The posterior_interval generic is re-exported
from rstantools and an S3 method is provided that dispatches on
hbmfit objects. This lets users call
posterior_interval(fit) on the return value of
hbm just as they would on a brmsfit.
Arguments
- object
An
hbmfitobject.- prob
Coverage probability in \((0, 1)\) (default
0.95; note thatrstantools::posterior_interval's own default is0.9).- params
Optional character vector of parameter names to keep.
- ...
Additional arguments forwarded to
posterior_draws.
Examples
# \donttest{
library(hbsaems)
library(brms)
data("data_fhnorm")
model <- hbm(brms::bf(y ~ x1), data = data_fhnorm,
re = ~ (1 | regency), # area-level random effect
chains = 4, iter = 2000, warmup = 1000,
cores = 1, seed = 1, refresh = 0)
#> Compiling Stan program...
#> Start sampling
#> Warning: There were 32 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: There were 3 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.44, 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
posterior_interval(model, prob = 0.90)
#> variable
#> b_Intercept b_x1 sd_regency__Intercept sigma Intercept
#> 5% 9.733409 0.770820 0.1041003 0.1329507 9.571327
#> 95% 10.286432 1.263571 1.5804342 1.5662764 10.121574
#> variable
#> r_regency[regency_001,Intercept] r_regency[regency_002,Intercept]
#> 5% -2.401233 -1.0889555
#> 95% 0.279015 0.8852009
#> variable
#> r_regency[regency_003,Intercept] r_regency[regency_004,Intercept]
#> 5% -3.5925534 -0.7504843
#> 95% 0.1463933 1.0817428
#> variable
#> r_regency[regency_005,Intercept] r_regency[regency_006,Intercept]
#> 5% -0.7566513 -0.4432796
#> 95% 1.0819304 1.7852899
#> variable
#> r_regency[regency_007,Intercept] r_regency[regency_008,Intercept]
#> 5% -2.2848932 -0.4917826
#> 95% 0.2597766 1.8394768
#> variable
#> r_regency[regency_009,Intercept] r_regency[regency_010,Intercept]
#> 5% -2.0305868 -1.0997710
#> 95% 0.4200014 0.7849404
#> variable
#> r_regency[regency_011,Intercept] r_regency[regency_012,Intercept]
#> 5% -1.420643 -0.2517772
#> 95% 0.578699 2.5852451
#> variable
#> r_regency[regency_013,Intercept] r_regency[regency_014,Intercept]
#> 5% -1.8021251 -1.0713848
#> 95% 0.4462022 0.7867288
#> variable
#> r_regency[regency_015,Intercept] r_regency[regency_016,Intercept]
#> 5% -0.3413133 -2.4714429
#> 95% 2.2837507 0.2382759
#> variable
#> r_regency[regency_017,Intercept] r_regency[regency_018,Intercept]
#> 5% -4.49857898 -1.6605686
#> 95% 0.05659349 0.4424408
#> variable
#> r_regency[regency_019,Intercept] r_regency[regency_020,Intercept]
#> 5% -3.7690378 -0.4905167
#> 95% 0.1077582 1.6026975
#> variable
#> r_regency[regency_021,Intercept] r_regency[regency_022,Intercept]
#> 5% -0.4128254 -0.8593918
#> 95% 1.7348068 1.0087542
#> variable
#> r_regency[regency_023,Intercept] r_regency[regency_024,Intercept]
#> 5% -0.4504253 -0.6261126
#> 95% 1.8138817 1.4070394
#> variable
