Perform Bayesian pairwise or network meta-regression
Source:R/run.metareg_function.R
run_metareg.RdPerforms a one-stage pairwise or network meta-regression while addressing aggregate binary or continuous missing participant outcome data via the pattern-mixture model.
Usage
run_metareg(
full,
covariate,
covar_assumption,
cov_value,
n_chains,
n_iter,
n_burnin,
n_thin,
inits = NULL
)Arguments
- full
- covariate
A numeric vector or matrix for a trial-specific covariate that is a potential effect modifier. See 'Details'.
- covar_assumption
Character string indicating the structure of the intervention-by-covariate interaction, as described in Cooper et al. (2009). Set
covar_assumptionequal to"exchangeable","independent", or"common".- cov_value
Numeric for the covariate value of interest.
- n_chains
Positive integer specifying the number of chains for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 2.- n_iter
Positive integer specifying the number of Markov chains for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 10000.- n_burnin
Positive integer specifying the number of iterations to discard at the beginning of the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 1000.- n_thin
Positive integer specifying the thinning rate for the MCMC sampling; an argument of the
jagsfunction of the R-package R2jags. The default argument is 1.- inits
A list with the initial values for the parameters; an argument of the
jagsfunction of the R-package R2jags. The default argument isNULL, and JAGS generates the initial values.
Value
A list of R2jags outputs on the summaries of the posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman et al., 1992) for the following monitored parameters for a fixed-effect pairwise meta-analysis:
- EM
The estimated summary effect measure (according to the argument
measuredefined inrun_model).- beta_all
The estimated regression coefficient for all possible pairwise comparisons according to the argument
covar_assumption.- dev_o
The deviance contribution of each trial-arm based on the observed outcome.
- hat_par
The fitted outcome at each trial-arm.
- phi
The informative missingness parameter.
For a fixed-effect network meta-analysis, the output additionally includes:
- SUCRA
The surface under the cumulative ranking (SUCRA) curve for each intervention.
- effectiveneness
The ranking probability of each intervention for every rank.
For a random-effects pairwise meta-analysis, the output additionally includes the following elements:
- EM_pred
The predicted summary effect measure (according to the argument
measuredefined inrun_model).- delta
The estimated trial-specific effect measure (according to the argument
measuredefined inrun_model). For a multi-arm trial, we estimate T-1 effects, where T is the number of interventions in the trial.- tau
The between-trial standard deviation.
In network meta-analysis, EM and EM_pred refer to all
possible pairwise comparisons of interventions in the network. Furthermore,
tau is typically assumed to be common for all observed comparisons
in the network.
For a multi-arm trial, we estimate a total T-1 of delta for
comparisons with the baseline intervention of the trial (found in the first
column of the element t), with T being the number of
interventions in the trial.
Furthermore, the output includes the following elements:
- abs_risk
The adjusted absolute risks for each intervention. This appears only when
measure = "OR",measure = "RR", ormeasure = "RD".- leverage_o
The leverage for the observed outcome at each trial-arm.
- sign_dev_o
The sign of the difference between observed and fitted outcome at each trial-arm.
- model_assessment
A data-frame on the measures of model assessment: deviance information criterion, number of effective parameters, and total residual deviance.
- jagsfit
An object of S3 class
jagswith the posterior results on all monitored parameters to be used in themcmc_diagnosticsfunction.
The run_metareg function also returns the arguments data,
measure, model, assumption, covariate,
covar_assumption, n_chains, n_iter, n_burnin,
and n_thin to be inherited by other relevant functions of the
package.
Details
run_metareg inherits the arguments data,
measure, model, assumption, heter_prior,
mean_misspar, var_misspar, D, ref,
indic, and base_risk from run_model
(now contained in the argument full). This prevents specifying a
different Bayesian model from that considered in run_model.
Therefore, the user needs first to apply run_model, and then
use run_metareg (see 'Examples').
The model runs in JAGS and the progress of the simulation appears on
the R console. The output of run_metareg is used as an S3 object by
other functions of the package to be processed further and provide an
end-user-ready output. The model is updated until convergence using the
autojags function of the R-package
R2jags with 2 updates and
number of iterations and thinning equal to n_iter and n_thin,
respectively.
The models described in Spineli et al. (2021), and Spineli (2019) have been extended to incorporate one study-level covariate variable following the assumptions of Cooper et al. (2009) for the structure of the intervention-by-covariate interaction. The covariate can be either a numeric vector or matrix with columns equal to the maximum number of arms in the dataset.
References
Cooper NJ, Sutton AJ, Morris D, Ades AE, Welton NJ. Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation. Stat Med 2009;28(14):1861–81. doi: 10.1002/sim.3594
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7(4):457–72. doi: 10.1214/ss/1177011136
Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome data in network meta-analysis: a one-stage pattern-mixture model approach. Stat Methods Med Res 2021;30(4):958–75. doi: 10.1177/0962280220983544
Spineli LM. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis. BMC Med Res Methodol 2019;19(1):86. doi: 10.1186/s12874-019-0731-y
Examples
data("nma.baker2009")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_baker.rds', package = 'rnmamod'))
# Publication year
pub_year <- c(1996, 1998, 1999, 2000, 2000, 2001, rep(2002, 5), 2003, 2003,
rep(2005, 4), 2006, 2006, 2007, 2007)
# \donttest{
# Perform a random-effects network meta-regression (exchangeable structure)
# Note: Ideally, set 'n_iter' to 10000 and 'n_burnin' to 1000
run_metareg(full = res,
covariate = pub_year,
covar_assumption = "exchangeable",
cov_value = 2007,
n_chains = 3,
n_iter = 1000,
n_burnin = 100,
n_thin = 1)
#> **Fixed baseline risk assigned**
#> JAGS generates initial values for the parameters.
#> Running the model ...
#> module glm loaded
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 157
#> Total graph size: 2824
#>
#> Initializing model
#>
#> ... Updating the model until convergence
#> $EM
#> mean sd 2.5% 25% 50% 75%
#> EM[2,1] -0.65757878 0.7395046 -2.0241156 -1.06197082 -0.6430021227 -0.31245951
#> EM[3,1] -0.46843907 0.6586888 -1.7306584 -0.83509556 -0.4719796859 -0.13561941
#> EM[4,1] 0.16645548 0.5077501 -0.7460444 -0.16024993 0.1353123703 0.45822906
#> EM[5,1] -0.40729098 0.3664670 -1.2001788 -0.62520488 -0.3962392697 -0.15816487
#> EM[6,1] 0.19235678 0.4139407 -0.5565004 -0.09201090 0.1826985963 0.44253398
#> EM[7,1] -0.24844435 0.2677228 -0.7642920 -0.43138040 -0.2585782665 -0.06930978
#> EM[8,1] -0.41590065 0.2192633 -0.9046767 -0.54787508 -0.4071724066 -0.26760980
#> EM[3,2] 0.18913971 0.7937865 -1.4172639 -0.19646422 0.1610743463 0.58131797
#> EM[4,2] 0.82403426 0.8297251 -0.7115464 0.31207533 0.8108848768 1.30952799
#> EM[5,2] 0.25028779 0.7935551 -1.5230658 -0.13079903 0.2643350368 0.71790437
#> EM[6,2] 0.84993555 0.7584778 -0.5805897 0.37529855 0.8255721591 1.29944656
#> EM[7,2] 0.40913443 0.7735805 -1.2342719 -0.01186701 0.4025832735 0.86655911
#> EM[8,2] 0.24167812 0.7457844 -1.4061010 -0.09048099 0.2566115243 0.64881933
#> EM[4,3] 0.63489455 0.7478599 -0.7588922 0.13423698 0.6144717870 1.06143045
#> EM[5,3] 0.06114809 0.7226005 -1.6087555 -0.27010974 0.1026500406 0.47656064
#> EM[6,3] 0.66079585 0.7024461 -0.6470542 0.19631537 0.6382974412 1.06165961
#> EM[7,3] 0.21999472 0.6748497 -1.1837191 -0.15457923 0.2290062047 0.62259807
#> EM[8,3] 0.05253842 0.6785250 -1.4968489 -0.27701542 0.0914778138 0.44622974
#> EM[5,4] -0.57374646 0.6424465 -2.0952163 -0.93138655 -0.4951134281 -0.14634755
#> EM[6,4] 0.02590130 0.5481084 -1.0133334 -0.31740158 0.0116749957 0.36101868
#> EM[7,4] -0.41489983 0.5176248 -1.6054330 -0.69254751 -0.3637657550 -0.11295525
#> EM[8,4] -0.58235613 0.5438184 -1.8684268 -0.87184547 -0.5262784763 -0.20716388
#> EM[6,5] 0.59964776 0.5397228 -0.2665962 0.20581932 0.5310393954 0.90872270
#> EM[7,5] 0.15884663 0.4280596 -0.5997154 -0.12893971 0.1153705580 0.40944708
#> EM[8,5] -0.00860967 0.4010973 -0.7830106 -0.28097585 0.0009095145 0.23378745
#> EM[7,6] -0.44080113 0.4203450 -1.4257215 -0.66997929 -0.4080870803 -0.17586832
#> EM[8,6] -0.60825743 0.4622685 -1.6640572 -0.86733626 -0.5834568072 -0.29431439
#> EM[8,7] -0.16745630 0.3059773 -0.8097014 -0.35930097 -0.1587161515 0.04267481
#> 97.5% Rhat n.eff
#> EM[2,1] 0.89815897 1.038106 400
#> EM[3,1] 0.98057758 1.027550 1400
#> EM[4,1] 1.30244474 1.221528 14
#> EM[5,1] 0.27611421 1.012542 1100
