Performs a one-stage pairwise or network meta-analysis while addressing aggregate binary or continuous missing participant outcome data via the pattern-mixture model.
Usage
run_model(
data,
measure,
model,
assumption,
heter_prior,
mean_misspar,
var_misspar,
D,
ref,
base_risk,
n_chains,
n_iter,
n_burnin,
n_thin,
inits = NULL,
adjust_wgt = NULL
)Format
The columns of the data-frame in the argument data refer
to the following elements for a continuous outcome:
| t | An intervention identifier in each arm. |
| y | The observed mean value of the outcome in each arm. |
| sd | The observed standard deviation of the outcome in each arm. |
| m | The number of missing participant outcome data in each arm. |
| n | The number of randomised participants in each arm. |
For a binary outcome, the columns of the data-frame in the argument
data refer to the following elements:
| t | An intervention identifier in each arm. |
| r | The observed number of events of the outcome in each arm. |
| m | The number of missing participant outcome data in each arm. |
| n | The number of randomised participants in each arm. |
The number of rows in data equals the number of collected trials.
Each element appears in data as many times as the maximum number of
interventions compared in a trial of the dataset.
In pairwise meta-analysis, the maximum number of arms is inherently two.
The same holds for a network meta-analysis without multi-arm trials.
In the case of network meta-analysis with multi-arm trials, the maximum
number of arms exceeds two. See 'Examples' that illustrates the structure
of data for a network with a maximum number of four arms.
It is not a prerequisite of run_model that the multi-arm trials
appear at the bottom of the dataset.
Arguments
- data
A data-frame of the one-trial-per-row format with arm-level data. See 'Format' for the specification of the columns.
- measure
Character string indicating the effect measure. For a binary outcome, the following can be considered:
"OR","RR"or"RD"for the odds ratio, relative risk, and risk difference, respectively. For a continuous outcome, the following can be considered:"MD","SMD", or"ROM"for mean difference, standardised mean difference and ratio of means, respectively.- model
Character string indicating the analysis model with values
"RE", or"FE"for the random-effects and fixed-effect model, respectively. The default argument is"RE".- assumption
Character string indicating the structure of the informative missingness parameter. Set
assumptionequal to one of the following:"HIE-COMMON","HIE-TRIAL","HIE-ARM","IDE-COMMON","IDE-TRIAL","IDE-ARM","IND-CORR", or"IND-UNCORR". The default argument is"IDE-ARM". The abbreviations"IDE","HIE", and"IND"stand for identical, hierarchical and independent, respectively."CORR"and"UNCORR"stand for correlated and uncorrelated, respectively.- heter_prior
A list of three elements with the following order: 1) a character string indicating the distribution with (currently available) values
"halfnormal","uniform","lognormal", or"logt"; 2) two numeric values that refer to the parameters of the selected distribution. For"lognormal", and"logt"these numbers refer to the mean and precision, respectively. For"halfnormal", these numbers refer to zero and the scale parameter (equal to 4 or 1 being the corresponding precision of the scale parameter 0.5 or 1). For"uniform", these numbers refer to the minimum and maximum value of the distribution. See 'Details' inheterogeneity_param_prior.- mean_misspar
A scalar or numeric vector of two numeric values for the mean of the normal distribution of the informative missingness parameter (see 'Details'). The default argument is 0 and corresponds to the missing-at-random assumption. See also 'Details' in
missingness_param_prior.- var_misspar
A positive non-zero number for the variance of the normal distribution of the informative missingness parameter. When the
measureis"OR","MD", or"SMD"the default argument is 1. When themeasureis"ROM"the default argument is 0.04.- D
A binary number for the direction of the outcome. Set
D = 1for beneficial outcome andD = 0for harmful outcome.- ref
An integer specifying the reference intervention. The number should match the intervention identifier under element t in
data(See 'Format').- base_risk
A scalar, a vector of length three with elements sorted in ascending order, or a matrix with two columns and number of rows equal to the number of relevant trials. In the case of a scalar or vector, the elements should be in the interval (0, 1) (see 'Details'). If
base_riskhas not been defined, the function uses the median event risk for the reference intervention from the corresponding trials indata. This argument is only relevant for a binary outcome.- 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.- adjust_wgt
A positive numeric vector with length equal to the number of studies in the network, or a positive numeric matrix with two columns and number of rows equal to the number of studies in the network. The elements comprise study-specific weights. This argument is optional. See 'Details'.