#> r_regency[regency_025,Intercept] r_regency[regency_026,Intercept]
#> 5% -0.3334575 -0.9137556
#> 95% 2.1020700 0.9860068
#> variable
#> r_regency[regency_027,Intercept] r_regency[regency_028,Intercept]
#> 5% -0.9410380 -0.3155173
#> 95% 0.9421062 2.2405751
#> variable
#> r_regency[regency_029,Intercept] r_regency[regency_030,Intercept]
#> 5% -1.6907546 -0.8729571
#> 95% 0.4383858 1.0256455
#> variable
#> r_regency[regency_031,Intercept] r_regency[regency_032,Intercept]
#> 5% -1.0920458 -0.691739
#> 95% 0.7475649 1.268374
#> variable
#> r_regency[regency_033,Intercept] r_regency[regency_034,Intercept]
#> 5% -0.2040619 -0.5476931
#> 95% 3.0067076 1.5782589
#> variable
#> r_regency[regency_035,Intercept] r_regency[regency_036,Intercept]
#> 5% -1.6207446 -1.1982334
#> 95% 0.4432017 0.7524107
#> variable
#> r_regency[regency_037,Intercept] r_regency[regency_038,Intercept]
#> 5% -1.5144146 -0.459241
#> 95% 0.5146388 1.972734
#> variable
#> r_regency[regency_039,Intercept] r_regency[regency_040,Intercept]
#> 5% -0.5362565 -0.2885942
#> 95% 1.4244176 2.4560123
#> variable
#> r_regency[regency_041,Intercept] r_regency[regency_042,Intercept]
#> 5% -0.4594529 -0.4547891
#> 95% 1.7652192 1.8601064
#> variable
#> r_regency[regency_043,Intercept] r_regency[regency_044,Intercept]
#> 5% -0.4950369 -0.5274896
#> 95% 1.6308136 1.6114450
#> variable
#> r_regency[regency_045,Intercept] r_regency[regency_046,Intercept]
#> 5% -1.789179 -0.1732887
#> 95% 0.405637 3.2352001
#> variable
#> r_regency[regency_047,Intercept] r_regency[regency_048,Intercept]
#> 5% -0.2695222 -1.303255
#> 95% 2.5144580 0.668779
#> variable
#> r_regency[regency_049,Intercept] r_regency[regency_050,Intercept]
#> 5% -1.0916343 -1.276056
#> 95% 0.6717805 0.774288
#> variable
#> r_regency[regency_051,Intercept] r_regency[regency_052,Intercept]
#> 5% -0.6522476 -2.169812
#> 95% 1.3349703 0.321461
#> variable
#> r_regency[regency_053,Intercept] r_regency[regency_054,Intercept]
#> 5% -1.5885510 -0.5712904
#> 95% 0.4865044 1.7486542
#> variable
#> r_regency[regency_055,Intercept] r_regency[regency_056,Intercept]
#> 5% -1.0853989 -1.2014513
#> 95% 0.7571634 0.6802977
#> variable
#> r_regency[regency_057,Intercept] r_regency[regency_058,Intercept]
#> 5% -1.2717548 -0.9135611
#> 95% 0.6804797 1.0309680
#> variable
#> r_regency[regency_059,Intercept] r_regency[regency_060,Intercept]
#> 5% -0.8624002 -1.9258997
#> 95% 1.0746938 0.3873318
#> variable
#> r_regency[regency_061,Intercept] r_regency[regency_062,Intercept]
#> 5% -0.4503556 -0.2055982
#> 95% 1.8032843 2.8732038
#> variable
#> r_regency[regency_063,Intercept] r_regency[regency_064,Intercept]
#> 5% -0.7732058 -1.1921068
#> 95% 1.0673116 0.7515556
#> variable
#> r_regency[regency_065,Intercept] r_regency[regency_066,Intercept]
#> 5% -1.5842160 -2.0444678
#> 95% 0.5527895 0.3941948
#> variable
#> r_regency[regency_067,Intercept] r_regency[regency_068,Intercept]