#> EM[6,1] 1.09216959 1.076480 33
#> EM[7,1] 0.29706315 1.067665 39
#> EM[8,1] -0.02790323 1.030215 180
#> EM[3,2] 1.84461947 1.034421 1300
#> EM[4,2] 2.51964376 1.083895 45
#> EM[5,2] 1.67985089 1.030779 360
#> EM[6,2] 2.37994933 1.031557 120
#> EM[7,2] 1.83449453 1.042241 210
#> EM[8,2] 1.50761386 1.042347 450
#> EM[4,3] 2.29403694 1.079629 35
#> EM[5,3] 1.39901281 1.033879 1300
#> EM[6,3] 2.22545691 1.028937 130
#> EM[7,3] 1.51744963 1.029307 310
#> EM[8,3] 1.30237214 1.030306 770
#> EM[5,4] 0.48359506 1.127987 23
#> EM[6,4] 1.17640965 1.043853 54
#> EM[7,4] 0.58413693 1.109237 24
#> EM[8,4] 0.28083034 1.224849 14
#> EM[6,5] 1.81829134 1.043386 70
#> EM[7,5] 1.10872731 1.035165 160
#> EM[8,5] 0.82806598 1.018084 220
#> EM[7,6] 0.31486794 1.023815 160
#> EM[8,6] 0.16213429 1.088831 30
#> EM[8,7] 0.39110902 1.091261 28
#>
#> $EM_pred
#> mean sd 2.5% 25% 50%
#> EM.pred[2,1] -0.657178729 0.7549179 -2.0543769 -1.08551068 -0.638724730
#> EM.pred[3,1] -0.466357809 0.6751549 -1.8157785 -0.84586265 -0.455521310
#> EM.pred[4,1] 0.165878044 0.5290396 -0.7861184 -0.17995140 0.139480844
#> EM.pred[5,1] -0.405531043 0.3939568 -1.2378025 -0.64271818 -0.398616942
#> EM.pred[6,1] 0.194519609 0.4384240 -0.5968222 -0.10145768 0.185853312
#> EM.pred[7,1] -0.250877621 0.3080127 -0.8722290 -0.45258975 -0.258086043
#> EM.pred[8,1] -0.411696312 0.2618573 -0.9958586 -0.55115429 -0.398713900
#> EM.pred[3,2] 0.189901223 0.8112030 -1.4623329 -0.19826241 0.168117064
#> EM.pred[4,2] 0.824081313 0.8377706 -0.7364427 0.31327445 0.800936810
#> EM.pred[5,2] 0.248972094 0.8055027 -1.5449744 -0.13888876 0.242374372
#> EM.pred[6,2] 0.852868616 0.7721753 -0.6339190 0.35414784 0.821759561
#> EM.pred[7,2] 0.410826141 0.7809234 -1.2050608 -0.02516902 0.400275620
#> EM.pred[8,2] 0.238564269 0.7614705 -1.3664006 -0.11215533 0.250959458
#> EM.pred[4,3] 0.638240470 0.7616434 -0.7660026 0.13179707 0.606796318
#> EM.pred[5,3] 0.064058002 0.7362924 -1.6648170 -0.28722303 0.104016410
#> EM.pred[6,3] 0.663560685 0.7208449 -0.6546991 0.18538752 0.629122993
#> EM.pred[7,3] 0.217121293 0.6880583 -1.1984125 -0.16541628 0.224296549
#> EM.pred[8,3] 0.049420356 0.6929085 -1.6002084 -0.29305628 0.084885280
#> EM.pred[5,4] -0.572566059 0.6592668 -2.0837326 -0.94608024 -0.497684039
#> EM.pred[6,4] 0.019343797 0.5679646 -1.0786014 -0.33668850 0.007245999
#> EM.pred[7,4] -0.416610037 0.5420876 -1.5995074 -0.71912934 -0.363832660
#> EM.pred[8,4] -0.582622276 0.5641880 -1.8975487 -0.88293724 -0.524455484
#> EM.pred[6,5] 0.600801424 0.5619203 -0.3175890 0.19770720 0.535698522
#> EM.pred[7,5] 0.162908919 0.4583155 -0.6823526 -0.14188570 0.119396067
#> EM.pred[8,5] -0.005021523 0.4243393 -0.8447939 -0.27654021 0.006239964
#> EM.pred[7,6] -0.440111922 0.4429735 -1.4300511 -0.68240077 -0.401574960
#> EM.pred[8,6] -0.603667311 0.4783740 -1.6787635 -0.88283268 -0.586982893
#> EM.pred[8,7] -0.164818812 0.3405547 -0.8599676 -0.38814558 -0.150630706
#> 75% 97.5% Rhat n.eff
#> EM.pred[2,1] -0.30350483 0.94048346 1.037273 410
#> EM.pred[3,1] -0.12787785 0.98998387 1.025884 1600
#> EM.pred[4,1] 0.46312309 1.33251628 1.189788 15
#> EM.pred[5,1] -0.14795134 0.32605055 1.011564 790
#> EM.pred[6,1] 0.46834243 1.15178824 1.067742 39
#> EM.pred[7,1] -0.04533506 0.35289990 1.047303 54
#> EM.pred[8,1] -0.24325082 0.05186116 1.022403 280
#> EM.pred[3,2] 0.59444386 1.84904416 1.032333 1000
#> EM.pred[4,2] 1.30665348 2.52407451 1.079644 45
#> EM.pred[5,2] 0.72615154 1.71467207 1.030693 360
#> EM.pred[6,2] 1.30767249 2.42732673 1.029199 130
#> EM.pred[7,2] 0.88532899 1.86199985 1.038862 180
#> EM.pred[8,2] 0.66351396 1.55210899 1.039593 440
#> EM.pred[4,3] 1.07906992 2.29660244 1.077929 35
#> EM.pred[5,3] 0.48503397 1.45521968 1.032996 1600
#> EM.pred[6,3] 1.07514929 2.28005161 1.027480 120
#> EM.pred[7,3] 0.62604608 1.52280966 1.028641 310
#> EM.pred[8,3] 0.44585343 1.36850853 1.029641 900
#> EM.pred[5,4] -0.12940637 0.55485988 1.118532 24
#> EM.pred[6,4] 0.36727346 1.19773291 1.043582 54
#> EM.pred[7,4] -0.09502941 0.63141116 1.097303 26
#> EM.pred[8,4] -0.20255875 0.37178195 1.209293 14
#> EM.pred[6,5] 0.92442067 1.88767499 1.042274 72
#> EM.pred[7,5] 0.43964100 1.15842831 1.033655 150
#> EM.pred[8,5] 0.24853888 0.88209196 1.016147 270
#> EM.pred[7,6] -0.16083893 0.33961065 1.024716 170
#> EM.pred[8,6] -0.27705022 0.18982736 1.075363 34
#> EM.pred[8,7] 0.06382399 0.45225674 1.077762 33
#>
#> $tau
#> mean sd 2.5% 25% 50% 75%
#> 0.12070228 0.08303623 0.01281009 0.04718787 0.10948935 0.17368671
#> 97.5% Rhat n.eff
#> 0.31410698 1.22908561 16.00000000
#>
#> $delta
#> mean sd 2.5% 25% 50% 75%
#> delta[1,2] -0.12558562 0.3400602 -0.7762024 -0.3421884 -0.13695946 0.06264948
#> delta[2,2] -0.09437053 0.3274403 -0.6927463 -0.3072092 -0.11015391 0.09551814
#> delta[3,2] -0.46814195 0.1952787 -0.8697350 -0.5937392 -0.46969976 -0.34212954
#> delta[4,2] -0.44157328 0.1661088 -0.7761040 -0.5398504 -0.44679066 -0.33810090
#> delta[5,2] -0.44305047 0.2067894 -0.8350323 -0.5821088 -0.44790633 -0.30761546
#> delta[6,2] -0.36972062 0.2001951 -0.7071617 -0.5164826 -0.38531877 -0.23098836
#> delta[7,2] -0.43824977 0.1488294 -0.7442425 -0.5299702 -0.43918143 -0.34027450
#> delta[8,2] -0.41889486 0.1792640 -0.7417543 -0.5441387 -0.42195860 -0.30242578
#> delta[9,2] -0.44286787 0.1805211 -0.7778997 -0.5669756 -0.44816332 -0.32043773
#> delta[10,2] -0.05620131 0.3383816 -0.6433277 -0.2967496 -0.06993655 0.13515027
#> delta[11,2] -0.22136348 0.2737415 -0.7711080 -0.4160451 -0.24391156 -0.01863170
#> delta[12,2] -0.10838293 0.3012719 -0.6390255 -0.3157944 -0.12061178 0.06724113
#> delta[13,2] -0.90965578 0.4222237 -1.8640943 -1.1824978 -0.85170364 -0.58310016
#> delta[14,2] -0.04739270 0.1923539 -0.4194225 -0.1725813 -0.04831724 0.07410025
#> delta[15,2] -0.18729251 0.2927552 -0.7681643 -0.3995928 -0.19835753 0.03527971
#> delta[16,2] -0.36187274 0.1474435 -0.6317830 -0.4675237 -0.36790304 -0.26241593
#> delta[17,2] -0.31645990 0.3317800 -1.0064325 -0.5527770 -0.29699668 -0.07359448
#> delta[18,2] -0.42384269 0.2869891 -0.9352663 -0.6166657 -0.46631317 -0.24032094
#> delta[19,2] -0.40981609 0.2902499 -0.9236976 -0.6076459 -0.45079700 -0.21521157
#> delta[20,2] -0.43219534 0.2051551 -0.8294551 -0.5735267 -0.43807961 -0.29101748
#> delta[21,2] -0.51600433 0.1894489 -0.9616227 -0.6224025 -0.49475372 -0.39379227
#> delta[9,3] -0.50035137 0.1625795 -0.8556974 -0.5957370 -0.48870154 -0.39851331
#> delta[10,3] -0.39176619 0.2813880 -0.8598081 -0.5955723 -0.43844162 -0.21147413
#> delta[12,3] -0.32427418 0.2878641 -0.7763411 -0.5317148 -0.36275644 -0.13777732
#> delta[13,3] -0.69333801 0.3588249 -1.4213504 -0.9439811 -0.67117873 -0.46107400
#> delta[19,3] -0.47768364 0.2260259 -0.9750575 -0.6175349 -0.47159881 -0.33112589
#> delta[10,4] -0.45887506 0.2047883 -0.8943369 -0.5886639 -0.45760168 -0.32065404
#> delta[12,4] -0.38555241 0.2094195 -0.7585588 -0.5316482 -0.39209514 -0.24695177
#> delta[13,4] -0.26156222 0.3090968 -0.8817016 -0.4601911 -0.29555029 -0.01886715
#> 97.5% Rhat n.eff
#> delta[1,2] 0.63279452 1.197047 15
#> delta[2,2] 0.64582052 1.232110 13
#> delta[3,2] -0.09312607 1.100933 28
#> delta[4,2] -0.10943073 1.037414 97
#> delta[5,2] -0.03168465 1.080490 38
#> delta[6,2] 0.04499090 1.123211 22
#> delta[7,2] -0.14923267 1.076015 40
#> delta[8,2] -0.06223054 1.121556 24
#> delta[9,2] -0.07814151 1.104070 29
#> delta[10,2] 0.71134765 1.293109 11
#> delta[11,2] 0.29120182 1.062155 44
#> delta[12,2] 0.56605746 1.256274 12
#> delta[13,2] -0.25106184 1.029908 83
#> delta[14,2] 0.31848517 1.026166 84
#> delta[15,2] 0.35126178 1.036701 70
#> delta[16,2] -0.06006461 1.147048 19
#> delta[17,2] 0.26918811 1.022155 94
#> delta[18,2] 0.19531759 1.319930 11
#> delta[19,2] 0.19233523 1.321167 11
#> delta[20,2] -0.03125860 1.073077 38
#> delta[21,2] -0.17379907 1.046126 65
#> delta[9,3] -0.19119814 1.035898 84
#> delta[10,3] 0.20595018 1.405205 9
#> delta[12,3] 0.30097488 1.395843 9
#> delta[13,3] 0.03608919 1.016248 930
#> delta[19,3] -0.06481464 1.055189 61
#> delta[10,4] -0.07393417 1.107465 27
#> delta[12,4] 0.03080194 1.117481 25
#> delta[13,4] 0.30286147 1.026703 130
#>
#> $beta_all
#> mean sd 2.5% 25% 50%
#> beta.all[2,1] 0.052593468 0.15010898 -0.20047772 -0.006449407 0.0392752427
#> beta.all[3,1] 0.046265296 0.13060266 -0.21350927 -0.010345222 0.0406952692
#> beta.all[4,1] 0.061647548 0.06074125 -0.04529324 0.022838509 0.0560815346
#> beta.all[5,1] -0.005977732 0.08309959 -0.20480713 -0.046974033 0.0069865349
#> beta.all[6,1] 0.091819280 0.08628304 -0.03892478 0.034559023 0.0786013782
#> beta.all[7,1] 0.042537256 0.04228495 -0.04225052 0.014558859 0.0428090708
#> beta.all[8,1] 0.011794668 0.04519457 -0.09265872 -0.013262576 0.0156096822
#> beta.all[3,2] -0.006328172 0.16488696 -0.38406156 -0.050257296 -0.0007848425
#> beta.all[4,2] 0.009054080 0.15283154 -0.31386134 -0.030604185 0.0066609522
#> beta.all[5,2] -0.058571201 0.16952675 -0.52863305 -0.100528353 -0.0214209447
#> beta.all[6,2] 0.039225812 0.14552077 -0.26758184 -0.014184778 0.0209492894
#> beta.all[7,2] -0.010056213 0.15020912 -0.36646523 -0.044087340 0.0001661499
#> beta.all[8,2] -0.040798800 0.15702321 -0.43829260 -0.079056775 -0.0125790410
#> beta.all[4,3] 0.015382252 0.13201356 -0.26563822 -0.029899671 0.0077683476
