"Call:"
"RoBMA(y = d, se = d_se, priors_bias = list(prior_weightfunction(\"two-sided\", "
"    list(c(0.1), c(1, 1))), prior_PET(\"normal\", list(0, 1))), "
"    parallel = TRUE, seed = 1)"
""
"Robust Bayesian meta-analysis                                                               "
" Model              1             Parameter prior distributions"
" Prior prob.    0.083                             mu ~ Spike(0)"
" log(marglik)   -2.90                            tau ~ Spike(0)"
" Post. prob.    0.043                                          "
" Inclusion BF   0.499                                          "
""
"Parameter estimates:"
"[1] Mean           SD             lCI            Median         uCI            error(MCMC)    error(MCMC)/SD ESS            R-hat         "
"<0 rows> (or 0-length row.names)"
""
"                                                                           "
" Model              2                         Parameter prior distributions"
" Prior prob.    0.083                               mu ~ Spike(0)          "
" log(marglik)   -2.60                              tau ~ Spike(0)          "
" Post. prob.    0.059             omega[two-sided: .1] ~ CumDirichlet(1, 1)"
" Inclusion BF   0.686                                                      "
""
"Parameter estimates:"
"              Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"omega[0,0.1] 1.000 0.000 1.000  1.000 1.000          NA             NA   NA 1.000"
"omega[0.1,1] 0.480 0.267 0.066  0.453 0.967     0.00388          0.015 4722 1.000"
""
"                                                               "
" Model              3             Parameter prior distributions"
" Prior prob.    0.083                 mu ~ Spike(0)            "
" log(marglik)   -1.46                tau ~ Spike(0)            "
" Post. prob.    0.185                PET ~ Normal(0, 1)[0, Inf]"
" Inclusion BF   2.492                                          "
""
"Parameter estimates:"
"     Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat"
"PET 0.909 0.458 0.110  0.890 1.853     0.00390          0.009 13829 1.000"
""
"                                                               "
" Model              4             Parameter prior distributions"
" Prior prob.    0.083                    mu ~ Spike(0)         "
" log(marglik)   -2.66                   tau ~ InvGamma(1, 0.15)"
" Post. prob.    0.056                                          "
" Inclusion BF   0.647                                          "
""
"Parameter estimates:"
"     Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"tau 0.296 0.278 0.046  0.222 0.975     0.00302          0.011 8468 1.007"
""
"                                                                           "
" Model              5                         Parameter prior distributions"
" Prior prob.    0.083                               mu ~ Spike(0)          "
" log(marglik)   -2.61                              tau ~ InvGamma(1, 0.15) "
" Post. prob.    0.058             omega[two-sided: .1] ~ CumDirichlet(1, 1)"
" Inclusion BF   0.683                                                      "
""
"Parameter estimates:"
"              Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"tau          0.237 0.208 0.039  0.179 0.759     0.00249          0.012 6979 1.000"
"omega[0,0.1] 1.000 0.000 1.000  1.000 1.000          NA             NA   NA 1.001"
"omega[0.1,1] 0.529 0.262 0.085  0.522 0.973     0.00373          0.014 4932 1.000"
""
"                                                               "
" Model              6             Parameter prior distributions"
" Prior prob.    0.083                 mu ~ Spike(0)            "
" log(marglik)   -1.73                tau ~ InvGamma(1, 0.15)   "
" Post. prob.    0.141                PET ~ Normal(0, 1)[0, Inf]"
" Inclusion BF   1.799                                          "
""
"Parameter estimates:"
"     Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat"
"tau 0.213 0.190 0.039  0.155 0.708     0.00197          0.010  9260 1.001"
"PET 0.861 0.475 0.072  0.835 1.854     0.00385          0.008 15203 1.000"
""
"                                                               "
