Extract consensus intervals from ICM Stan Fit Object
Source:R/extract_consensus.R
extract_consensus.RdThis function extracts parameter estimates for the consensus intervals from a
fitted Interval Consensus Model Stan fit object of class icm_stanfit.
Value
A list containing:
- df_rvar
A data frame with extracted posterior samples in the random variable datatype (see
posterior::rvar()).- summary
A summary data frame with median and credible intervals for the extracted parameters.
Details
This function extracts parameter estimates for the consensus intervals from a
fitted Interval Consensus Model Stan fit object of class icm_stanfit.
Examples
# \donttest{
# Create minimal example data
df_simplex <- data.frame(
x1 = c(0.3, 0.4, 0.2, 0.5),
x2 = c(0.3, 0.2, 0.4, 0.2),
x3 = c(0.4, 0.4, 0.4, 0.3)
)
id_person <- c(1, 1, 2, 2)
id_item <- c(1, 2, 1, 2)
# Fit ICM model
fit <- fit_icm(df_simplex, id_person, id_item, chains = 1,
iter_sampling = 100, iter_warmup = 100)
#>
#> SAMPLING FOR MODEL 'icm_ilr' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 5.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.54 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1: three stages of adaptation as currently configured.
#> Chain 1: Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1: the given number of warmup iterations:
#> Chain 1: init_buffer = 15
#> Chain 1: adapt_window = 75
#> Chain 1: term_buffer = 10
#> Chain 1:
#> Chain 1: Iteration: 1 / 200 [ 0%] (Warmup)
#> Chain 1: Iteration: 20 / 200 [ 10%] (Warmup)
#> Chain 1: Iteration: 40 / 200 [ 20%] (Warmup)
#> Chain 1: Iteration: 60 / 200 [ 30%] (Warmup)
#> Chain 1: Iteration: 80 / 200 [ 40%] (Warmup)
#> Chain 1: Iteration: 100 / 200 [ 50%] (Warmup)
#> Chain 1: Iteration: 101 / 200 [ 50%] (Sampling)
#> Chain 1: Iteration: 120 / 200 [ 60%] (Sampling)
#> Chain 1: Iteration: 140 / 200 [ 70%] (Sampling)
#> Chain 1: Iteration: 160 / 200 [ 80%] (Sampling)
#> Chain 1: Iteration: 180 / 200 [ 90%] (Sampling)
#> Chain 1: Iteration: 200 / 200 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.233 seconds (Warm-up)
#> Chain 1: 0.19 seconds (Sampling)
#> Chain 1: 0.423 seconds (Total)
#> Chain 1:
#> Warning: There were 9 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.08, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
# Extract consensus intervals
consensus <- extract_consensus(fit)
#> T_L_median T_L_CI_025 T_L_CI_975 T_U_median T_U_CI_025 T_U_CI_975
#> 1 0.22 0.02 0.42 0.57 0.23 0.77
#> 2 0.44 0.24 0.69 0.65 0.46 0.85
# }