#> beta.all[5,3] -0.052243029 0.15098875 -0.44002409 -0.101656787 -0.0172604283
#> beta.all[6,3] 0.045553984 0.13634973 -0.21847521 -0.012676400 0.0243764059
#> beta.all[7,3] -0.003728041 0.13163109 -0.29696688 -0.043973580 0.0008528330
#> beta.all[8,3] -0.034470628 0.13896708 -0.37833832 -0.078945111 -0.0135401643
#> beta.all[5,4] -0.067625280 0.10791945 -0.35318922 -0.113529856 -0.0391094618
#> beta.all[6,4] 0.030171732 0.08814842 -0.13306691 -0.016030798 0.0148181279
#> beta.all[7,4] -0.019110292 0.06862062 -0.17663623 -0.053235776 -0.0096025915
#> beta.all[8,4] -0.049852880 0.07542279 -0.24242696 -0.086153224 -0.0342701038
#> beta.all[6,5] 0.097797012 0.12439311 -0.05337798 0.006961491 0.0620525615
#> beta.all[7,5] 0.048514988 0.08742121 -0.08996914 -0.005152451 0.0292393613
#> beta.all[8,5] 0.017772400 0.08155056 -0.12698435 -0.026253553 0.0052229866
#> beta.all[7,6] -0.049282024 0.08981673 -0.26769994 -0.094549615 -0.0286712818
#> beta.all[8,6] -0.080024612 0.10335586 -0.33528771 -0.131433620 -0.0524290713
#> beta.all[8,7] -0.030742588 0.05650043 -0.15182516 -0.064723766 -0.0221587123
#> 75% 97.5% Rhat n.eff
#> beta.all[2,1] 0.0922645360 0.41110520 1.103723 310
#> beta.all[3,1] 0.0911222031 0.33438852 1.040792 610
#> beta.all[4,1] 0.0953696451 0.19994722 1.094125 28
#> beta.all[5,1] 0.0478710858 0.12335135 1.167515 19
#> beta.all[6,1] 0.1374164502 0.29308940 1.050099 98
#> beta.all[7,1] 0.0696392827 0.12807849 1.003906 810
#> beta.all[8,1] 0.0423729541 0.09332831 1.116088 24
#> beta.all[3,2] 0.0499194998 0.31959757 1.063906 960
#> beta.all[4,2] 0.0640267647 0.29080329 1.095925 470
#> beta.all[5,2] 0.0158134796 0.17081482 1.149120 40
#> beta.all[6,2] 0.0944343715 0.34874074 1.064299 3000
#> beta.all[7,2] 0.0424799020 0.24689818 1.105147 450
#> beta.all[8,2] 0.0204551632 0.19847751 1.128124 88
#> beta.all[4,3] 0.0637165569 0.30899682 1.042653 430
#> beta.all[5,3] 0.0171308988 0.18461958 1.090985 49
#> beta.all[6,3] 0.0934286957 0.36544212 1.026208 1100
#> beta.all[7,3] 0.0446214452 0.26500575 1.040891 1000
#> beta.all[8,3] 0.0230765307 0.20920250 1.062474 140
#> beta.all[5,4] 0.0015017330 0.08561596 1.176593 19
#> beta.all[6,4] 0.0708799404 0.24036841 1.030611 390
#> beta.all[7,4] 0.0179349674 0.11464046 1.055755 44
#> beta.all[8,4] -0.0007061761 0.06389523 1.136538 22
#> beta.all[6,5] 0.1591226352 0.41256337 1.132356 24
#> beta.all[7,5] 0.0933442566 0.26077571 1.173264 19
#> beta.all[8,5] 0.0522889137 0.22376767 1.063832 63
#> beta.all[7,6] 0.0058791454 0.09186261 1.046772 150
#> beta.all[8,6] -0.0061285243 0.05536143 1.089172 44
#> beta.all[8,7] 0.0039535783 0.07331461 1.092578 30
#>
#> $dev_o
#> mean sd 2.5% 25% 50% 75%
#> dev.o[1,1] 2.1560497 2.2747969 0.0061197712 0.43405078 1.4501793 3.0957344
#> dev.o[2,1] 0.8865045 1.2130370 0.0009036052 0.09566198 0.4351175 1.1839175
#> dev.o[3,1] 0.8708556 1.1961560 0.0006440374 0.08853294 0.3963623 1.1775527
#> dev.o[4,1] 0.8402122 1.2166884 0.0010862534 0.07308596 0.3592601 1.0877571
#> dev.o[5,1] 0.6645889 0.9804518 0.0005675829 0.05831306 0.2860482 0.8537939
#> dev.o[6,1] 1.0779171 1.3637956 0.0013901128 0.12970078 0.5725763 1.4974979
#> dev.o[7,1] 0.8113394 1.1209754 0.0009431878 0.08222272 0.3715326 1.0947160
#> dev.o[8,1] 0.6934740 0.9836053 0.0005746768 0.07457337 0.3114932 0.8941400
#> dev.o[9,1] 0.7551912 1.0340743 0.0007917253 0.07459973 0.3565831 1.0229403
#> dev.o[10,1] 0.5835094 0.8151358 0.0008845579 0.06188607 0.2672818 0.7549711
#> dev.o[11,1] 0.7499784 1.0794413 0.0007730863 0.07421713 0.3349313 0.9794617
#> dev.o[12,1] 1.0746712 1.3117684 0.0014807259 0.15911638 0.6143076 1.4913969
#> dev.o[13,1] 1.4794957 1.8933840 0.0015279714 0.19069689 0.7622863 2.1045998
#> dev.o[14,1] 0.8344236 1.1644638 0.0007684336 0.08898561 0.4058530 1.1135205
#> dev.o[15,1] 0.9236066 1.2856320 0.0008413893 0.10199374 0.4384865 1.2271591
#> dev.o[16,1] 1.2349815 1.6288747 0.0010158475 0.12276835 0.6004523 1.6921025
#> dev.o[17,1] 1.9694373 2.2695713 0.0023537923 0.29799285 1.1868412 2.8485628
#> dev.o[18,1] 1.2542182 1.5901008 0.0017861032 0.13727754 0.6320635 1.7599798
#> dev.o[19,1] 1.9040038 1.8307488 0.0068215383 0.51406330 1.3865702 2.8372029
#> dev.o[20,1] 0.7726423 1.0919438 0.0008734432 0.08025956 0.3409281 0.9943742
#> dev.o[21,1] 1.3720691 1.7131767 0.0021944172 0.18965109 0.7184204 1.9060450
#> dev.o[1,2] 2.8519740 1.7922883 0.5495649569 1.52263402 2.4829695 3.7738469
#> dev.o[2,2] 0.8898657 1.2573182 0.0008393223 0.08629374 0.4167965 1.1831830
#> dev.o[3,2] 0.7936156 1.0835555 0.0010347130 0.08402599 0.3709873 1.0525676
#> dev.o[4,2] 0.8955108 1.2668727 0.0006982305 0.08746247 0.4232576 1.1837070
#> dev.o[5,2] 0.5759540 0.8192362 0.0005060435 0.05589630 0.2573487 0.7802131
#> dev.o[6,2] 1.2532746 1.4903882 0.0019700984 0.15717720 0.6784907 1.8518041
#> dev.o[7,2] 0.8652072 1.2260839 0.0006220328 0.08945793 0.3902779 1.1533076
#> dev.o[8,2] 0.6521836 0.9142112 0.0005014251 0.06093938 0.2914749 0.8811683
#> dev.o[9,2] 0.6627143 0.9458511 0.0007256790 0.06420660 0.3013132 0.8793769
#> dev.o[10,2] 1.7294329 1.9020252 0.0037387438 0.30673764 1.1029046 2.5423939
#> dev.o[11,2] 0.9510833 1.4148089 0.0006287158 0.09519823 0.4023807 1.2030616
#> dev.o[12,2] 1.1111751 1.4956223 0.0015685739 0.11758073 0.5332394 1.4924883
#> dev.o[13,2] 1.0427025 1.4768151 0.0008000898 0.10282244 0.4793785 1.3684344
#> dev.o[14,2] 0.7821635 1.0999011 0.0004295030 0.07961802 0.3681982 1.0562952
#> dev.o[15,2] 0.9678904 1.3383744 0.0009284333 0.10361192 0.4350913 1.3149915
#> dev.o[16,2] 1.3099287 1.6713131 0.0020369340 0.16205104 0.6746699 1.7893776
#> dev.o[17,2] 2.2728545 2.1147994 0.0128295949 0.64710891 1.7014115 3.2994491
#> dev.o[18,2] 1.2327036 1.6139113 0.0010737803 0.12956477 0.6049387 1.6831380
#> dev.o[19,2] 0.4293190 0.5956517 0.0003872659 0.04087909 0.2041828 0.5720976
#> dev.o[20,2] 0.6914614 1.0021871 0.0006500977 0.06087130 0.3041888 0.9067656
#> dev.o[21,2] 1.3146078 1.5862540 0.0016559460 0.17022630 0.7390862 1.8748535
#> dev.o[9,3] 0.9388726 1.2605259 0.0008748094 0.09825757 0.4482231 1.2939318
#> dev.o[10,3] 0.7701822 1.0948565 0.0005870690 0.07660135 0.3332837 1.0354275
#> dev.o[12,3] 1.2428054 1.5690892 0.0015472921 0.15259396 0.6743242 1.7086676
#> dev.o[13,3] 0.9772052 1.4100000 0.0012623079 0.10801921 0.4610824 1.2871451
#> dev.o[19,3] 1.6049495 1.3337697 0.0401114181 0.61586518 1.2771723 2.2741665
#> dev.o[10,4] 1.0995241 1.3220554 0.0021077110 0.15529681 0.6010532 1.5484461
#> dev.o[12,4] 0.8565923 1.1405943 0.0010465196 0.09504678 0.4200604 1.1545940
#> dev.o[13,4] 1.0871968 1.4537834 0.0012027230 0.11982385 0.5451929 1.4446374
#> 97.5% Rhat n.eff
#> dev.o[1,1] 8.297866 1.001150 3000
#> dev.o[2,1] 4.458940 1.006780 420
#> dev.o[3,1] 4.181724 1.002483 1000
#> dev.o[4,1] 4.329539 1.001270 2600
#> dev.o[5,1] 3.471318 1.005808 380
#> dev.o[6,1] 4.892280 1.002416 1000
#> dev.o[7,1] 3.986599 1.002912 830
#> dev.o[8,1] 3.657996 1.001809 3000
#> dev.o[9,1] 3.691421 1.001515 2000
#> dev.o[10,1] 2.935917 1.002475 1000
#> dev.o[11,1] 3.872174 1.004460 540
#> dev.o[12,1] 4.737603 1.007927 310
#> dev.o[13,1] 6.610296 1.026434 88
#> dev.o[14,1] 3.900916 1.001632 1800
#> dev.o[15,1] 4.429748 1.001130 3000
#> dev.o[16,1] 5.705568 1.001759 2800
#> dev.o[17,1] 8.140028 1.014126 160
#> dev.o[18,1] 5.745956 1.000952 3000
#> dev.o[19,1] 6.545552 1.001263 2600
#> dev.o[20,1] 4.062701 1.003923 1300
#> dev.o[21,1] 6.054748 1.012199 200
#> dev.o[1,2] 7.226351 1.002597 960
#> dev.o[2,2] 4.425779 1.000810 3000
#> dev.o[3,2] 4.002930 1.001252 2700
#> dev.o[4,2] 4.395002 1.003321 710
#> dev.o[5,2] 3.129609 1.002358 2700
#> dev.o[6,2] 5.232894 1.004226 540
#> dev.o[7,2] 4.363057 1.002325 1300
#> dev.o[8,2] 3.203703 1.001418 2200
#> dev.o[9,2] 3.323535 1.002304 2500
#> dev.o[10,2] 6.941812 1.000987 3000
#> dev.o[11,2] 4.792794 1.001468 2100
#> dev.o[12,2] 5.315607 1.004526 500
#> dev.o[13,2] 4.991795 1.001557 1900
#> dev.o[14,2] 3.914398 1.013322 410
#> dev.o[15,2] 4.797278 1.003146 760
#> dev.o[16,2] 5.978983 1.007072 470
#> dev.o[17,2] 7.823776 1.014332 160
#> dev.o[18,2] 5.811015 1.001473 3000
#> dev.o[19,2] 2.251736 1.000772 3000
#> dev.o[20,2] 3.644875 1.001266 2600
#> dev.o[21,2] 5.668013 1.007938 270
#> dev.o[9,3] 4.491149 1.006642 380
#> dev.o[10,3] 3.965978 1.009841 260
#> dev.o[12,3] 5.675115 1.004784 470
#> dev.o[13,3] 4.756649 1.001556 2200
#> dev.o[19,3] 5.038304 1.002717 900
#> dev.o[10,4] 4.849228 1.018046 160
#> dev.o[12,4] 4.046988 1.008263 390
#> dev.o[13,4] 5.326679 1.001059 3000
#>
#> $hat_par
#> mean sd 2.5% 25% 50%
#> hat.par[1,1] 1.667517 0.8120079 0.3847576 1.0560707 1.568148
#> hat.par[2,1] 50.746631 4.9251038 41.1153456 47.3201217 50.717085
#> hat.par[3,1] 45.043178 4.4925139 36.5984305 41.9992127 45.010493
#> hat.par[4,1] 42.410123 5.0252951 32.8918953 39.1180807 42.163769
#> hat.par[5,1] 17.493732 2.5137420 12.6251855 15.8409880 17.491025
#> hat.par[6,1] 44.317206 4.1114521 36.2420740 41.5021664 44.382865
#> hat.par[7,1] 157.048967 7.6162763 142.0173807 151.9943375 157.101243
#> hat.par[8,1] 68.756240 5.3595204 57.9954104 65.0925250 68.737800
#> hat.par[9,1] 89.509974 4.9470456 79.9215086 86.1731495 89.477134