Value
A list of R2jags output on the summaries of the posterior distribution, and the Gelman-Rubin convergence diagnostic (Gelman et al., 1992) of the following monitored parameters for a fixed-effect pairwise meta-analysis:
- EM
The estimated summary effect measure (according to the argument
measure).- EM_LOR
The estimated summary odd ratio in the logarithmic scale when
measure = "RR"ormeasure = "RD".- 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 curve for each intervention.
- SUCRA_LOR
The surface under the cumulative ranking curve for each intervention under the odds ratio effect measure when
measure = "RR"ormeasure = "RD".- 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
measure).- EM_pred_LOR
The predicted summary odds ratio in the logarithmic scale when
measure = "RR"ormeasure = "RD".- delta
The estimated trial-specific effect measure (according to the argument
measure).- 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 of T-1
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:
- 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.
- indic
The sign of basic parameters in relation to the reference intervention as specified in argument
reg- jagsfit
An object of S3 class
jagswith the posterior results on all monitored parameters to be used in themcmc_diagnosticsfunction.
The run_model function also returns the arguments data,
measure, model, assumption, heter_prior,
mean_misspar, var_misspar, D, ref,
base_risk, n_chains, n_iter, n_burnin,
and n_thin as specified by the user to be inherited by other
functions of the package.
Details
The model runs in JAGS and the progress of the simulation
appears on the R console. The output of run_model is used as an S3
object by other functions of the package to be processed further and
provide an end-user-ready output.
The data_preparation function is called to prepare the data
for the Bayesian analysis. data_preparation creates the
pseudo-data-frames m_new, and I, that have the same
dimensions with the element N. m_new takes the zero
value for the observed trial-arms with unreported missing participant
outcome data (i.e., m equals NA for the corresponding
trial-arms), the same value with m for the observed trial-arms with
reported missing participant outcome data, and NA for the unobserved
trial-arms. I is a dummy pseudo-data-frame and takes the value one
for the observed trial-arms with reported missing participant outcome data,
the zero value for the observed trial-arms with unreported missing
participant outcome data (i.e., m_new equals zero for the
corresponding trial-arms), and NA for the unobserved trial-arms.
Thus, I indicates whether missing participant outcome data have been
collected for the observed trial-arms. If the user has not defined the
element m in data, m_new and I take the zero
value for all observed trial-arms to indicate that no missing participant
outcome data have been collected for the analysed outcome. See 'Details' in
data_preparation.
Furthermore, data_preparation sorts the interventions across
the arms of each trial in an ascending order and correspondingly the
remaining elements in data (see 'Format').
data_preparation considers the first column in t as
being the control arm for every trial. Thus, this sorting ensures that
interventions with a lower identifier are consistently treated as the
control arm in each trial. This case is relevant in non-star-shaped
networks.
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.
To perform a Bayesian pairwise or network meta-analysis, the
prepare_model function is called which contains the WinBUGS
code as written by Dias et al. (2013a) for binomial and normal likelihood to
analyse aggregate binary and continuous outcome data, respectively.
prepare_model uses the consistency model (as described in
Lu and Ades (2006)) to estimate all possible comparisons in the network.
It also accounts for the multi-arm trials by assigning conditional
univariate normal distributions on the underlying trial-specific effect
size of comparisons with the baseline arm of the multi-arm trial
(Dias et al., 2013a).
