tests/testthat/_snaps/meta_random_bayes.md

meta_analysis works - bayesian

Code
  dplyr::select(df, -expression)
Output
  # A tibble: 2 x 19
    term    effectsize                       estimate std.error conf.level
    <chr>   <chr>                               <dbl>     <dbl>      <dbl>
  1 Overall meta-analytic posterior estimate   -0.650     0.222       0.95
  2 tau     meta-analytic posterior estimate    0.486     0.184       0.95
    conf.low conf.high weight  bf10  rhat   ess component prior.distribution
       <dbl>     <dbl>  <dbl> <dbl> <dbl> <dbl> <chr>     <chr>             
  1   -1.12     -0.251     NA  53.0    NA    NA meta      Student's t       
  2    0.205     0.917     NA  53.0    NA    NA meta      Inverse gamma     
    prior.location prior.scale method                                 conf.method
             <dbl>       <dbl> <chr>                                  <chr>      
  1              0       0.707 Bayesian meta-analysis using 'metaBMA' ETI        
  2              1       0.15  Bayesian meta-analysis using 'metaBMA' ETI        
    log_e_bf10 n.obs
         <dbl> <int>
  1       3.97    16
  2       3.97    16
Code
  df[["expression"]]
Output
  [[1]]
  list(log[e] * (BF["01"]) == "-3.970", widehat(delta)["difference"]^"posterior" == 
      "-0.650", CI["95%"]^ETI ~ "[" * "-1.121", "-0.251" * "]", 
      italic("r")["Cauchy"]^"JZS" == "0.707")

  [[2]]
  list(log[e] * (BF["01"]) == "-3.970", widehat(delta)["difference"]^"posterior" == 
      "0.486", CI["95%"]^ETI ~ "[" * "0.205", "0.917" * "]", italic("r")["Cauchy"]^"JZS" == 
      "0.150")


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statsExpressions documentation built on Sept. 12, 2023, 5:07 p.m.