#> hat.par[10,1] 78.377731 3.6573296 71.0788148 75.8856619 78.433212
#> hat.par[11,1] 75.018177 5.4637705 64.4188720 71.3274692 75.092228
#> hat.par[12,1] 76.932790 4.0389445 68.4432327 74.4158003 77.074276
#> hat.par[13,1] 48.751792 4.7802256 39.6206681 45.4265299 48.716107
#> hat.par[14,1] 34.609217 4.7583391 26.1421255 31.2478784 34.389696
#> hat.par[15,1] 35.321811 4.9760454 26.1647424 31.8191242 35.257064
#> hat.par[16,1] 303.893147 13.1242786 276.8759484 295.5256721 303.867278
#> hat.par[17,1] 10.838355 2.6091508 6.2344432 8.9777252 10.656861
#> hat.par[18,1] 21.511243 3.4063565 15.2722442 19.0736626 21.393012
#> hat.par[19,1] 3.799965 1.3097927 1.7628069 2.8397863 3.649425
#> hat.par[20,1] 23.674373 3.7381590 16.6650349 21.0948130 23.539946
#> hat.par[21,1] 31.353982 4.4787239 23.3578269 28.3183898 31.126682
#> hat.par[1,2] 1.266036 0.7039992 0.2701170 0.7262141 1.149950
#> hat.par[2,2] 45.143689 5.0160664 35.5323354 41.7075271 45.114569
#> hat.par[3,2] 30.007964 3.8201706 22.5982327 27.3004262 29.912758
#> hat.par[4,2] 43.689507 5.4891269 33.4435162 39.9084532 43.444807
#> hat.par[5,2] 11.560188 2.1110390 7.7344461 10.0738533 11.485958
#> hat.par[6,2] 34.360363 3.9546431 27.2508025 31.5207618 34.259328
#> hat.par[7,2] 196.704239 9.9457909 177.2899915 189.9637616 196.704958
#> hat.par[8,2] 51.066728 4.7618474 42.1418587 47.7708511 51.026734
#> hat.par[9,2] 81.134513 5.4975032 70.4567489 77.4245701 81.015845
#> hat.par[10,2] 72.532972 3.8692776 64.8447288 69.9285863 72.519238
#> hat.par[11,2] 117.165143 8.4821887 100.8211522 111.5690389 117.060599
#> hat.par[12,2] 82.173650 4.9607325 72.0423169 78.9599779 82.391494
#> hat.par[13,2] 26.567578 4.6747530 17.8348367 23.3147562 26.280703
#> hat.par[14,2] 31.464088 4.5461572 23.5323288 28.3462874 31.259966
#> hat.par[15,2] 33.447675 4.8179559 24.6618156 30.1103463 33.199263
#> hat.par[16,2] 246.825648 12.6062428 223.3042122 238.2861005 246.533551
#> hat.par[17,2] 7.224937 2.0189214 3.7562072 5.7514282 7.037035
#> hat.par[18,2] 13.446727 2.7156847 8.6362130 11.5740919 13.210594
#> hat.par[19,2] 2.523815 0.9552056 1.0329537 1.8316419 2.388848
#> hat.par[20,2] 20.275551 3.4007293 13.9392778 17.9419802 20.075434
#> hat.par[21,2] 22.542584 3.8063225 15.3270360 19.8728268 22.430673
#> hat.par[9,3] 80.476141 5.7566248 69.2942815 76.5161444 80.425658
#> hat.par[10,3] 69.308245 4.3063051 60.9231832 66.4090494 69.334230
#> hat.par[12,3] 66.884849 4.6547079 57.6998485 63.8609578 66.791724
#> hat.par[13,3] 35.430830 5.1256320 25.9735121 31.7014502 35.268579
#> hat.par[19,3] 2.692587 0.9778648 1.1920257 1.9848861 2.555053
#> hat.par[10,4] 66.518634 4.2520924 58.0262144 63.7580424 66.666819
#> hat.par[12,4] 62.315142 4.2857059 54.0224091 59.4530219 62.281698
#> hat.par[13,4] 41.208300 4.6510921 32.3060705 38.0774093 41.132665
#> 75% 97.5% Rhat n.eff
#> hat.par[1,1] 2.199784 3.428572 1.000869 3000
#> hat.par[2,1] 54.198177 60.347152 1.007357 360
#> hat.par[3,1] 48.050522 54.139562 1.008581 250
#> hat.par[4,1] 45.534946 52.968482 1.028183 83
#> hat.par[5,1] 19.146720 22.666924 1.012523 320
#> hat.par[6,1] 47.128698 52.330931 1.007323 300
#> hat.par[7,1] 162.212781 171.628784 1.027208 80
#> hat.par[8,1] 72.201482 79.384476 1.020166 100
#> hat.par[9,1] 92.748968 99.338802 1.001559 2900
#> hat.par[10,1] 80.976401 85.286396 1.002828 3000
#> hat.par[11,1] 78.645427 86.107070 1.005498 620
#> hat.par[12,1] 79.573709 84.702238 1.015766 140
#> hat.par[13,1] 51.942457 58.091448 1.052882 43
#> hat.par[14,1] 37.757934 44.560743 1.008543 250
#> hat.par[15,1] 38.535561 45.340192 1.026744 82
#> hat.par[16,1] 312.707450 328.863416 1.006515 330
#> hat.par[17,1] 12.525794 16.447137 1.019378 110
#> hat.par[18,1] 23.772523 28.571662 1.000593 3000
#> hat.par[19,1] 4.514016 6.792177 1.002476 1000
#> hat.par[20,1] 26.077533 31.509717 1.002301 3000
#> hat.par[21,1] 34.172351 40.617649 1.014018 170
#> hat.par[1,2] 1.680904 2.907369 1.002499 1000
#> hat.par[2,2] 48.591868 55.247994 1.003633 640
#> hat.par[3,2] 32.479231 37.903053 1.001654 1700
#> hat.par[4,2] 47.163976 54.869768 1.031639 68
#> hat.par[5,2] 12.955170 16.005461 1.003089 1600
#> hat.par[6,2] 36.962705 42.607421 1.007251 300
#> hat.par[7,2] 203.471048 216.216053 1.003856 600
#> hat.par[8,2] 54.187755 60.790041 1.009812 220
#> hat.par[9,2] 84.649481 92.108589 1.012601 170
#> hat.par[10,2] 75.048284 80.095001 1.001794 1500
#> hat.par[11,2] 122.698100 134.323492 1.001113 3000
#> hat.par[12,2] 85.492733 91.485646 1.020760 100
#> hat.par[13,2] 29.759781 35.976256 1.019678 110
#> hat.par[14,2] 34.453152 40.977940 1.025288 84
#> hat.par[15,2] 36.591795 43.590396 1.019567 110
#> hat.par[16,2] 254.821913 272.778443 1.006641 330
#> hat.par[17,2] 8.502414 11.711900 1.021123 99
#> hat.par[18,2] 15.151386 19.257280 1.002350 1400
#> hat.par[19,2] 3.103659 4.808962 1.002076 3000
#> hat.par[20,2] 22.405014 27.406076 1.010567 200
#> hat.par[21,2] 25.029216 30.279170 1.011799 180
#> hat.par[9,3] 84.432016 91.751613 1.022327 97
#> hat.par[10,3] 72.261138 77.693963 1.023655 95
#> hat.par[12,3] 70.001860 76.141050 1.010072 220
#> hat.par[13,3] 38.766145 45.796321 1.011844 180
#> hat.par[19,3] 3.277384 4.978632 1.004119 550
#> hat.par[10,4] 69.436531 74.478304 1.018866 110
#> hat.par[12,4] 65.213347 70.733043 1.010313 220
#> hat.par[13,4] 44.174184 50.547827 1.005019 480
#>
#> $leverage_o
#> [1] 0.9325701 0.8841921 0.7355146 0.7776254 0.6395452 0.6479018 0.7963034
#> [8] 0.6799498 0.6023772 0.5669936 0.7499702 0.5629938 0.8411800 0.7714981
#> [15] 0.8613051 0.9042444 0.9474875 0.8440144 0.7339899 0.7669681 0.8609185
#> [22] 0.2157863 0.8891428 0.6144398 0.8488834 0.5373964 0.7026677 0.8508373
#> [29] 0.6285110 0.6468010 0.6903855 0.9507241 0.7965031 1.0275510 0.7049569
#> [36] 0.8809808 0.9287899 0.3874329 0.6286146 0.3073435 0.6869232 0.6480646
#> [43] 0.6855439 0.7065442 0.6961282 0.9703128 0.1495023 0.6606186 0.6155931
#> [50] 0.7243915
#>
#> $sign_dev_o
#> [1] 1 1 1 -1 1 -1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 1 1 1 -1 -1 -1 1
#> [26] -1 1 1 1 1 1 -1 -1 -1 -1 1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1
#>
#> $phi
#> mean sd 2.5% 25% 50% 75%
#> phi[1] -0.33453166 0.4693772 -1.317625 -0.6276799 -0.31567546 -0.02550441
#> phi[2] 0.14622211 0.9554933 -1.734346 -0.5286982 0.17232897 0.86244063
#> phi[3] 0.05037396 0.9469083 -1.837083 -0.5792161 0.06981625 0.70753366
#> phi[4] -0.81557208 0.8285695 -2.403535 -1.3713015 -0.84998430 -0.29468823
#> phi[5] -0.43112873 0.9203390 -2.081707 -1.0603185 -0.48384856 0.14424128
#> phi[6] 0.47010445 0.9138056 -1.482954 -0.1088446 0.53131284 1.09712329
#> phi[7] -0.34256746 0.6878830 -1.741141 -0.7972201 -0.32733509 0.11291696
#> phi[8] -0.03513310 0.9762807 -1.947862 -0.6709125 -0.02758958 0.62456115
#> 97.5% Rhat n.eff
#> phi[1] 0.5768523 1.218503 15
#> phi[2] 1.8433629 1.045666 50
#> phi[3] 1.8113542 1.024877 85
#> phi[4] 0.9423465 1.082481 32
#> phi[5] 1.4263815 1.101937 26
#> phi[6] 2.1366788 1.006273 3000
#> phi[7] 1.0548385 1.001683 3000
#> phi[8] 1.8444215 1.008904 340
#>
#> $model_assessment
#> DIC pD dev
#> 1 90.65103 35.88891 54.76212
#>
#> $measure
#> [1] "OR"
#>
#> $model
#> [1] "RE"
#>
#> $assumption
#> [1] "IDE-ARM"
#>
#> $covariate
#> [1] 1996 1998 1999 2000 2000 2001 2002 2002 2002 2002 2002 2003 2003 2005 2005
#> [16] 2005 2005 2006 2006 2007 2007
#>
#> $covar_assumption
#> [1] "exchangeable"
#>
#> $cov_value
#> [1] 2007
#>
#> $jagsfit
#> Inference for Bugs model at "4", fit using jags,
#> 3 chains, each with 1000 iterations (first 0 discarded)
#> n.sims = 3000 iterations saved. Running time = secs
#> mu.vect sd.vect 2.5% 25% 50% 75% 97.5%
#> EM[2,1] -0.658 0.740 -2.024 -1.062 -0.643 -0.312 0.898
#> EM[3,1] -0.468 0.659 -1.731 -0.835 -0.472 -0.136 0.981
#> EM[4,1] 0.166 0.508 -0.746 -0.160 0.135 0.458 1.302
#> EM[5,1] -0.407 0.366 -1.200 -0.625 -0.396 -0.158 0.276
#> EM[6,1] 0.192 0.414 -0.557 -0.092 0.183 0.443 1.092
#> EM[7,1] -0.248 0.268 -0.764 -0.431 -0.259 -0.069 0.297
#> EM[8,1] -0.416 0.219 -0.905 -0.548 -0.407 -0.268 -0.028
#> EM[3,2] 0.189 0.794 -1.417 -0.196 0.161 0.581 1.845
#> EM[4,2] 0.824 0.830 -0.712 0.312 0.811 1.310 2.520
#> EM[5,2] 0.250 0.794 -1.523 -0.131 0.264 0.718 1.680
#> EM[6,2] 0.850 0.758 -0.581 0.375 0.826 1.299 2.380
#> EM[7,2] 0.409 0.774 -1.234 -0.012 0.403 0.867 1.834
#> EM[8,2] 0.242 0.746 -1.406 -0.090 0.257 0.649 1.508
#> EM[4,3] 0.635 0.748 -0.759 0.134 0.614 1.061 2.294
#> EM[5,3] 0.061 0.723 -1.609 -0.270 0.103 0.477 1.399
#> EM[6,3] 0.661 0.702 -0.647 0.196 0.638 1.062 2.225
#> EM[7,3] 0.220 0.675 -1.184 -0.155 0.229 0.623 1.517
#> EM[8,3] 0.053 0.679 -1.497 -0.277 0.091 0.446 1.302
#> EM[5,4] -0.574 0.642 -2.095 -0.931 -0.495 -0.146 0.484
#> EM[6,4] 0.026 0.548 -1.013 -0.317 0.012 0.361 1.176
#> EM[7,4] -0.415 0.518 -1.605 -0.693 -0.364 -0.113 0.584
#> EM[8,4] -0.582 0.544 -1.868 -0.872 -0.526 -0.207 0.281
#> EM[6,5] 0.600 0.540 -0.267 0.206 0.531 0.909 1.818
#> EM[7,5] 0.159 0.428 -0.600 -0.129 0.115 0.409 1.109
#> EM[8,5] -0.009 0.401 -0.783 -0.281 0.001 0.234 0.828
#> EM[7,6] -0.441 0.420 -1.426 -0.670 -0.408 -0.176 0.315
#> EM[8,6] -0.608 0.462 -1.664 -0.867 -0.583 -0.294 0.162
#> EM[8,7] -0.167 0.306 -0.810 -0.359 -0.159 0.043 0.391
#> EM.pred[2,1] -0.657 0.755 -2.054 -1.086 -0.639 -0.304 0.940
