Fixed another parameter conflict for S3 method. Updated manuals

R/metapred_measures.R

@@ -338,7 +338,7 @@ squared_diff <- function(x, ...) {
 
 # Mean of largest half of values
 mean_of_large <- function(x, ...) {
- z <- unlist(s[["estimate"]])
+ z <- unlist(x[["estimate"]])
  mean(z[z >= median(z)])
 }
 
@@ -481,7 +481,7 @@ ma.perf <- function(object, method = "REML", test = "knha", ...) {
 #' 
 #' @author Valentijn de Jong <[email protected]>
 #' 
-#' @param object A \code{metapred} fit object.
+#' @param x A \code{metapred} fit object
 #' @param step Which step should be plotted? Defaults to the best step. numeric is converted to name of the step: 0 for 
 #' an unchanged model, 1 for the first change...
 #' @param model Which model change should be plotted? NULL (default, best change) or character name of variable or (integer) 

tests/testthat/test_valmeta_3_cstat_bayes.R

@@ -1,8 +1,9 @@
 context("valmeta 3. bayesian meta-analysis of c-statistic")
 skip_on_cran()
-
+skip_if_not_installed("rjags")
 
 library(metafor)
+library(rjags)
 library(dplyr)
 data(EuroSCORE)
 
@@ -16,7 +17,6 @@ test_that("Bayesian random effect meta-analysis of concordance statistic", {
 
 
 test_that("Bayesian random effect meta-analysis of concordance statistic using normality/identity model", {
-
  pars <- list(model.cstat = "normal/identity")
 
  fit <- valmeta(cstat = c.index, cstat.se = se.c.index, cstat.cilb = c.index.95CIl,

tests/testthat/test_valmeta_5_oe_bayes.R

@@ -1,5 +1,6 @@
 context("valmeta 5. bayesian meta-analysis of total O:E ratio")
 skip_on_cran()
+skip_if_not_installed("rjags")
 
 library(lme4)
 library(rjags)
@@ -8,7 +9,6 @@ library(dplyr)
 data(EuroSCORE)
 
 test_that("Bayesian random effect meta-analysis of total O:E ratio", {
-
  pars <- list(hp.tau.dist = "dhalft", # Prior for the between-study standard deviation
  hp.tau.sigma = 1.5, # Standard deviation for 'hp.tau.dist'
  hp.tau.df = 3, # Degrees of freedom for 'hp.tau.dist'
@@ -39,7 +39,6 @@ test_that("Bayesian random effect meta-analysis of total O:E ratio", {
 })
 
 test_that("Adjusting JAGS sampling parameters works", {
-
  fit1 <- valmeta(measure = "OE", 
  O = EuroSCORE$n.events, 
  E = EuroSCORE$e.events,