.heterogeneity: Computation of heterogeneity indices from meta-analytic results

Description

Computation of heterogeneity indices from meta-analytic results

Usage

.heterogeneity(
  mean_es,
  var_es,
  var_pre,
  var_res,
  var_e = NA,
  var_art = NA,
  wt_vec,
  N,
  k,
  es_vec,
  vare_vec,
  es_failsafe = NULL,
  conf_level = 0.95,
  es_type = "es",
  wt_type,
  ma_method,
  var_unbiased,
  var_res_ci_method
)

Value

A list of heterogeneity statistics.

Arguments

mean_es: The mean effect size.
var_es: The observed variances of effect sizes.
var_pre: The total predicted variance of effect sizes due to sampling error and other artifacts.
var_res: The estimated residual variance of effect sizes.
var_e: The predicted variance of effect sizes due to sampling error.
var_art: The predicted variance of effect sizes predicted from other artifacts.
wt_vec: The vector of weights used in the meta-analysis.
N: The total sample size of the meta-analysis.
k: The number of effect sizes included in the meta-analysis.
es_vec: The vector of effect sizes used in the meta-analysis.
vare_vec: The vector of sampling-error variances used in the meta-analysis.
es_failsafe: Failsafe value of the effect size for use in file-drawer analyses.
conf_level: Confidence level to define the width of the confidence interval (default = .95).
es_type: Name of effect-size type.
wt_type: Weighting method.
ma_method: What artifact correction method is used. Options are "bb", "ic", and "ad".
var_unbiased: Are variances calculated using the unbiased (TRUE) or maximum likelihood (FALSE) estimator?
var_res_ci_method: Method to use to estimate a confidence interval for var_res. See heterogeneity() for details.