#> EM.pred[3,1] -0.466 0.675 -1.816 -0.846 -0.456 -0.128 0.990
#> EM.pred[4,1] 0.166 0.529 -0.786 -0.180 0.139 0.463 1.333
#> EM.pred[5,1] -0.406 0.394 -1.238 -0.643 -0.399 -0.148 0.326
#> EM.pred[6,1] 0.195 0.438 -0.597 -0.101 0.186 0.468 1.152
#> EM.pred[7,1] -0.251 0.308 -0.872 -0.453 -0.258 -0.045 0.353
#> EM.pred[8,1] -0.412 0.262 -0.996 -0.551 -0.399 -0.243 0.052
#> EM.pred[3,2] 0.190 0.811 -1.462 -0.198 0.168 0.594 1.849
#> EM.pred[4,2] 0.824 0.838 -0.736 0.313 0.801 1.307 2.524
#> EM.pred[5,2] 0.249 0.806 -1.545 -0.139 0.242 0.726 1.715
#> EM.pred[6,2] 0.853 0.772 -0.634 0.354 0.822 1.308 2.427
#> EM.pred[7,2] 0.411 0.781 -1.205 -0.025 0.400 0.885 1.862
#> EM.pred[8,2] 0.239 0.761 -1.366 -0.112 0.251 0.664 1.552
#> EM.pred[4,3] 0.638 0.762 -0.766 0.132 0.607 1.079 2.297
#> EM.pred[5,3] 0.064 0.736 -1.665 -0.287 0.104 0.485 1.455
#> EM.pred[6,3] 0.664 0.721 -0.655 0.185 0.629 1.075 2.280
#> EM.pred[7,3] 0.217 0.688 -1.198 -0.165 0.224 0.626 1.523
#> EM.pred[8,3] 0.049 0.693 -1.600 -0.293 0.085 0.446 1.369
#> EM.pred[5,4] -0.573 0.659 -2.084 -0.946 -0.498 -0.129 0.555
#> EM.pred[6,4] 0.019 0.568 -1.079 -0.337 0.007 0.367 1.198
#> EM.pred[7,4] -0.417 0.542 -1.600 -0.719 -0.364 -0.095 0.631
#> EM.pred[8,4] -0.583 0.564 -1.898 -0.883 -0.524 -0.203 0.372
#> EM.pred[6,5] 0.601 0.562 -0.318 0.198 0.536 0.924 1.888
#> EM.pred[7,5] 0.163 0.458 -0.682 -0.142 0.119 0.440 1.158
#> EM.pred[8,5] -0.005 0.424 -0.845 -0.277 0.006 0.249 0.882
#> EM.pred[7,6] -0.440 0.443 -1.430 -0.682 -0.402 -0.161 0.340
#> EM.pred[8,6] -0.604 0.478 -1.679 -0.883 -0.587 -0.277 0.190
#> EM.pred[8,7] -0.165 0.341 -0.860 -0.388 -0.151 0.064 0.452
#> SUCRA[1] 0.272 0.181 0.000 0.143 0.286 0.429 0.571
#> SUCRA[2] 0.768 0.290 0.000 0.571 0.857 1.000 1.000
#> SUCRA[3] 0.669 0.310 0.000 0.429 0.714 0.857 1.000
#> SUCRA[4] 0.237 0.260 0.000 0.000 0.143 0.429 0.857
#> SUCRA[5] 0.643 0.261 0.143 0.429 0.714 0.857 1.000
#> SUCRA[6] 0.195 0.212 0.000 0.000 0.143 0.286 0.714
#> SUCRA[7] 0.541 0.234 0.143 0.429 0.571 0.714 1.000
#> SUCRA[8] 0.676 0.203 0.286 0.571 0.714 0.857 1.000
#> abs_risk[1] 0.390 0.000 0.390 0.390 0.390 0.390 0.390
#> abs_risk[2] 0.267 0.133 0.078 0.181 0.252 0.319 0.611
#> abs_risk[3] 0.300 0.127 0.102 0.217 0.285 0.358 0.630
#> abs_risk[4] 0.433 0.118 0.233 0.353 0.423 0.503 0.702
#> abs_risk[5] 0.304 0.074 0.161 0.255 0.301 0.353 0.457
#> abs_risk[6] 0.439 0.098 0.268 0.368 0.434 0.499 0.656
#> abs_risk[7] 0.335 0.059 0.229 0.293 0.331 0.374 0.463
#> abs_risk[8] 0.299 0.045 0.206 0.270 0.298 0.329 0.383
#> beta[1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> beta[2] 0.053 0.150 -0.200 -0.006 0.039 0.092 0.411
#> beta[3] 0.046 0.131 -0.214 -0.010 0.041 0.091 0.334
#> beta[4] 0.062 0.061 -0.045 0.023 0.056 0.095 0.200
#> beta[5] -0.006 0.083 -0.205 -0.047 0.007 0.048 0.123
#> beta[6] 0.092 0.086 -0.039 0.035 0.079 0.137 0.293
#> beta[7] 0.043 0.042 -0.042 0.015 0.043 0.070 0.128
#> beta[8] 0.012 0.045 -0.093 -0.013 0.016 0.042 0.093
#> beta.all[2,1] 0.053 0.150 -0.200 -0.006 0.039 0.092 0.411
#> beta.all[3,1] 0.046 0.131 -0.214 -0.010 0.041 0.091 0.334
#> beta.all[4,1] 0.062 0.061 -0.045 0.023 0.056 0.095 0.200
#> beta.all[5,1] -0.006 0.083 -0.205 -0.047 0.007 0.048 0.123
#> beta.all[6,1] 0.092 0.086 -0.039 0.035 0.079 0.137 0.293
#> beta.all[7,1] 0.043 0.042 -0.042 0.015 0.043 0.070 0.128
#> beta.all[8,1] 0.012 0.045 -0.093 -0.013 0.016 0.042 0.093
#> beta.all[3,2] -0.006 0.165 -0.384 -0.050 -0.001 0.050 0.320
#> beta.all[4,2] 0.009 0.153 -0.314 -0.031 0.007 0.064 0.291
#> beta.all[5,2] -0.059 0.170 -0.529 -0.101 -0.021 0.016 0.171
#> beta.all[6,2] 0.039 0.146 -0.268 -0.014 0.021 0.094 0.349
#> beta.all[7,2] -0.010 0.150 -0.366 -0.044 0.000 0.042 0.247
#> beta.all[8,2] -0.041 0.157 -0.438 -0.079 -0.013 0.020 0.198
#> beta.all[4,3] 0.015 0.132 -0.266 -0.030 0.008 0.064 0.309
#> beta.all[5,3] -0.052 0.151 -0.440 -0.102 -0.017 0.017 0.185
#> beta.all[6,3] 0.046 0.136 -0.218 -0.013 0.024 0.093 0.365
#> beta.all[7,3] -0.004 0.132 -0.297 -0.044 0.001 0.045 0.265
#> beta.all[8,3] -0.034 0.139 -0.378 -0.079 -0.014 0.023 0.209
#> beta.all[5,4] -0.068 0.108 -0.353 -0.114 -0.039 0.002 0.086
#> beta.all[6,4] 0.030 0.088 -0.133 -0.016 0.015 0.071 0.240
#> beta.all[7,4] -0.019 0.069 -0.177 -0.053 -0.010 0.018 0.115
#> beta.all[8,4] -0.050 0.075 -0.242 -0.086 -0.034 -0.001 0.064
#> beta.all[6,5] 0.098 0.124 -0.053 0.007 0.062 0.159 0.413
#> beta.all[7,5] 0.049 0.087 -0.090 -0.005 0.029 0.093 0.261
#> beta.all[8,5] 0.018 0.082 -0.127 -0.026 0.005 0.052 0.224
#> beta.all[7,6] -0.049 0.090 -0.268 -0.095 -0.029 0.006 0.092
#> beta.all[8,6] -0.080 0.103 -0.335 -0.131 -0.052 -0.006 0.055
#> beta.all[8,7] -0.031 0.057 -0.152 -0.065 -0.022 0.004 0.073
#> delta[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[2,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[3,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[4,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[5,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[6,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[7,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[8,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[9,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[10,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[11,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[12,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[13,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[14,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[15,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[16,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[17,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[18,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[19,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[20,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[21,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> delta[1,2] -0.126 0.340 -0.776 -0.342 -0.137 0.063 0.633
#> delta[2,2] -0.094 0.327 -0.693 -0.307 -0.110 0.096 0.646
#> delta[3,2] -0.468 0.195 -0.870 -0.594 -0.470 -0.342 -0.093
#> delta[4,2] -0.442 0.166 -0.776 -0.540 -0.447 -0.338 -0.109
#> delta[5,2] -0.443 0.207 -0.835 -0.582 -0.448 -0.308 -0.032
#> delta[6,2] -0.370 0.200 -0.707 -0.516 -0.385 -0.231 0.045
#> delta[7,2] -0.438 0.149 -0.744 -0.530 -0.439 -0.340 -0.149
#> delta[8,2] -0.419 0.179 -0.742 -0.544 -0.422 -0.302 -0.062
#> delta[9,2] -0.443 0.181 -0.778 -0.567 -0.448 -0.320 -0.078
#> delta[10,2] -0.056 0.338 -0.643 -0.297 -0.070 0.135 0.711
#> delta[11,2] -0.221 0.274 -0.771 -0.416 -0.244 -0.019 0.291
#> delta[12,2] -0.108 0.301 -0.639 -0.316 -0.121 0.067 0.566
#> delta[13,2] -0.910 0.422 -1.864 -1.182 -0.852 -0.583 -0.251
#> delta[14,2] -0.047 0.192 -0.419 -0.173 -0.048 0.074 0.318
#> delta[15,2] -0.187 0.293 -0.768 -0.400 -0.198 0.035 0.351
#> delta[16,2] -0.362 0.147 -0.632 -0.468 -0.368 -0.262 -0.060
#> delta[17,2] -0.316 0.332 -1.006 -0.553 -0.297 -0.074 0.269
#> delta[18,2] -0.424 0.287 -0.935 -0.617 -0.466 -0.240 0.195
#> delta[19,2] -0.410 0.290 -0.924 -0.608 -0.451 -0.215 0.192
#> delta[20,2] -0.432 0.205 -0.829 -0.574 -0.438 -0.291 -0.031
#> delta[21,2] -0.516 0.189 -0.962 -0.622 -0.495 -0.394 -0.174
#> delta[9,3] -0.500 0.163 -0.856 -0.596 -0.489 -0.399 -0.191
#> delta[10,3] -0.392 0.281 -0.860 -0.596 -0.438 -0.211 0.206
#> delta[12,3] -0.324 0.288 -0.776 -0.532 -0.363 -0.138 0.301
#> delta[13,3] -0.693 0.359 -1.421 -0.944 -0.671 -0.461 0.036
#> delta[19,3] -0.478 0.226 -0.975 -0.618 -0.472 -0.331 -0.065
#> delta[10,4] -0.459 0.205 -0.894 -0.589 -0.458 -0.321 -0.074
#> delta[12,4] -0.386 0.209 -0.759 -0.532 -0.392 -0.247 0.031
#> delta[13,4] -0.262 0.309 -0.882 -0.460 -0.296 -0.019 0.303
#> dev.o[1,1] 2.156 2.275 0.006 0.434 1.450 3.096 8.298
#> dev.o[2,1] 0.887 1.213 0.001 0.096 0.435 1.184 4.459
#> dev.o[3,1] 0.871 1.196 0.001 0.089 0.396 1.178 4.182
#> dev.o[4,1] 0.840 1.217 0.001 0.073 0.359 1.088 4.330
#> dev.o[5,1] 0.665 0.980 0.001 0.058 0.286 0.854 3.471
#> dev.o[6,1] 1.078 1.364 0.001 0.130 0.573 1.497 4.892
#> dev.o[7,1] 0.811 1.121 0.001 0.082 0.372 1.095 3.987
#> dev.o[8,1] 0.693 0.984 0.001 0.075 0.311 0.894 3.658
#> dev.o[9,1] 0.755 1.034 0.001 0.075 0.357 1.023 3.691
#> dev.o[10,1] 0.584 0.815 0.001 0.062 0.267 0.755 2.936
#> dev.o[11,1] 0.750 1.079 0.001 0.074 0.335 0.979 3.872
#> dev.o[12,1] 1.075 1.312 0.001 0.159 0.614 1.491 4.738
#> dev.o[13,1] 1.479 1.893 0.002 0.191 0.762 2.105 6.610
#> dev.o[14,1] 0.834 1.164 0.001 0.089 0.406 1.114 3.901
#> dev.o[15,1] 0.924 1.286 0.001 0.102 0.438 1.227 4.430
#> dev.o[16,1] 1.235 1.629 0.001 0.123 0.600 1.692 5.706
#> dev.o[17,1] 1.969 2.270 0.002 0.298 1.187 2.849 8.140
#> dev.o[18,1] 1.254 1.590 0.002 0.137 0.632 1.760 5.746
#> dev.o[19,1] 1.904 1.831 0.007 0.514 1.387 2.837 6.546
#> dev.o[20,1] 0.773 1.092 0.001 0.080 0.341 0.994 4.063
#> dev.o[21,1] 1.372 1.713 0.002 0.190 0.718 1.906 6.055
#> dev.o[1,2] 2.852 1.792 0.550 1.523 2.483 3.774 7.226
#> dev.o[2,2] 0.890 1.257 0.001 0.086 0.417 1.183 4.426
#> dev.o[3,2] 0.794 1.084 0.001 0.084 0.371 1.053 4.003
#> dev.o[4,2] 0.896 1.267 0.001 0.087 0.423 1.184 4.395
#> dev.o[5,2] 0.576 0.819 0.001 0.056 0.257 0.780 3.130
#> dev.o[6,2] 1.253 1.490 0.002 0.157 0.678 1.852 5.233