The code of Dias et al. (2013a) has been extended to incorporate the
pattern-mixture model to adjust the underlying outcome in each arm of
every trial for missing participant outcome data (Spineli et al., 2021;
Spineli, 2019a; Turner et al., 2015). The assumptions about the
missingness parameter are specified using the arguments mean_misspar
and var_misspar. Specifically, run_model considers the
informative missingness odds ratio in the logarithmic scale for binary
outcome data (Spineli, 2019a; Turner et al., 2015; White et al., 2008), the
informative missingness difference of means when measure is
"MD" or "SMD", and the informative missingness ratio of means
in the logarithmic scale when measure is "ROM"
(Spineli et al., 2021; Mavridis et al., 2015).
When assumption is trial-specific (i.e., "IDE-TRIAL" or
"HIE-TRIAL"), or independent (i.e., "IND-CORR" or
"IND-UNCORR"), only one numeric value can be assigned to
mean_misspar because the same missingness scenario is applied to all
trials and trial-arms of the dataset, respectively. When assumption
is "IDE-ARM" or "HIE-ARM", a maximum of two
different or identical numeric values can be assigned as a
vector to mean_misspars: the first value refers to the experimental
arm, and the second value refers to the control arm of a trial.
In the case of a network, the first value is considered for all
non-reference interventions and the second value is considered for the
reference intervention of the network (i.e., the intervention with
identifier equal to ref). This is necessary to ensure transitivity
in the assumptions for the missingness parameter across the network
(Spineli, 2019b).
When there is at least one trial-arm with unreported missing participant
outcome data (i.e., m equals NA for the corresponding
trial-arms) or when missing participant outcome data have not been
collected for the analysed outcome (i.e., m is missing in
data), run_model assigns the assumption "IND-UNCORR"
to assumption.
Currently, there are no empirically-based prior distributions for the
informative missingness parameters. The user may refer to Spineli (2019),
Turner et al. (2015), Mavridis et al. (2015), and White et al. (2008) to
determine mean_misspar and select a proper value for
var_misspar.
The scalar base_risk refers to a fixed baseline risk for the
selected reference intervention (as specified with ref).
When base_risk is a three-element vector, it refers to a random
baseline risk and the elements should be sorted in ascending order as they
refer to the lower bound, mean value, and upper bound of the 95%
confidence interval for the baseline risk for the selected reference
intervention. The baseline_model function is called to
calculate the mean and variance of the approximately normal distribution of
the logit of an event for ref using these three elements
(Dias et al., 2018). When base_risk is a matrix, it refers to the
predicted baseline risk with first column being the number of events, and
second column being the sample size of the corresponding trials on the
selected reference intervention. Then the baseline_model
function is called that contains the WinBUGS code as written by Dias et al.
(2013b) for the hierarchical baseline model. The posterior mean and
precision of the predictive distribution of the logit of an event
for the selected reference intervention are plugged in the WinBUGS code for
the relative effects model (via the prepare_model function).
The matrix base_risk should not comprise the trials in data
that include the ref, unless justified (Dias et al., 2018).
To obtain unique absolute risks for each intervention, the network
meta-analysis model has been extended to incorporate the transitive risks
framework, namely, an intervention has the same absolute risk regardless of
the comparator intervention(s) in a trial (Spineli et al., 2017).
The absolute risks are a function of the odds ratio (the base-case
effect measure for a binary outcome) and the selected baseline risk for the
reference intervention (ref) (Appendix in Dias et al., 2013a).
We advocate using the odds ratio as an effect measure for its desired
mathematical properties. Then, the relative risk and risk difference can be
obtained as a function of the absolute risks of the corresponding
interventions in the comparison of interest. Hence, regardless of the
selected measure for a binary outcome, run_model performs
pairwise or network meta-analysis based on the odds ratio.
When adjust_wgt is defined, run_model gives less weight to
studies with smaller values, and more weight to studies with larger values.
Specifically, the model weight the (contribution of the) studies by
inflating the between-study variance of the underlying treatment effects of
the studies (Proctor et al., 2022). This approach is only relevant for a
random-effect model (model = "RE"). When adjust_wgt is
specified as a matrix, the columns pertain to the bounds of the uniform
distribution. Then, for each study, prepare_model samples the
weights from the corresponding uniform distribution. This is similar to the
enrichment-through-weighting approach implemented by Proctor et al. (2022).
