A precomputed recovery_grid() result, shipped so that summary(),
plot_recovery() and dplyr verbs can be tried without compiling a
model. The grid crosses 20 and 50 subjects with 30 and 100 trials per
subject, with 5 replications per cell, for bmm::mixture2p() with
population values kappa = log(8) and thetat = qlogis(0.75) on the
link scale and between-subject SDs of 0.3 and 0.5. Every fit used 4
chains of 1000 iterations with cmdstanr, and the master seed was 2026.
Scores are on the natural scale.
Format
A bmmtools_recovery tibble at both the population and the
subject level, with the columns documented in recover() and a
condition column naming the grid row. The attribute cells holds
one row per cell with its seed, status, runtime and convergence
verdict; grid holds the design.
Examples
summary(recovery_mixture2p)
#> # A tibble: 16 × 20
#> condition n_subjects n_trials term level scale n bias
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
#> 1 row-1 20 30 kappa population natural 5 0.427
#> 2 row-1 20 30 thetat population natural 5 0.0349
#> 3 row-2 50 30 kappa population natural 5 0.656
#> 4 row-2 50 30 thetat population natural 5 -0.000662
#> 5 row-3 20 100 kappa population natural 5 0.125
#> 6 row-3 20 100 thetat population natural 5 -0.00646
#> 7 row-4 50 100 kappa population natural 5 0.121
#> 8 row-4 50 100 thetat population natural 5 -0.00299
#> 9 row-1 20 30 kappa subject natural 20 -0.275
#> 10 row-1 20 30 thetat subject natural 20 0.0210
#> 11 row-2 50 30 kappa subject natural 50 0.0974
#> 12 row-2 50 30 thetat subject natural 50 0.00457
#> 13 row-3 20 100 kappa subject natural 20 -0.144
#> 14 row-3 20 100 thetat subject natural 20 0.00453
#> 15 row-4 50 100 kappa subject natural 50 -0.259
#> 16 row-4 50 100 thetat subject natural 50 0.00164
#> rmse coverage coverage_50 z_mean z_sd contraction r ccc
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.45 0.8 0.2 0.217 1.73 NA NA NA
#> 2 0.0678 0.8 0 0.655 1.52 NA NA NA
#> 3 0.671 1 0.2 1.10 0.290 NA NA NA
#> 4 0.00505 1 1 -0.0321 0.284 NA NA NA
#> 5 0.512 1 0.6 0.246 0.714 NA NA NA
#> 6 0.0240 1 0.6 -0.213 1.01 NA NA NA
#> 7 0.225 1 0.8 0.283 0.509 NA NA NA
#> 8 0.00909 1 0.8 -0.157 0.603 NA NA NA
#> 9 2.38 0.87 0.37 -0.168 1.31 NA 0.581 0.0508
#> 10 0.0869 0.92 0.45 0.246 1.00 NA 0.757 0.587
#> 11 2.39 0.924 0.396 0.214 1.19 NA 0.605 0.255
#> 12 0.0712 0.92 0.48 0.00563 1.06 NA 0.660 0.453
#> 13 1.58 0.96 0.52 -0.0107 1.01 NA 0.856 0.799
#> 14 0.0500 0.97 0.52 0.0416 1.00 NA 0.889 0.854
#> 15 1.69 0.932 0.5 -0.0448 1.05 NA 0.751 0.676
#> 16 0.0502 0.948 0.476 -0.0250 1.07 NA 0.858 0.825
#> reliability_true reliability_model calibration_slope calibration_intercept
#> <dbl> <dbl> <dbl> <dbl>
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> 7 NA NA NA NA
#> 8 NA NA NA NA
#> 9 0.333 0.200 1.77 -6.84
#> 10 0.522 0.511 0.825 0.0675
#> 11 0.384 0.282 1.59 -8.26
#> 12 0.413 0.436 0.982 -0.0393
#> 13 0.710 0.628 1.11 -1.08
#> 14 0.754 0.721 1.05 -0.0501
#> 15 0.601 0.567 1.11 -0.699
#> 16 0.703 0.697 1.05 -0.0443
#> # 28 more columns; `tibble::as_tibble()` prints them all.
attr(recovery_mixture2p, "cells")
#> # A tibble: 20 × 20
#> condition replication n_subjects n_trials seed file status elapsed
#> <chr> <int> <int> <int> <dbl> <chr> <chr> <dbl>
#> 1 row-1 1 20 30 9946 data-raw/fits… ok 0.00263
#> 2 row-2 1 50 30 17865 data-raw/fits… ok 0.0158
#> 3 row-3 1 20 100 25784 data-raw/fits… ok 0.00222
#> 4 row-4 1 50 100 33703 data-raw/fits… ok 0.00248
#> 5 row-1 2 20 30 9947 data-raw/fits… ok 0.00243
#> 6 row-2 2 50 30 17866 data-raw/fits… ok 0.00284
#> 7 row-3 2 20 100 25785 data-raw/fits… ok 0.00207
#> 8 row-4 2 50 100 33704 data-raw/fits… ok 0.00237
#> 9 row-1 3 20 30 9948 data-raw/fits… ok 0.00208
#> 10 row-2 3 50 30 17867 data-raw/fits… ok 0.00226
#> 11 row-3 3 20 100 25786 data-raw/fits… ok 0.00200
#> 12 row-4 3 50 100 33705 data-raw/fits… ok 0.00218
#> 13 row-1 4 20 30 9949 data-raw/fits… ok 0.00204
#> 14 row-2 4 50 30 17868 data-raw/fits… ok 0.00212
#> 15 row-3 4 20 100 25787 data-raw/fits… ok 0.00214
#> 16 row-4 4 50 100 33706 data-raw/fits… ok 0.00206
#> 17 row-1 5 20 30 9950 data-raw/fits… ok 0.00193
#> 18 row-2 5 50 30 17869 data-raw/fits… ok 0.00213
#> 19 row-3 5 20 100 25788 data-raw/fits… ok 0.00212
#> 20 row-4 5 50 100 33707 data-raw/fits… ok 0.00213
#> # ℹ 12 more variables: converged <lgl>, max_rhat <dbl>, min_ess_bulk <dbl>,
#> # min_ess_tail <dbl>, n_divergent <int>, n_max_treedepth <int>,
#> # n_variables <int>, failed <chr>, fit_seconds <dbl>, chains <int>,
#> # iter <int>, threads <int>