#> dev.o[7,2] 0.865 1.226 0.001 0.089 0.390 1.153 4.363
#> dev.o[8,2] 0.652 0.914 0.001 0.061 0.291 0.881 3.204
#> dev.o[9,2] 0.663 0.946 0.001 0.064 0.301 0.879 3.324
#> dev.o[10,2] 1.729 1.902 0.004 0.307 1.103 2.542 6.942
#> dev.o[11,2] 0.951 1.415 0.001 0.095 0.402 1.203 4.793
#> dev.o[12,2] 1.111 1.496 0.002 0.118 0.533 1.492 5.316
#> dev.o[13,2] 1.043 1.477 0.001 0.103 0.479 1.368 4.992
#> dev.o[14,2] 0.782 1.100 0.000 0.080 0.368 1.056 3.914
#> dev.o[15,2] 0.968 1.338 0.001 0.104 0.435 1.315 4.797
#> dev.o[16,2] 1.310 1.671 0.002 0.162 0.675 1.789 5.979
#> dev.o[17,2] 2.273 2.115 0.013 0.647 1.701 3.299 7.824
#> dev.o[18,2] 1.233 1.614 0.001 0.130 0.605 1.683 5.811
#> dev.o[19,2] 0.429 0.596 0.000 0.041 0.204 0.572 2.252
#> dev.o[20,2] 0.691 1.002 0.001 0.061 0.304 0.907 3.645
#> dev.o[21,2] 1.315 1.586 0.002 0.170 0.739 1.875 5.668
#> dev.o[9,3] 0.939 1.261 0.001 0.098 0.448 1.294 4.491
#> dev.o[10,3] 0.770 1.095 0.001 0.077 0.333 1.035 3.966
#> dev.o[12,3] 1.243 1.569 0.002 0.153 0.674 1.709 5.675
#> dev.o[13,3] 0.977 1.410 0.001 0.108 0.461 1.287 4.757
#> dev.o[19,3] 1.605 1.334 0.040 0.616 1.277 2.274 5.038
#> dev.o[10,4] 1.100 1.322 0.002 0.155 0.601 1.548 4.849
#> dev.o[12,4] 0.857 1.141 0.001 0.095 0.420 1.155 4.047
#> dev.o[13,4] 1.087 1.454 0.001 0.120 0.545 1.445 5.327
#> effectiveness[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,1] 0.434 0.496 0.000 0.000 0.000 1.000 1.000
#> effectiveness[3,1] 0.240 0.427 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,1] 0.016 0.127 0.000 0.000 0.000 0.000 0.000
#> effectiveness[5,1] 0.148 0.355 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,1] 0.007 0.081 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,1] 0.057 0.232 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,1] 0.098 0.297 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,2] 0.004 0.060 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,2] 0.196 0.397 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,2] 0.250 0.433 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,2] 0.026 0.159 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,2] 0.186 0.389 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,2] 0.011 0.104 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,2] 0.104 0.305 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,2] 0.223 0.417 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,3] 0.017 0.128 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,3] 0.108 0.310 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,3] 0.120 0.325 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,3] 0.059 0.236 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,3] 0.224 0.417 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,3] 0.025 0.157 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,3] 0.177 0.382 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,3] 0.269 0.444 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,4] 0.088 0.284 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,4] 0.074 0.261 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,4] 0.113 0.317 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,4] 0.080 0.272 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,4] 0.151 0.358 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,4] 0.056 0.231 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,4] 0.211 0.408 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,4] 0.226 0.418 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,5] 0.205 0.404 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,5] 0.061 0.239 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,5] 0.078 0.268 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,5] 0.087 0.282 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,5] 0.136 0.343 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,5] 0.098 0.297 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,5] 0.220 0.414 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,5] 0.116 0.320 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,6] 0.293 0.455 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,6] 0.052 0.222 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,6] 0.076 0.265 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,6] 0.135 0.341 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,6] 0.081 0.273 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,6] 0.158 0.364 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,6] 0.160 0.367 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,6] 0.045 0.208 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,7] 0.241 0.428 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,7] 0.035 0.185 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,7] 0.066 0.248 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,7] 0.239 0.426 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,7] 0.051 0.220 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,7] 0.291 0.454 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,7] 0.054 0.227 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,7] 0.023 0.150 0.000 0.000 0.000 0.000 0.000
#> effectiveness[1,8] 0.152 0.359 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,8] 0.039 0.194 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,8] 0.058 0.233 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,8] 0.358 0.479 0.000 0.000 0.000 1.000 1.000
#> effectiveness[5,8] 0.022 0.148 0.000 0.000 0.000 0.000 0.000
#> effectiveness[6,8] 0.354 0.478 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,8] 0.016 0.127 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,8] 0.000 0.018 0.000 0.000 0.000 0.000 0.000
#> hat.par[1,1] 1.668 0.812 0.385 1.056 1.568 2.200 3.429
#> hat.par[2,1] 50.747 4.925 41.115 47.320 50.717 54.198 60.347
#> hat.par[3,1] 45.043 4.493 36.598 41.999 45.010 48.051 54.140
#> hat.par[4,1] 42.410 5.025 32.892 39.118 42.164 45.535 52.968
#> hat.par[5,1] 17.494 2.514 12.625 15.841 17.491 19.147 22.667
#> hat.par[6,1] 44.317 4.111 36.242 41.502 44.383 47.129 52.331
#> hat.par[7,1] 157.049 7.616 142.017 151.994 157.101 162.213 171.629
#> hat.par[8,1] 68.756 5.360 57.995 65.093 68.738 72.201 79.384
#> hat.par[9,1] 89.510 4.947 79.922 86.173 89.477 92.749 99.339
#> hat.par[10,1] 78.378 3.657 71.079 75.886 78.433 80.976 85.286
#> hat.par[11,1] 75.018 5.464 64.419 71.327 75.092 78.645 86.107
#> hat.par[12,1] 76.933 4.039 68.443 74.416 77.074 79.574 84.702
#> hat.par[13,1] 48.752 4.780 39.621 45.427 48.716 51.942 58.091
#> hat.par[14,1] 34.609 4.758 26.142 31.248 34.390 37.758 44.561
#> hat.par[15,1] 35.322 4.976 26.165 31.819 35.257 38.536 45.340
#> hat.par[16,1] 303.893 13.124 276.876 295.526 303.867 312.707 328.863
#> hat.par[17,1] 10.838 2.609 6.234 8.978 10.657 12.526 16.447
#> hat.par[18,1] 21.511 3.406 15.272 19.074 21.393 23.773 28.572
#> hat.par[19,1] 3.800 1.310 1.763 2.840 3.649 4.514 6.792
#> hat.par[20,1] 23.674 3.738 16.665 21.095 23.540 26.078 31.510
#> hat.par[21,1] 31.354 4.479 23.358 28.318 31.127 34.172 40.618
#> hat.par[1,2] 1.266 0.704 0.270 0.726 1.150 1.681 2.907
#> hat.par[2,2] 45.144 5.016 35.532 41.708 45.115 48.592 55.248
#> hat.par[3,2] 30.008 3.820 22.598 27.300 29.913 32.479 37.903
#> hat.par[4,2] 43.690 5.489 33.444 39.908 43.445 47.164 54.870
#> hat.par[5,2] 11.560 2.111 7.734 10.074 11.486 12.955 16.005
#> hat.par[6,2] 34.360 3.955 27.251 31.521 34.259 36.963 42.607
#> hat.par[7,2] 196.704 9.946 177.290 189.964 196.705 203.471 216.216
#> hat.par[8,2] 51.067 4.762 42.142 47.771 51.027 54.188 60.790
#> hat.par[9,2] 81.135 5.498 70.457 77.425 81.016 84.649 92.109
#> hat.par[10,2] 72.533 3.869 64.845 69.929 72.519 75.048 80.095
#> hat.par[11,2] 117.165 8.482 100.821 111.569 117.061 122.698 134.323
#> hat.par[12,2] 82.174 4.961 72.042 78.960 82.391 85.493 91.486
#> hat.par[13,2] 26.568 4.675 17.835 23.315 26.281 29.760 35.976
#> hat.par[14,2] 31.464 4.546 23.532 28.346 31.260 34.453 40.978
#> hat.par[15,2] 33.448 4.818 24.662 30.110 33.199 36.592 43.590
#> hat.par[16,2] 246.826 12.606 223.304 238.286 246.534 254.822 272.778
#> hat.par[17,2] 7.225 2.019 3.756 5.751 7.037 8.502 11.712
#> hat.par[18,2] 13.447 2.716 8.636 11.574 13.211 15.151 19.257
#> hat.par[19,2] 2.524 0.955 1.033 1.832 2.389 3.104 4.809
#> hat.par[20,2] 20.276 3.401 13.939 17.942 20.075 22.405 27.406
#> hat.par[21,2] 22.543 3.806 15.327 19.873 22.431 25.029 30.279
#> hat.par[9,3] 80.476 5.757 69.294 76.516 80.426 84.432 91.752
#> hat.par[10,3] 69.308 4.306 60.923 66.409 69.334 72.261 77.694
#> hat.par[12,3] 66.885 4.655 57.700 63.861 66.792 70.002 76.141
#> hat.par[13,3] 35.431 5.126 25.974 31.701 35.269 38.766 45.796
#> hat.par[19,3] 2.693 0.978 1.192 1.985 2.555 3.277 4.979
#> hat.par[10,4] 66.519 4.252 58.026 63.758 66.667 69.437 74.478
#> hat.par[12,4] 62.315 4.286 54.022 59.453 62.282 65.213 70.733
#> hat.par[13,4] 41.208 4.651 32.306 38.077 41.133 44.174 50.548
#> phi[1] -0.335 0.469 -1.318 -0.628 -0.316 -0.026 0.577
#> phi[2] 0.146 0.955 -1.734 -0.529 0.172 0.862 1.843
#> phi[3] 0.050 0.947 -1.837 -0.579 0.070 0.708 1.811
#> phi[4] -0.816 0.829 -2.404 -1.371 -0.850 -0.295 0.942
#> phi[5] -0.431 0.920 -2.082 -1.060 -0.484 0.144 1.426
