#102 #97 replace md equation syntax with roxygen

R/back_transformations.R

@@ -5,8 +5,8 @@
 #' 
 #' @details We assume analysts' estimates are normally distributed. Each function uses a normal distribution to simulate the a distribution of effect-sizes and their standard errors. Next this distribution is back-transformed to the desired response scale. The mean `m_est`, standard error `se_est`, and quantiles (`lower` and `upper`) of the back-transformed distribution are returned within a dataframe.
 #' @param beta Analyst beta estimate
-#' @param se Standard error of analyst's effect size estimate $\\beta$ 
-#' or out-of-sample prediction estimate $y\\_i$.
+#' @param se Standard error of analyst's effect size estimate \eqn{\beta} 
+#' or out-of-sample prediction estimate \eqn{y_i}.
 #' @param sim numeric vector of length 1. number of simulations.
 #' @return data frame containing the mean estimate, its standard error, and quantiles.
 #' @family back transformation
@@ -71,7 +71,7 @@ probit_back <- function(beta, se, sim) {
   return(set)
 }
 
-#' @describeIn back Back transform beta estimates for models with $1/x$ link
+#' @describeIn back Back transform beta estimates for models with \eqn{1/x} link
 #' @export
 inverse_back <- function(beta, se, sim) {
   simulated <- rnorm(sim, beta, se)
@@ -90,7 +90,7 @@ inverse_back <- function(beta, se, sim) {
   return(set)
 }
 
-#' @describeIn back Back transform beta estimates for models with $x^2$-link
+#' @describeIn back Back transform beta estimates for models with \eqn{x^2}-link
 #' @export
 square_back <- function(beta, se, sim) {
   simulated <- rnorm(sim, beta, se)
@@ -109,7 +109,7 @@ square_back <- function(beta, se, sim) {
   return(set)
 }
 
-#' @describeIn back Back transform beta estimates for models with $x^3$-link
+#' @describeIn back Back transform beta estimates for models with \eqn{x^3}-link
 #' @export
 cube_back <- function(beta, se, sim) {
   simulated <- rnorm(sim, beta, se)

R/get_MA_fit_stats.R

@@ -1,6 +1,6 @@
 #' Extract meta-analytic statistics like \eqn{I^2}, etc.
 #' @param MA_mod a fitted model of class rma.mv
-#' @return A tidy tibble with \eqn{\sigma^2} and \eqn{I^2} estimates for \code{MA_mod}
+#' @return A tidy tibble with \eqn{\sigma^2} and \eqn{I^2} estimates for `MA_mod`
 #' @export
 get_MA_fit_stats <- function(MA_mod) {
   stopifnot("MA_mod must be an object of class rma.mv" = "rma.mv" %in% class(MA_mod))

R/utils.R

@@ -90,8 +90,8 @@ capwords <- function(s, strict = FALSE) {
 #' Removes infinite and NA values from a dataframe of standardised effects
 #'
 #' @param effects_analysis A dataframe containing the standardised effects
-#' @param Z_colname unquoted or bare column name with the $Z$ or $Z_r$ estimates
-#' @param VZ_colname unquoted or bare column name containing the $VZ$ or $\\text{VZ}_r$ estimates
+#' @param Z_colname unquoted or bare column name with the \eqn{Z} or \eqn{Z_r} estimates
+#' @param VZ_colname unquoted or bare column name containing the \eqn{VZ} or \eqn{\text{VZ}_r} estimates
 #'
 #' @return a dataframe without
 #' @export