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
Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network Meta-Analysis for Decision Making. Chichester (UK): Wiley; 2018.
Dias S, Sutton AJ, Ades AE, Welton NJ. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making 2013a;33(5):607–17. doi: 10.1177/0272989X12458724
Dias S, Welton NJ, Sutton AJ, Ades AE. Evidence synthesis for decision making 5: the baseline natural history model. Med Decis Making 2013b;33(5):657–70. doi: 10.1177/0272989X13485155
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7(4):457–72. doi: 10.1214/ss/1177011136
Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc 2006;101:447–59. doi: 10.1198/016214505000001302
Mavridis D, White IR, Higgins JP, Cipriani A, Salanti G. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Stat Med 2015;34(5):721–41. doi: 10.1002/sim.6365
Proctor T, Zimmermann S, Seide S, Kieser M. A comparison of methods for enriching network meta-analyses in the absence of individual patient data. Res Synth Methods 2022;13(6):745–759. doi: 10.1002/jrsm.1568.
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 2019a;19(1):86. doi: 10.1186/s12874-019-0731-y
Spineli LM. Modeling missing binary outcome data while preserving transitivity assumption yielded more credible network meta-analysis results. J Clin Epidemiol 2019b;105:19–26. doi: 10.1016/j.jclinepi.2018.09.002
Spineli LM, Brignardello-Petersen R, Heen AF, Achille F, Brandt L, Guyatt GH, et al. Obtaining absolute effect estimates to facilitate shared decision making in the context of multiple-treatment comparisons. Abstracts of the Global Evidence Summit, Cape Town, South Africa. Cochrane Database of Systematic Reviews 2017;9(Suppl 1):18911.
Turner NL, Dias S, Ades AE, Welton NJ. A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis. Stat Med 2015;34(12):2062–80. doi: 10.1002/sim.6475
White IR, Higgins JP, Wood AM. Allowing for uncertainty due to missing data in meta-analysis–part 1: two-stage methods. Stat Med 2008;27(5):711–27. doi: 10.1002/sim.3008
Examples
data("nma.baker2009")
# Show the first six trials of the dataset
head(nma.baker2009)
#> 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
# \donttest{
# Perform a random-effects network meta-analysis
# Note: Ideally, set 'n_iter' to 10000 and 'n_burnin' to 1000
run_model(data = nma.baker2009,
measure = "OR",
model = "RE",
assumption = "IDE-ARM",
heter_prior = list("halfnormal", 0, 1),
mean_misspar = c(0, 0),
var_misspar = 1,
D = 0,
ref = 1,
n_chains = 3,
n_iter = 1000,
n_burnin = 100,
n_thin = 1)
#> JAGS generates initial values for the parameters.
#> Running the model ...