#> 5% -0.8129541 -2.4308545
#> 95% 1.0299721 0.2700321
#> variable
#> r_regency[regency_069,Intercept] r_regency[regency_070,Intercept]
#> 5% -0.9774569 -2.4719052
#> 95% 0.9119975 0.2377517
#> variable
#> r_regency[regency_071,Intercept] r_regency[regency_072,Intercept]
#> 5% -0.4293281 -1.6217188
#> 95% 1.8702439 0.4765958
#> variable
#> r_regency[regency_073,Intercept] r_regency[regency_074,Intercept]
#> 5% -2.9358476 -1.148260
#> 95% 0.2117917 0.730387
#> variable
#> r_regency[regency_075,Intercept] r_regency[regency_076,Intercept]
#> 5% -0.9426874 -0.4073616
#> 95% 0.9171798 1.9678612
#> variable
#> r_regency[regency_077,Intercept] r_regency[regency_078,Intercept]
#> 5% -1.0153972 -1.5165009
#> 95% 0.8559687 0.5466559
#> variable
#> r_regency[regency_079,Intercept] r_regency[regency_080,Intercept]
#> 5% -0.2689795 -0.516788
#> 95% 2.5613471 1.525764
#> variable
#> r_regency[regency_081,Intercept] r_regency[regency_082,Intercept]
#> 5% -1.1439176 -1.5250915
#> 95% 0.7659964 0.4940346
#> variable
#> r_regency[regency_083,Intercept] r_regency[regency_084,Intercept]
#> 5% -1.8959669 -0.9657439
#> 95% 0.4290276 0.7881775
#> variable
#> r_regency[regency_085,Intercept] r_regency[regency_086,Intercept]
#> 5% -0.6854984 -0.2537162
#> 95% 1.2388900 2.6716193
#> variable
#> r_regency[regency_087,Intercept] r_regency[regency_088,Intercept]
#> 5% -0.6220721 -0.6164877
#> 95% 1.4612763 1.5246780
#> variable
#> r_regency[regency_089,Intercept] r_regency[regency_090,Intercept]
#> 5% -0.4251226 -0.7766951
#> 95% 1.7676577 1.1156169
#> variable
#> r_regency[regency_091,Intercept] r_regency[regency_092,Intercept]
#> 5% -0.300865 -0.9397277
#> 95% 2.284440 0.9777499
#> variable
#> r_regency[regency_093,Intercept] r_regency[regency_094,Intercept]
#> 5% -1.339939 -0.3434568
#> 95% 0.569926 2.0784489
#> variable
#> r_regency[regency_095,Intercept] r_regency[regency_096,Intercept]
#> 5% -1.4786456 -1.9552372
#> 95% 0.5769936 0.3893045
#> variable
#> r_regency[regency_097,Intercept] r_regency[regency_098,Intercept]
#> 5% -0.484998 -0.7376373
#> 95% 1.697094 1.2115195
#> variable
#> r_regency[regency_099,Intercept] r_regency[regency_100,Intercept]
#> 5% -0.5972142 -1.3079461
#> 95% 1.4226235 0.5991122
#> variable
#> lprior lp__ z_1[1,1] z_1[1,2] z_1[1,3] z_1[1,4] z_1[1,5]
#> 5% -4.669981 -333.91671 -2.0832028 -1.349668 -2.731560 -1.154217 -1.080609
#> 95% -4.556847 -84.08301 0.8060169 1.191556 0.608765 1.333160 1.313812
#> variable
#> z_1[1,6] z_1[1,7] z_1[1,8] z_1[1,9] z_1[1,10] z_1[1,11]
#> 5% -0.9042602 -1.9842232 -0.9703484 -1.8396170 -1.355925 -1.590354
#> 95% 1.6822585 0.7134774 1.6346995 0.9062527 1.154916 1.034630
#> variable
#> z_1[1,12] z_1[1,13] z_1[1,14] z_1[1,15] z_1[1,16] z_1[1,17]
#> 5% -0.7069984 -1.7857016 -1.328830 -0.8735161 -2.080332 -3.2693482
#> 95% 2.0188204 0.9366266 1.139376 1.9580140 0.691904 0.3616728
