the median or the MAP, to describe posterior distributions.
 #'
-#' @param centrality The point-estimates (centrality indices) to compute.  Character (vector) or list with one or more of these options: `"median"`, `"mean"`, `"MAP"` or `"all"`.
-#' @param dispersion Logical, if `TRUE`, computes indices of dispersion related to the estimate(s) (`SD` and `MAD` for `mean` and `median`, respectively).
-#' @param threshold For `centrality = "trimmed"` (i.e. trimmed mean), indicates the fraction (0 to 0.5) of observations to be trimmed from each end of the vector before the mean is computed.
+#' @param centrality The point-estimates (centrality indices) to compute. Character
+#' (vector) or list with one or more of these options: `"median"`, `"mean"`, `"MAP"`
+#' (see [`map_estimate()`]), `"trimmed"` (which is just `mean(x, trim = threshold)`),
+#' `"mode"` or `"all"`.
+#' @param dispersion Logical, if `TRUE`, computes indices of dispersion related
+#' to the estimate(s) (`SD` and `MAD` for `mean` and `median`, respectively).
+#' Dispersion is not available for `"MAP"` or `"mode"` centrality indices.
+#' @param threshold For `centrality = "trimmed"` (i.e. trimmed mean), indicates
+#' the fraction (0 to 0.5) of observations to be trimmed from each end of the
+#' vector before the mean is computed.
 #' @param ... Additional arguments to be passed to or from methods.
 #' @inheritParams hdi
 #'
-#' @references Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and 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}
+#' @references Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and 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}
 #'
 #' @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}.
 #'
@@ -59,13 +68,15 @@ point_estimate <- function(x, ...) {
 
 #' @export
 point_estimate.default <- function(x, ...) {
-  stop(insight::format_message(paste0("'point_estimate()' is not yet implemented for objects of class '", class(x)[1], "'.")), call. = FALSE)
+  insight::format_error(
+    paste0("'point_estimate()' is not yet implemented for objects of class '", class(x)[1], "'.")
+  )
 }
 
 
 #' @rdname point_estimate
 #' @export
-point_estimate.numeric <- function(x, centrality = "all", dispersion = FALSE, threshold = .1, ...) {
+point_estimate.numeric <- function(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...) {
   centrality <- match.arg(tolower(centrality), c("median", "mean", "map", "trimmed", "mode", "all"), several.ok = TRUE)
   if ("all" %in% centrality) {
     estimate_list <- c("median", "mean", "map")
@@ -119,15 +130,8 @@ point_estimate.numeric <- function(x, centrality = "all", dispersion = FALSE, th
 }
 
 
-.mode_estimate <- function(x) {
-  ux <- unique(x)
-  ux[which.max(tabulate(match(x, ux)))]
-}
-
-
-
 #' @export
-point_estimate.data.frame <- function(x, centrality = "all", dispersion = FALSE, threshold = .1, ...) {
+point_estimate.data.frame <- function(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...) {
   x <- .select_nums(x)
 
   if (ncol(x) == 1) {
@@ -148,8 +152,14 @@ point_estimate.data.frame <- function(x, centrality = "all", dispersion = FALSE,
 
 
 #' @export
-point_estimate.draws <- function(x, centrality = "all", dispersion = FALSE, threshold = .1, ...) {
-  point_estimate(.posterior_draws_to_df(x), centrality = centrality, dispersion = dispersion, threshold = threshold, ...)
+point_estimate.draws <- function(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...) {
+  point_estimate(
+    .posterior_draws_to_df(x),
+    centrality = centrality,
+    dispersion = dispersion,
+    threshold = threshold,
+    ...
+  )
 }
 
