A model fit to the simulated data example_data. This model includes one constant set of parameters, with no multilevel structure.
Examples
# \donttest{
# inspect summary of posterior distribution
summary(example_model())
#> Family: metad__4__normal__absolute__multinomial
#> Links: mu = log
#> Formula: N ~ 1
#> Data: data.aggregated (Number of observations: 1)
#> Draws: 4 chains, each with iter = 500; warmup = 250; thin = 1;
#> total post-warmup draws = 1000
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 0.08 0.14 -0.17 0.34 1.01 740 760
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> dprime 1.03 0.08 0.86 1.20 1.01 788 692
#> c 0.03 0.04 -0.06 0.11 1.00 892 740
#> metac2zero1diff 0.48 0.04 0.41 0.55 1.01 754 701
#> metac2zero2diff 0.51 0.04 0.44 0.60 1.01 1172 872
#> metac2zero3diff 0.63 0.06 0.53 0.75 1.00 974 511
#> metac2one1diff 0.48 0.03 0.42 0.55 1.00 1191 794
#> metac2one2diff 0.54 0.04 0.46 0.62 1.00 872 607
#> metac2one3diff 0.54 0.05 0.45 0.64 1.00 882 554
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
# obtain posterior expectations
epred_draws_metad(example_model(), tidyr::tibble(.row = 1))
#> # A tibble: 16,000 × 9
#> # Groups: .row, stimulus, joint_response, response, confidence [16]
#> .row stimulus joint_response response confidence .epred .chain .iteration
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 1 0 1 0 4 0.129 NA NA
#> 2 1 0 1 0 4 0.145 NA NA
#> 3 1 0 1 0 4 0.134 NA NA
#> 4 1 0 1 0 4 0.140 NA NA
#> 5 1 0 1 0 4 0.131 NA NA
#> 6 1 0 1 0 4 0.153 NA NA
#> 7 1 0 1 0 4 0.135 NA NA
#> 8 1 0 1 0 4 0.132 NA NA
#> 9 1 0 1 0 4 0.160 NA NA
#> 10 1 0 1 0 4 0.140 NA NA
#> # ℹ 15,990 more rows
#> # ℹ 1 more variable: .draw <int>
# }