" Model              7             Parameter prior distributions"
" Prior prob.    0.083                         mu ~ Normal(0, 1)"
" log(marglik)   -2.01                        tau ~ Spike(0)    "
" Post. prob.    0.107                                          "
" Inclusion BF   1.313                                          "
""
"Parameter estimates:"
"    Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat"
"mu 0.473 0.218 0.050  0.473 0.899     0.00184          0.008 14006 1.000"
""
"                                                                           "
" Model              8                         Parameter prior distributions"
" Prior prob.    0.083                               mu ~ Normal(0, 1)      "
" log(marglik)   -2.28                              tau ~ Spike(0)          "
" Post. prob.    0.081             omega[two-sided: .1] ~ CumDirichlet(1, 1)"
" Inclusion BF   0.969                                                      "
""
"Parameter estimates:"
"              Mean    SD   lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"mu           0.409 0.214 0.001  0.407 0.833     0.00236          0.011 8178 1.000"
"omega[0,0.1] 1.000 0.000 1.000  1.000 1.000          NA             NA   NA 1.000"
"omega[0.1,1] 0.593 0.251 0.120  0.611 0.979     0.00377          0.015 4444 1.000"
""
"                                                               "
" Model              9             Parameter prior distributions"
" Prior prob.    0.083                 mu ~ Normal(0, 1)        "
" log(marglik)   -2.25                tau ~ Spike(0)            "
" Post. prob.    0.083                PET ~ Normal(0, 1)[0, Inf]"
" Inclusion BF   0.999                                          "
""
"Parameter estimates:"
"     Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"mu  0.246 0.279 -0.340  0.261 0.751     0.00353          0.013 6266 1.001"
"PET 0.654 0.503  0.027  0.543 1.863     0.00645          0.013 6099 1.001"
""
"                                                               "
" Model             10             Parameter prior distributions"
" Prior prob.    0.083                    mu ~ Normal(0, 1)     "
" log(marglik)   -2.38                   tau ~ InvGamma(1, 0.15)"
" Post. prob.    0.073                                          "
" Inclusion BF   0.870                                          "
""
"Parameter estimates:"
"     Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD   ESS R-hat"
"mu  0.449 0.268 -0.090  0.452 0.954     0.00222          0.008 14526 1.001"
"tau 0.209 0.213  0.036  0.144 0.757     0.00247          0.012  7460 1.000"
""
"                                                                           "
" Model             11                         Parameter prior distributions"
" Prior prob.    0.083                               mu ~ Normal(0, 1)      "
" log(marglik)   -2.70                              tau ~ InvGamma(1, 0.15) "
" Post. prob.    0.053             omega[two-sided: .1] ~ CumDirichlet(1, 1)"
" Inclusion BF   0.621                                                      "
""
"Parameter estimates:"
"              Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"mu           0.385 0.256 -0.103  0.383 0.894     0.00293          0.011 7608 1.000"
"tau          0.193 0.184  0.036  0.139 0.662     0.00220          0.012 6994 1.000"
"omega[0,0.1] 1.000 0.000  1.000  1.000 1.000          NA             NA   NA 1.001"
"omega[0.1,1] 0.600 0.249  0.130  0.618 0.981     0.00357          0.014 4864 1.000"
""
"                                                               "
" Model             12             Parameter prior distributions"
" Prior prob.    0.083                 mu ~ Normal(0, 1)        "
" log(marglik)   -2.56                tau ~ InvGamma(1, 0.15)   "
" Post. prob.    0.061                PET ~ Normal(0, 1)[0, Inf]"
" Inclusion BF   0.716                                          "
""
"Parameter estimates:"
"     Mean    SD    lCI Median   uCI error(MCMC) error(MCMC)/SD  ESS R-hat"
"mu  0.204 0.332 -0.495  0.224 0.802     0.00392          0.012 7168 1.000"
"tau 0.223 0.230  0.038  0.156 0.832     0.00260          0.011 7844 1.001"
"PET 0.681 0.526  0.024  0.570 1.965     0.00647          0.012 6625 1.000"
