From a18e975847bae57bc7df833049255c29d51b8ff0 Mon Sep 17 00:00:00 2001 From: Dominique Makowski Date: Thu, 8 Feb 2024 21:56:08 +0000 Subject: [PATCH] minor docs --- R/p_direction.R | 4 ++-- man/p_direction.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/p_direction.R b/R/p_direction.R index b15122118..477f97c26 100644 --- a/R/p_direction.R +++ b/R/p_direction.R @@ -50,7 +50,7 @@ #' with an equal posterior mass of positive and negative values. Values close to #' 0.5 _cannot_ be used to support the null hypothesis (that the parameter does #' _not_ have a direction) is a similar why to how large p-values cannot be used -#' to support the null hypothesis (see [`pd_tp_p()`]; Makowski et al., 2019). +#' to support the null hypothesis (see [`pd_to_p()`]; Makowski et al., 2019). #' \cr\cr #' **For a discrete parameter space or a parameter space that is a mixture #' between discrete and continuous spaces**, exact values of 0 (or any point @@ -75,7 +75,7 @@ #' the larger of the two. This "simple" method is the most straightforward, but #' its precision is directly tied to the number of posterior draws. #' \cr\cr -#' The second approach relies on [`density estimation()`]: It starts by +#' The second approach relies on [density estimation][estimate_density]: It starts by #' estimating the continuous-smooth density function (for which many methods are #' available), and then computing the [area under the curve][area_under_curve] #' (AUC) of the density curve on either side of `null` and taking the maximum diff --git a/man/p_direction.Rd b/man/p_direction.Rd index 46317167a..e87ff6ee9 100644 --- a/man/p_direction.Rd +++ b/man/p_direction.Rd @@ -143,7 +143,7 @@ positive, some negative. Therefore, the smallest the \emph{pd} can be is 0.5 - with an equal posterior mass of positive and negative values. Values close to 0.5 \emph{cannot} be used to support the null hypothesis (that the parameter does \emph{not} have a direction) is a similar why to how large p-values cannot be used -to support the null hypothesis (see \code{\link[=pd_tp_p]{pd_tp_p()}}; Makowski et al., 2019). +to support the null hypothesis (see \code{\link[=pd_to_p]{pd_to_p()}}; Makowski et al., 2019). \cr\cr \strong{For a discrete parameter space or a parameter space that is a mixture between discrete and continuous spaces}, exact values of 0 (or any point @@ -172,7 +172,7 @@ proportion of negative (or smaller than \code{null}) posterior samples, and take the larger of the two. This "simple" method is the most straightforward, but its precision is directly tied to the number of posterior draws. \cr\cr -The second approach relies on \code{\link[=density estimation]{density estimation()}}: It starts by +The second approach relies on \link[=estimate_density]{density estimation}: It starts by estimating the continuous-smooth density function (for which many methods are available), and then computing the \link[=area_under_curve]{area under the curve} (AUC) of the density curve on either side of \code{null} and taking the maximum