#> phi[6] 0.470 0.914 -1.483 -0.109 0.531 1.097 2.137
#> phi[7] -0.343 0.688 -1.741 -0.797 -0.327 0.113 1.055
#> phi[8] -0.035 0.976 -1.948 -0.671 -0.028 0.625 1.844
#> tau 0.121 0.083 0.013 0.047 0.109 0.174 0.314
#> totresdev.o 54.762 9.129 38.357 48.322 54.252 60.423 73.938
#> deviance 582.422 13.492 557.480 572.937 581.523 591.203 611.437
#> Rhat n.eff
#> EM[2,1] 1.038 400
#> EM[3,1] 1.028 1400
#> EM[4,1] 1.222 14
#> EM[5,1] 1.013 1100
#> EM[6,1] 1.076 33
#> EM[7,1] 1.068 39
#> EM[8,1] 1.030 180
#> EM[3,2] 1.034 1300
#> EM[4,2] 1.084 45
#> EM[5,2] 1.031 360
#> EM[6,2] 1.032 120
#> EM[7,2] 1.042 210
#> EM[8,2] 1.042 450
#> EM[4,3] 1.080 35
#> EM[5,3] 1.034 1300
#> EM[6,3] 1.029 130
#> EM[7,3] 1.029 310
#> EM[8,3] 1.030 770
#> EM[5,4] 1.128 23
#> EM[6,4] 1.044 54
#> EM[7,4] 1.109 24
#> EM[8,4] 1.225 14
#> EM[6,5] 1.043 70
#> EM[7,5] 1.035 160
#> EM[8,5] 1.018 220
#> EM[7,6] 1.024 160
#> EM[8,6] 1.089 30
#> EM[8,7] 1.091 28
#> EM.pred[2,1] 1.037 410
#> EM.pred[3,1] 1.026 1600
#> EM.pred[4,1] 1.190 15
#> EM.pred[5,1] 1.012 790
#> EM.pred[6,1] 1.068 39
#> EM.pred[7,1] 1.047 54
#> EM.pred[8,1] 1.022 280
#> EM.pred[3,2] 1.032 1000
#> EM.pred[4,2] 1.080 45
#> EM.pred[5,2] 1.031 360
#> EM.pred[6,2] 1.029 130
#> EM.pred[7,2] 1.039 180
#> EM.pred[8,2] 1.040 440
#> EM.pred[4,3] 1.078 35
#> EM.pred[5,3] 1.033 1600
#> EM.pred[6,3] 1.027 120
#> EM.pred[7,3] 1.029 310
#> EM.pred[8,3] 1.030 900
#> EM.pred[5,4] 1.119 24
#> EM.pred[6,4] 1.044 54
#> EM.pred[7,4] 1.097 26
#> EM.pred[8,4] 1.209 14
#> EM.pred[6,5] 1.042 72
#> EM.pred[7,5] 1.034 150
#> EM.pred[8,5] 1.016 270
#> EM.pred[7,6] 1.025 170
#> EM.pred[8,6] 1.075 34
#> EM.pred[8,7] 1.078 33
#> SUCRA[1] 1.181 16
#> SUCRA[2] 1.004 810
#> SUCRA[3] 1.001 3000
#> SUCRA[4] 1.136 21
#> SUCRA[5] 1.014 170
#> SUCRA[6] 1.009 690
#> SUCRA[7] 1.031 84
#> SUCRA[8] 1.062 46
#> abs_risk[1] 1.000 1
#> abs_risk[2] 1.017 240
#> abs_risk[3] 1.024 1600
#> abs_risk[4] 1.226 13
#> abs_risk[5] 1.012 2000
#> abs_risk[6] 1.066 35
#> abs_risk[7] 1.060 42
#> abs_risk[8] 1.031 180
#> beta[1] 1.000 1
#> beta[2] 1.104 310
#> beta[3] 1.041 610
#> beta[4] 1.094 28
#> beta[5] 1.168 19
#> beta[6] 1.050 98
#> beta[7] 1.004 810
#> beta[8] 1.116 24
#> beta.all[2,1] 1.104 310
#> beta.all[3,1] 1.041 610
#> beta.all[4,1] 1.094 28
#> beta.all[5,1] 1.168 19
#> beta.all[6,1] 1.050 98
#> beta.all[7,1] 1.004 810
#> beta.all[8,1] 1.116 24
#> beta.all[3,2] 1.064 960
#> beta.all[4,2] 1.096 470
#> beta.all[5,2] 1.149 40
#> beta.all[6,2] 1.064 3000
#> beta.all[7,2] 1.105 450
#> beta.all[8,2] 1.128 88
#> beta.all[4,3] 1.043 430
#> beta.all[5,3] 1.091 49
#> beta.all[6,3] 1.026 1100
#> beta.all[7,3] 1.041 1000
#> beta.all[8,3] 1.062 140
#> beta.all[5,4] 1.177 19
#> beta.all[6,4] 1.031 390
#> beta.all[7,4] 1.056 44
#> beta.all[8,4] 1.137 22
#> beta.all[6,5] 1.132 24
#> beta.all[7,5] 1.173 19
#> beta.all[8,5] 1.064 63
#> beta.all[7,6] 1.047 150
#> beta.all[8,6] 1.089 44
#> beta.all[8,7] 1.093 30
#> delta[1,1] 1.000 1
#> delta[2,1] 1.000 1
#> delta[3,1] 1.000 1
#> delta[4,1] 1.000 1
#> delta[5,1] 1.000 1
#> delta[6,1] 1.000 1
#> delta[7,1] 1.000 1
#> delta[8,1] 1.000 1
#> delta[9,1] 1.000 1
#> delta[10,1] 1.000 1
#> delta[11,1] 1.000 1
#> delta[12,1] 1.000 1
#> delta[13,1] 1.000 1
#> delta[14,1] 1.000 1
#> delta[15,1] 1.000 1
#> delta[16,1] 1.000 1
#> delta[17,1] 1.000 1
#> delta[18,1] 1.000 1
#> delta[19,1] 1.000 1
#> delta[20,1] 1.000 1
#> delta[21,1] 1.000 1
#> delta[1,2] 1.197 15
#> delta[2,2] 1.232 13
#> delta[3,2] 1.101 28
#> delta[4,2] 1.037 97
#> delta[5,2] 1.080 38
#> delta[6,2] 1.123 22
#> delta[7,2] 1.076 40
#> delta[8,2] 1.122 24
#> delta[9,2] 1.104 29
#> delta[10,2] 1.293 11
#> delta[11,2] 1.062 44
#> delta[12,2] 1.256 12
#> delta[13,2] 1.030 83
#> delta[14,2] 1.026 84
#> delta[15,2] 1.037 70
#> delta[16,2] 1.147 19
#> delta[17,2] 1.022 94
#> delta[18,2] 1.320 11
#> delta[19,2] 1.321 11
#> delta[20,2] 1.073 38
#> delta[21,2] 1.046 65
#> delta[9,3] 1.036 84
#> delta[10,3] 1.405 9
#> delta[12,3] 1.396 9
#> delta[13,3] 1.016 930
#> delta[19,3] 1.055 61
#> delta[10,4] 1.107 27
#> delta[12,4] 1.117 25
#> delta[13,4] 1.027 130
#> dev.o[1,1] 1.001 3000
#> dev.o[2,1] 1.007 420
#> dev.o[3,1] 1.002 1000
#> dev.o[4,1] 1.001 2600
#> dev.o[5,1] 1.006 380
#> dev.o[6,1] 1.002 1000
#> dev.o[7,1] 1.003 830
#> dev.o[8,1] 1.002 3000
#> dev.o[9,1] 1.002 2000
#> dev.o[10,1] 1.002 1000
#> dev.o[11,1] 1.004 540
#> dev.o[12,1] 1.008 310
#> dev.o[13,1] 1.026 88
#> dev.o[14,1] 1.002 1800
#> dev.o[15,1] 1.001 3000
#> dev.o[16,1] 1.002 2800
#> dev.o[17,1] 1.014 160
#> dev.o[18,1] 1.001 3000
#> dev.o[19,1] 1.001 2600
#> dev.o[20,1] 1.004 1300
#> dev.o[21,1] 1.012 200
#> dev.o[1,2] 1.003 960
#> dev.o[2,2] 1.001 3000
#> dev.o[3,2] 1.001 2700
#> dev.o[4,2] 1.003 710
#> dev.o[5,2] 1.002 2700
#> dev.o[6,2] 1.004 540
#> dev.o[7,2] 1.002 1300
#> dev.o[8,2] 1.001 2200
#> dev.o[9,2] 1.002 2500
#> dev.o[10,2] 1.001 3000
#> dev.o[11,2] 1.001 2100
#> dev.o[12,2] 1.005 500
#> dev.o[13,2] 1.002 1900
#> dev.o[14,2] 1.013 410
#> dev.o[15,2] 1.003 760
#> dev.o[16,2] 1.007 470
#> dev.o[17,2] 1.014 160
#> dev.o[18,2] 1.001 3000
#> dev.o[19,2] 1.001 3000
#> dev.o[20,2] 1.001 2600
#> dev.o[21,2] 1.008 270
#> dev.o[9,3] 1.007 380
#> dev.o[10,3] 1.010 260
#> dev.o[12,3] 1.005 470
#> dev.o[13,3] 1.002 2200
#> dev.o[19,3] 1.003 900
#> dev.o[10,4] 1.018 160
#> dev.o[12,4] 1.008 390
#> dev.o[13,4] 1.001 3000
#> effectiveness[1,1] 1.000 1
#> effectiveness[2,1] 1.005 480
#> effectiveness[3,1] 1.002 1000
#> effectiveness[4,1] 1.097 280
#> effectiveness[5,1] 1.004 800
#> effectiveness[6,1] 1.274 170
#> effectiveness[7,1] 1.104 83
#> effectiveness[8,1] 1.009 580
#> effectiveness[1,2] 1.193 510
#> effectiveness[2,2] 1.004 760
#> effectiveness[3,2] 1.001 3000
#> effectiveness[4,2] 1.117 150
#> effectiveness[5,2] 1.004 670
#> effectiveness[6,2] 1.063 670
#> effectiveness[7,2] 1.008 600
#> effectiveness[8,2] 1.007 400
#> effectiveness[1,3] 1.152 160
#> effectiveness[2,3] 1.011 460
#> effectiveness[3,3] 1.007 640
#> effectiveness[4,3] 1.163 49
#> effectiveness[5,3] 1.002 1500
#> effectiveness[6,3] 1.001 3000
#> effectiveness[7,3] 1.001 3000
#> effectiveness[8,3] 1.014 180
#> effectiveness[1,4] 1.062 96
#> effectiveness[2,4] 1.020 350
#> effectiveness[3,4] 1.001 3000
#> effectiveness[4,4] 1.063 100
#> effectiveness[5,4] 1.001 3000
#> effectiveness[6,4] 1.005 1800
#> effectiveness[7,4] 1.004 680
#> effectiveness[8,4] 1.001 3000
#> effectiveness[1,5] 1.036 87
#> effectiveness[2,5] 1.015 570
#> effectiveness[3,5] 1.001 3000
#> effectiveness[4,5] 1.033 180
#> effectiveness[5,5] 1.005 730
#> effectiveness[6,5] 1.018 300
#> effectiveness[7,5] 1.006 480
#> effectiveness[8,5] 1.038 130
#> effectiveness[1,6] 1.020 120
#> effectiveness[2,6] 1.029 340
#> effectiveness[3,6] 1.010 650
#> effectiveness[4,6] 1.003 1200
#> effectiveness[5,6] 1.015 440
#> effectiveness[6,6] 1.004 810
#> effectiveness[7,6] 1.003 1100
#> effectiveness[8,6] 1.078 140
#> effectiveness[1,7] 1.008 340
#> effectiveness[2,7] 1.007 2100
#> effectiveness[3,7] 1.014 560
#> effectiveness[4,7] 1.007 390
#> effectiveness[5,7] 1.009 1100
#> effectiveness[6,7] 1.003 710
#> effectiveness[7,7] 1.030 310
#> effectiveness[8,7] 1.215 78
#> effectiveness[1,8] 1.245 17
#> effectiveness[2,8] 1.039 320
#> effectiveness[3,8] 1.001 3000
#> effectiveness[4,8] 1.065 37
#> effectiveness[5,8] 1.053 390
#> effectiveness[6,8] 1.001 3000
#> effectiveness[7,8] 1.109 250
#> effectiveness[8,8] 1.291 3000
#> hat.par[1,1] 1.001 3000
#> hat.par[2,1] 1.007 360
#> hat.par[3,1] 1.009 250
#> hat.par[4,1] 1.028 83
#> hat.par[5,1] 1.013 320
#> hat.par[6,1] 1.007 300
#> hat.par[7,1] 1.027 80
#> hat.par[8,1] 1.020 100
#> hat.par[9,1] 1.002 2900
#> hat.par[10,1] 1.003 3000
#> hat.par[11,1] 1.005 620
#> hat.par[12,1] 1.016 140
#> hat.par[13,1] 1.053 43
#> hat.par[14,1] 1.009 250
#> hat.par[15,1] 1.027 82
#> hat.par[16,1] 1.007 330
#> hat.par[17,1] 1.019 110
#> hat.par[18,1] 1.001 3000
#> hat.par[19,1] 1.002 1000
#> hat.par[20,1] 1.002 3000
#> hat.par[21,1] 1.014 170
#> hat.par[1,2] 1.002 1000
#> hat.par[2,2] 1.004 640
#> hat.par[3,2] 1.002 1700
#> hat.par[4,2] 1.032 68
#> hat.par[5,2] 1.003 1600
#> hat.par[6,2] 1.007 300
#> hat.par[7,2] 1.004 600
#> hat.par[8,2] 1.010 220
#> hat.par[9,2] 1.013 170
#> hat.par[10,2] 1.002 1500
#> hat.par[11,2] 1.001 3000
#> hat.par[12,2] 1.021 100
#> hat.par[13,2] 1.020 110
#> hat.par[14,2] 1.025 84
#> hat.par[15,2] 1.020 110
#> hat.par[16,2] 1.007 330
#> hat.par[17,2] 1.021 99
#> hat.par[18,2] 1.002 1400
#> hat.par[19,2] 1.002 3000
#> hat.par[20,2] 1.011 200
#> hat.par[21,2] 1.012 180
#> hat.par[9,3] 1.022 97
#> hat.par[10,3] 1.024 95
#> hat.par[12,3] 1.010 220
#> hat.par[13,3] 1.012 180
#> hat.par[19,3] 1.004 550
#> hat.par[10,4] 1.019 110
#> hat.par[12,4] 1.010 220
#> hat.par[13,4] 1.005 480
#> phi[1] 1.219 15
#> phi[2] 1.046 50
#> phi[3] 1.025 85
#> phi[4] 1.082 32
#> phi[5] 1.102 26
#> phi[6] 1.006 3000
#> phi[7] 1.002 3000
#> phi[8] 1.009 340
#> tau 1.229 16
#> totresdev.o 1.010 240
#> deviance 1.004 620
#>
#> For each parameter, n.eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
#>
#> DIC info (using the rule: pV = var(deviance)/2)
#> pV = 90.8 and DIC = 673.2
#> DIC is an estimate of expected predictive error (lower deviance is better).