 #' @export
@@ -186,16 +196,25 @@ point_estimate.BGGM <- point_estimate.bcplm
 #' @export
 point_estimate.bamlss <- function(x, centrality = "all", dispersion = FALSE, component = c("conditional", "location", "all"), ...) {
   component <- match.arg(component)
-  out <- point_estimate(insight::get_parameters(x, component = component), centrality = centrality, dispersion = dispersion, ...)
-  out <- .add_clean_parameters_attribute(out, x)
-  out
+  out <- point_estimate(
+    insight::get_parameters(x, component = component),
+    centrality = centrality,
+    dispersion = dispersion,
+    ...
+  )
+  .add_clean_parameters_attribute(out, x)
 }
 
 
 #' @export
 point_estimate.MCMCglmm <- function(x, centrality = "all", dispersion = FALSE, ...) {
   nF <- x$Fixed$nfl
-  point_estimate(as.data.frame(x$Sol[, 1:nF, drop = FALSE]), centrality = centrality, dispersion = dispersion, ...)
+  point_estimate(
+    as.data.frame(x$Sol[, 1:nF, drop = FALSE]),
+    centrality = centrality,
+    dispersion = dispersion,
+    ...
+  )
 }
 
 
@@ -212,17 +231,6 @@ point_estimate.emmGrid <- function(x, centrality = "all", dispersion = FALSE, ..
 point_estimate.emm_list <- point_estimate.emmGrid
 
 
-# Helper ------------------------------------------------------------------
-
-
-
-#' @keywords internal
-.point_estimate_models <- function(x, effects, component, parameters, centrality = "all", dispersion = FALSE, ...) {
-  out <- point_estimate(insight::get_parameters(x, effects = effects, component = component, parameters = parameters), centrality = centrality, dispersion = dispersion, ...)
-  out
-}
-
-
 #' @rdname point_estimate
 #' @export
 point_estimate.stanreg <- function(x, centrality = "all", dispersion = FALSE, effects = c("fixed", "random", "all"), component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), parameters = NULL, ...) {
@@ -294,7 +302,6 @@ point_estimate.sim.merMod <- function(x, centrality = "all", dispersion = FALSE,
 }
 
 
-
 #' @export
 point_estimate.sim <- function(x, centrality = "all", dispersion = FALSE, parameters = NULL, ...) {
   out <- .point_estimate_models(
@@ -314,7 +321,6 @@ point_estimate.sim <- function(x, centrality = "all", dispersion = FALSE, parame
 }
 
 
-
 #' @rdname point_estimate
 #' @export
 point_estimate.BFBayesFactor <- function(x, centrality = "all", dispersion = FALSE, ...) {
@@ -341,3 +347,22 @@ point_estimate.get_predicted <- function(x, ...) {
     as.numeric(x)
   }
 }
+
+
+# Helper ------------------------------------------------------------------
+
+#' @keywords internal
+.point_estimate_models <- function(x, effects, component, parameters, centrality = "all", dispersion = FALSE, ...) {
+  point_estimate(
+    insight::get_parameters(x, effects = effects, component = component, parameters = parameters),
+    centrality = centrality,
+    dispersion = dispersion,
+    ...
+  )
+}
+
+#' @keywords internal
+.mode_estimate <- function(x) {
+  ux <- unique(x)
+  ux[which.max(tabulate(match(x, ux)))]
+}

R/reshape_iterations.R

@@ -32,9 +32,9 @@ reshape_iterations <- function(x, prefix = c("draw", "iter", "iteration", "sim")
   prefix <- prefix[min(which(sapply(tolower(prefix), function(prefix) sum(grepl(prefix, tolower(names(x)), fixed = TRUE)) > 1)))]
 
   if (is.na(prefix) || is.null(prefix)) {
-    stop(insight::format_message(
+    insight::format_error(
       "Couldn't find columns corresponding to iterations in your dataframe, please specify the correct prefix."
-    ), call. = FALSE)
+    )
   }
 
   # Get column names
@@ -58,7 +58,7 @@ reshape_iterations <- function(x, prefix = c("draw", "iter", "iteration", "sim")
   )
   row.names(long) <- NULL
 
-  class(long) <- class(long)[which(class(long) == "data.frame"):length(class(long))]
+  class(long) <- class(long)[which(inherits(long, "data.frame")):length(class(long))]
   long
 }