#> variable
#> z_1[1,18] z_1[1,19] z_1[1,20] z_1[1,21] z_1[1,22] z_1[1,23]
#> 5% -1.6108155 -2.8028662 -0.9549518 -0.8998044 -1.157533 -0.9352148
#> 95% 0.9876241 0.4963746 1.5701078 1.6565216 1.272422 1.6422729
#> variable
#> z_1[1,24] z_1[1,25] z_1[1,26] z_1[1,27] z_1[1,28] z_1[1,29] z_1[1,30]
#> 5% -1.019936 -0.7997091 -1.191710 -1.222995 -0.7785767 -1.6994429 -1.170577
#> 95% 1.487228 1.8366573 1.265265 1.262104 1.9056853 0.8971189 1.295240
#> variable
#> z_1[1,31] z_1[1,32] z_1[1,33] z_1[1,34] z_1[1,35] z_1[1,36] z_1[1,37]
#> 5% -1.308361 -1.095089 -0.6571443 -0.9968887 -1.6469875 -1.438281 -1.587629
#> 95% 1.149838 1.449103 2.3221914 1.5881529 0.9604803 1.139025 1.029658
#> variable
#> z_1[1,38] z_1[1,39] z_1[1,40] z_1[1,41] z_1[1,42] z_1[1,43]
#> 5% -0.9612357 -1.024165 -0.7964382 -0.9858247 -0.9156996 -0.9877723
#> 95% 1.7606654 1.477007 2.0048592 1.7396236 1.6895299 1.6151860
#> variable
#> z_1[1,44] z_1[1,45] z_1[1,46] z_1[1,47] z_1[1,48] z_1[1,49] z_1[1,50]
#> 5% -1.008661 -1.7035302 -0.7119076 -0.7600451 -1.495829 -1.347402 -1.459372
#> 95% 1.556487 0.8045543 2.4009248 2.0026031 1.073252 1.081362 1.077474
#> variable
#> z_1[1,51] z_1[1,52] z_1[1,53] z_1[1,54] z_1[1,55] z_1[1,56] z_1[1,57]
#> 5% -1.048086 -1.954747 -1.585049 -0.9609586 -1.329656 -1.412271 -1.487004
#> 95% 1.433938 0.826465 1.030117 1.7084117 1.147312 1.150035 1.133311
#> variable
#> z_1[1,58] z_1[1,59] z_1[1,60] z_1[1,61] z_1[1,62] z_1[1,63] z_1[1,64]
#> 5% -1.232581 -1.172438 -1.7713509 -0.8851064 -0.6626854 -1.169219 -1.379726
#> 95% 1.340078 1.345838 0.8481793 1.6784451 2.2001263 1.337042 1.171499
#> variable
#> z_1[1,65] z_1[1,66] z_1[1,67] z_1[1,68] z_1[1,69] z_1[1,70] z_1[1,71]
#> 5% -1.617536 -1.8788575 -1.217561 -2.0410044 -1.285158 -2.0839423 -0.9534745
#> 95% 1.004150 0.8620341 1.327728 0.6996618 1.345540 0.7083512 1.7268872
#> variable
#> z_1[1,72] z_1[1,73] z_1[1,74] z_1[1,75] z_1[1,76] z_1[1,77] z_1[1,78]
#> 5% -1.6780584 -2.314192 -1.390630 -1.177502 -0.8625169 -1.340465 -1.5990426
#> 95% 0.9775441 0.754395 1.077038 1.211283 1.7948916 1.236560 0.9676809
#> variable
#> z_1[1,79] z_1[1,80] z_1[1,81] z_1[1,82] z_1[1,83] z_1[1,84] z_1[1,85]
#> 5% -0.7400079 -0.9101074 -1.345191 -1.5346579 -1.8064597 -1.274384 -1.115002
#> 95% 2.0005156 1.5552137 1.112254 0.9194448 0.8919871 1.142524 1.463394
#> variable
#> z_1[1,86] z_1[1,87] z_1[1,88] z_1[1,89] z_1[1,90] z_1[1,91] z_1[1,92]
#> 5% -0.7631252 -1.023163 -1.090679 -0.9248782 -1.167483 -0.8059206 -1.217361
#> 95% 2.1170700 1.473692 1.612246 1.6796516 1.338843 1.8655945 1.225668
#> variable
#> z_1[1,93] z_1[1,94] z_1[1,95] z_1[1,96] z_1[1,97] z_1[1,98] z_1[1,99]
#> 5% -1.474265 -0.8954907 -1.5662996 -1.7452024 -0.9279997 -1.110853 -1.007596
#> 95% 1.013114 1.7908739 0.9592165 0.9106774 1.6722879 1.333239 1.551477
#> variable
#> z_1[1,100]
#> 5% -1.481560
#> 95% 1.066798
# }