#>
#> $data
#> study t1 t2 t3 t4 r1 r2 r3 r4 m1 m2 m3 m4 n1 n2 n3 n4
#> 1 Llewellyn-Jones, 1996 1 4 NA NA 3 0 NA NA 1 0 NA NA 8 8 NA NA
#> 2 Paggiaro, 1998 1 4 NA NA 51 45 NA NA 27 19 NA NA 139 142 NA NA
#> 3 Mahler, 1999 1 7 NA NA 47 28 NA NA 23 9 NA NA 143 135 NA NA
#> 4 Casaburi, 2000 1 8 NA NA 41 45 NA NA 18 12 NA NA 191 279 NA NA
#> 5 van Noord, 2000 1 7 NA NA 18 11 NA NA 8 7 NA NA 50 47 NA NA
#> 6 Rennard, 2001 1 7 NA NA 41 38 NA NA 29 22 NA NA 135 132 NA NA
#> 7 Casaburi, 2002 1 8 NA NA 156 198 NA NA 77 66 NA NA 371 550 NA NA
#> 8 Chapman, 2002 1 7 NA NA 68 52 NA NA 28 20 NA NA 207 201 NA NA
#> 9 Donohue, 2002 1 7 8 NA 92 82 77 NA 37 20 10 NA 201 213 209 NA
#> 10 Mahler, 2002 1 4 7 5 79 77 63 68 69 68 45 52 181 168 160 165
#> 11 Rossi, 2002 1 6 NA NA 75 117 NA NA 59 92 NA NA 220 425 NA NA
#> 12 Hanania, 2003 1 4 7 5 73 79 65 71 59 49 57 53 185 183 177 178
#> 13 Szafranski, 2003 1 2 6 3 53 26 38 35 90 62 64 59 205 198 201 208
#> 14 Briggs, 2005 8 7 NA NA 30 36 NA NA 29 41 NA NA 328 325 NA NA
#> 15 Campbell, 2005 1 6 NA NA 34 35 NA NA 39 30 NA NA 217 215 NA NA
#> 16 Niewoehner, 2005 1 8 NA NA 296 255 NA NA 111 75 NA NA 915 914 NA NA
#> 17 van Noord, 2005 8 6 NA NA 4 14 NA NA 1 1 NA NA 70 69 NA NA
#> 18 Barnes, 2006 1 5 NA NA 24 11 NA NA 4 8 NA NA 73 67 NA NA
#> 19 O Donnell, 2006 1 7 5 NA 6 1 2 NA 5 1 3 NA 64 59 62 NA
#> 20 Baumgartner, 2007 1 7 NA NA 24 20 NA NA 32 26 NA NA 143 144 NA NA
#> 21 Freeman, 2007 1 8 NA NA 35 19 NA NA 33 18 NA NA 195 200 NA NA
#>
#> $n_chains
#> [1] 3
#>
#> $n_iter
#> [1] 1000
#>
#> $n_burnin
#> [1] 100
#>
#> $n_thin
#> [1] 1
#>
#> $abs_risk
#> mean sd 2.5% 25% 50% 75%
#> abs_risk[1] 0.3900000 0.00000000 0.39000000 0.3900000 0.3900000 0.3900000
#> abs_risk[2] 0.2666283 0.13342927 0.07788563 0.1810451 0.2515591 0.3186958
#> abs_risk[3] 0.3001786 0.12696525 0.10174625 0.2171406 0.2851022 0.3582583
#> abs_risk[4] 0.4331099 0.11763922 0.23265914 0.3526155 0.4226267 0.5027292
#> abs_risk[5] 0.3039669 0.07414167 0.16144830 0.2549247 0.3007873 0.3530916
#> abs_risk[6] 0.4386527 0.09825909 0.26819248 0.3683451 0.4342299 0.4988054
#> abs_risk[7] 0.3353066 0.05916891 0.22941732 0.2934488 0.3305075 0.3736425
#> abs_risk[8] 0.2987017 0.04474788 0.20554540 0.2698887 0.2984930 0.3285122
#> 97.5% Rhat n.eff
#> abs_risk[1] 0.3900000 1.000000 1
#> abs_risk[2] 0.6108405 1.017443 240
#> abs_risk[3] 0.6302444 1.024186 1600
#> abs_risk[4] 0.7016427 1.225811 13
#> abs_risk[5] 0.4573047 1.012389 2000
#> abs_risk[6] 0.6558503 1.066362 35
#> abs_risk[7] 0.4625082 1.060376 42
#> abs_risk[8] 0.3833826 1.031128 180
#>
#> $SUCRA
#> mean sd 2.5% 25% 50% 75% 97.5%
#> SUCRA[1] 0.2715238 0.1810778 0.0000000 0.1428571 0.2857143 0.4285714 0.5714286
#> SUCRA[2] 0.7679048 0.2902673 0.0000000 0.5714286 0.8571429 1.0000000 1.0000000
#> SUCRA[3] 0.6690000 0.3099730 0.0000000 0.4285714 0.7142857 0.8571429 1.0000000
#> SUCRA[4] 0.2367619 0.2602858 0.0000000 0.0000000 0.1428571 0.4285714 0.8571429
#> SUCRA[5] 0.6427619 0.2606288 0.1428571 0.4285714 0.7142857 0.8571429 1.0000000
#> SUCRA[6] 0.1949524 0.2118473 0.0000000 0.0000000 0.1428571 0.2857143 0.7142857
#> SUCRA[7] 0.5410952 0.2335822 0.1428571 0.4285714 0.5714286 0.7142857 1.0000000
#> SUCRA[8] 0.6760000 0.2028126 0.2857143 0.5714286 0.7142857 0.8571429 1.0000000
#> Rhat n.eff
#> SUCRA[1] 1.181145 16
#> SUCRA[2] 1.003919 810
#> SUCRA[3] 1.000636 3000
#> SUCRA[4] 1.135873 21
#> SUCRA[5] 1.013855 170
#> SUCRA[6] 1.009218 690
#> SUCRA[7] 1.031292 84
#> SUCRA[8] 1.062481 46
#>
#> $effectiveness
#> mean sd 2.5% 25% 50% 75% 97.5% Rhat
#> effectiveness[1,1] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> effectiveness[2,1] 0.4343333333 0.49575176 0 0 0 1 1 1.004667
#> effectiveness[3,1] 0.2400000000 0.42715433 0 0 0 0 1 1.002420
#> effectiveness[4,1] 0.0163333333 0.12677505 0 0 0 0 0 1.097230
#> effectiveness[5,1] 0.1480000000 0.35515918 0 0 0 0 1 1.004405
#> effectiveness[6,1] 0.0066666667 0.08139060 0 0 0 0 0 1.273690
#> effectiveness[7,1] 0.0570000000 0.23188127 0 0 0 0 1 1.103642
#> effectiveness[8,1] 0.0976666667 0.29691291 0 0 0 0 1 1.009192
#> effectiveness[1,2] 0.0036666667 0.06045197 0 0 0 0 0 1.193131
#> effectiveness[2,2] 0.1963333333 0.39728978 0 0 0 0 1 1.003691
#> effectiveness[3,2] 0.2496666667 0.43289224 0 0 0 0 1 1.001176
#> effectiveness[4,2] 0.0260000000 0.15916169 0 0 0 0 1 1.116769
#> effectiveness[5,2] 0.1860000000 0.38917154 0 0 0 0 1 1.004452
#> effectiveness[6,2] 0.0110000000 0.10431983 0 0 0 0 0 1.062585
#> effectiveness[7,2] 0.1040000000 0.30531143 0 0 0 0 1 1.008436
#> effectiveness[8,2] 0.2233333333 0.41654939 0 0 0 0 1 1.006706
#> effectiveness[1,3] 0.0166666667 0.12804044 0 0 0 0 0 1.152111
#> effectiveness[2,3] 0.1080000000 0.31043215 0 0 0 0 1 1.010582
#> effectiveness[3,3] 0.1203333333 0.32540516 0 0 0 0 1 1.006907
#> effectiveness[4,3] 0.0593333333 0.23628690 0 0 0 0 1 1.162847
#> effectiveness[5,3] 0.2240000000 0.41699156 0 0 0 0 1 1.001838
#> effectiveness[6,3] 0.0253333333 0.15716166 0 0 0 0 1 1.001227
#> effectiveness[7,3] 0.1773333333 0.38201422 0 0 0 0 1 1.000838
#> effectiveness[8,3] 0.2690000000 0.44351389 0 0 0 1 1 1.013776
#> effectiveness[1,4] 0.0883333333 0.28382637 0 0 0 0 1 1.062438
#> effectiveness[2,4] 0.0736666667 0.26127121 0 0 0 0 1 1.020001
#> effectiveness[3,4] 0.1133333333 0.31705267 0 0 0 0 1 1.000958
#> effectiveness[4,4] 0.0803333333 0.27185386 0 0 0 0 1 1.062766
#> effectiveness[5,4] 0.1513333333 0.35843323 0 0 0 0 1 1.000801
#> effectiveness[6,4] 0.0563333333 0.23060272 0 0 0 0 1 1.004798
#> effectiveness[7,4] 0.2110000000 0.40808640 0 0 0 0 1 1.003917
#> effectiveness[8,4] 0.2256666667 0.41809029 0 0 0 0 1 1.001018
#> effectiveness[1,5] 0.2046666667 0.40352509 0 0 0 0 1 1.035790
#> effectiveness[2,5] 0.0610000000 0.23937021 0 0 0 0 1 1.014633
#> effectiveness[3,5] 0.0776666667 0.26769094 0 0 0 0 1 1.000520
#> effectiveness[4,5] 0.0870000000 0.28188204 0 0 0 0 1 1.032810
#> effectiveness[5,5] 0.1363333333 0.34319938 0 0 0 0 1 1.005292
#> effectiveness[6,5] 0.0980000000 0.29736421 0 0 0 0 1 1.018037
#> effectiveness[7,5] 0.2196666667 0.41408982 0 0 0 0 1 1.005574
#> effectiveness[8,5] 0.1156666667 0.31987810 0 0 0 0 1 1.037839
#> effectiveness[1,6] 0.2933333333 0.45536580 0 0 0 1 1 1.019695
#> effectiveness[2,6] 0.0520000000 0.22206404 0 0 0 0 1 1.028817
#> effectiveness[3,6] 0.0756666667 0.26450812 0 0 0 0 1 1.010434
#> effectiveness[4,6] 0.1346666667 0.34142409 0 0 0 0 1 1.003115
#> effectiveness[5,6] 0.0810000000 0.27288060 0 0 0 0 1 1.014560
#> effectiveness[6,6] 0.1576666667 0.36448892 0 0 0 0 1 1.004143
#> effectiveness[7,6] 0.1603333333 0.36697608 0 0 0 0 1 1.002809
#> effectiveness[8,6] 0.0453333333 0.20806887 0 0 0 0 1 1.077702
#> effectiveness[1,7] 0.2413333333 0.42796332 0 0 0 0 1 1.007521
#> effectiveness[2,7] 0.0353333333 0.18465171 0 0 0 0 1 1.006510
#> effectiveness[3,7] 0.0656666667 0.24773981 0 0 0 0 1 1.013993
#> effectiveness[4,7] 0.2386666667 0.42633963 0 0 0 0 1 1.006630
#> effectiveness[5,7] 0.0510000000 0.22003440 0 0 0 0 1 1.008619
#> effectiveness[6,7] 0.2906666667 0.45414569 0 0 0 1 1 1.003317
#> effectiveness[7,7] 0.0543333333 0.22671205 0 0 0 0 1 1.030475
#> effectiveness[8,7] 0.0230000000 0.14992829 0 0 0 0 0 1.215066
#> effectiveness[1,8] 0.1520000000 0.35908074 0 0 0 0 1 1.244922
#> effectiveness[2,8] 0.0393333333 0.19441919 0 0 0 0 1 1.039022
#> effectiveness[3,8] 0.0576666667 0.23315090 0 0 0 0 1 1.000928
#> effectiveness[4,8] 0.3576666667 0.47939319 0 0 0 1 1 1.065438
#> effectiveness[5,8] 0.0223333333 0.14778984 0 0 0 0 0 1.053418
#> effectiveness[6,8] 0.3543333333 0.47839054 0 0 0 1 1 1.000820
#> effectiveness[7,8] 0.0163333333 0.12677505 0 0 0 0 0 1.108675
#> effectiveness[8,8] 0.0003333333 0.01825742 0 0 0 0 0 1.290904
#> n.eff
#> effectiveness[1,1] 1
#> effectiveness[2,1] 480
#> effectiveness[3,1] 1000
#> effectiveness[4,1] 280
#> effectiveness[5,1] 800
#> effectiveness[6,1] 170
#> effectiveness[7,1] 83
#> effectiveness[8,1] 580
#> effectiveness[1,2] 510
#> effectiveness[2,2] 760
#> effectiveness[3,2] 3000
#> effectiveness[4,2] 150
#> effectiveness[5,2] 670
#> effectiveness[6,2] 670
#> effectiveness[7,2] 600
#> effectiveness[8,2] 400
#> effectiveness[1,3] 160
#> effectiveness[2,3] 460
#> effectiveness[3,3] 640
#> effectiveness[4,3] 49
#> effectiveness[5,3] 1500
#> effectiveness[6,3] 3000
#> effectiveness[7,3] 3000
#> effectiveness[8,3] 180
#> effectiveness[1,4] 96
#> effectiveness[2,4] 350
#> effectiveness[3,4] 3000
#> effectiveness[4,4] 100
#> effectiveness[5,4] 3000
#> effectiveness[6,4] 1800
#> effectiveness[7,4] 680
#> effectiveness[8,4] 3000
#> effectiveness[1,5] 87
#> effectiveness[2,5] 570
#> effectiveness[3,5] 3000
#> effectiveness[4,5] 180
#> effectiveness[5,5] 730
#> effectiveness[6,5] 300
#> effectiveness[7,5] 480
#> effectiveness[8,5] 130
#> effectiveness[1,6] 120
#> effectiveness[2,6] 340
#> effectiveness[3,6] 650
#> effectiveness[4,6] 1200
#> effectiveness[5,6] 440
#> effectiveness[6,6] 810
#> effectiveness[7,6] 1100
#> effectiveness[8,6] 140
#> effectiveness[1,7] 340
#> effectiveness[2,7] 2100
#> effectiveness[3,7] 560
#> effectiveness[4,7] 390
#> effectiveness[5,7] 1100
#> effectiveness[6,7] 710
#> effectiveness[7,7] 310
#> effectiveness[8,7] 78
#> effectiveness[1,8] 17
#> effectiveness[2,8] 320
#> effectiveness[3,8] 3000
#> effectiveness[4,8] 37
#> effectiveness[5,8] 390
#> effectiveness[6,8] 3000
#> effectiveness[7,8] 250
#> effectiveness[8,8] 3000
#>
#> $D
#> [1] 0
#>
#> attr(,"class")
#> [1] "run_metareg"
# }