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 100
#> Unobserved stochastic nodes: 148
#> Total graph size: 2620
#>
#> Initializing model
#>
#> ... Updating the model until convergence
#> $EM
#> mean sd 2.5% 25% 50%
#> EM[2,1] -0.94318940 0.4282660 -1.769702800 -1.22834512 -0.953791702
#> EM[3,1] -0.66085480 0.4328575 -1.576573840 -0.93880826 -0.640237486
#> EM[4,1] -0.24350971 0.3247757 -0.797694421 -0.47632380 -0.274695102
#> EM[5,1] -0.51256592 0.2826923 -1.051798760 -0.68606275 -0.534742892
#> EM[6,1] -0.02304669 0.2478971 -0.551750217 -0.19005646 -0.001313911
#> EM[7,1] -0.46436918 0.1818440 -0.795934462 -0.59480594 -0.476224482
#> EM[8,1] -0.53037100 0.1636677 -0.856308808 -0.63603541 -0.528799981
#> EM[3,2] 0.28233460 0.4849089 -0.663818824 -0.05428280 0.281364826
#> EM[4,2] 0.69967969 0.4619547 -0.238505246 0.39258492 0.674835766
#> EM[5,2] 0.43062348 0.4715143 -0.545419238 0.12472308 0.421168841
#> EM[6,2] 0.92014271 0.4492652 -0.009598997 0.64503412 0.938670730
#> EM[7,2] 0.47882022 0.4356892 -0.368431883 0.19361109 0.473565821
#> EM[8,2] 0.41281840 0.4327773 -0.479984957 0.14080569 0.430954138
#> EM[4,3] 0.41734508 0.4636523 -0.470050703 0.11833755 0.402650544
#> EM[5,3] 0.14828888 0.4728140 -0.802514698 -0.16448440 0.149472997
#> EM[6,3] 0.63780811 0.4404039 -0.243781448 0.33197830 0.662458779
#> EM[7,3] 0.19648562 0.4110816 -0.582797497 -0.07387556 0.212225632
#> EM[8,3] 0.13048380 0.4197854 -0.674443022 -0.14976333 0.141303994
#> EM[5,4] -0.26905621 0.3767213 -0.999425235 -0.52295141 -0.270627908
#> EM[6,4] 0.22046302 0.3501291 -0.463590641 -0.01604658 0.228846331
#> EM[7,4] -0.22085947 0.2992480 -0.845049071 -0.41448047 -0.202897004
#> EM[8,4] -0.28686128 0.3202874 -0.980823076 -0.49363073 -0.258193562
#> EM[6,5] 0.48951923 0.3349909 -0.242348071 0.28730669 0.506081192
#> EM[7,5] 0.04819674 0.2954861 -0.567812732 -0.14735367 0.045475539
#> EM[8,5] -0.01780508 0.2889111 -0.630461915 -0.19007078 -0.007521709
#> EM[7,6] -0.44132249 0.2398004 -0.886279100 -0.61049440 -0.448113701
#> EM[8,6] -0.50732431 0.2300243 -0.960203129 -0.65419406 -0.512675598
#> EM[8,7] -0.06600182 0.1640566 -0.387078024 -0.17429735 -0.059609984
#> 75% 97.5% Rhat n.eff
#> EM[2,1] -0.671779167 -0.04982525 1.084250 35
#> EM[3,1] -0.375844443 0.13231876 1.063323 52
#> EM[4,1] -0.021187119 0.43804638 1.129526 21
#> EM[5,1] -0.347135242 0.12999656 1.024410 770
#> EM[6,1] 0.165575353 0.39681644 1.131188 23
#> EM[7,1] -0.341474801 -0.08728508 1.039033 120
#> EM[8,1] -0.413478109 -0.21821686 1.023187 350
#> EM[3,2] 0.630363955 1.22180562 1.007434 1300
#> EM[4,2] 1.018310893 1.63247671 1.020274 210
#> EM[5,2] 0.725790286 1.37586034 1.041768 57
#> EM[6,2] 1.220188336 1.73286764 1.048786 58
#> EM[7,2] 0.764643099 1.31379879 1.050465 59
#> EM[8,2] 0.717396997 1.19928243 1.077844 35
#> EM[4,3] 0.693606746 1.39201252 1.018766 290
#> EM[5,3] 0.465053427 1.11915730 1.027751 89
#> EM[6,3] 0.926396441 1.53921184 1.045761 66
#> EM[7,3] 0.463536586 1.01641478 1.037873 87
#> EM[8,3] 0.408280691 0.97618088 1.055155 46
#> EM[5,4] -0.016146626 0.48390863 1.076418 36
#> EM[6,4] 0.467087647 0.82565672 1.135701 20
#> EM[7,4] 0.001926603 0.31349688 1.091873 27
#> EM[8,4] -0.043369786 0.24981603 1.148424 18
#> EM[6,5] 0.717485525 1.11592045 1.046171 50
#> EM[7,5] 0.238850130 0.59664954 1.009634 2000
#> EM[8,5] 0.178830917 0.50179916 1.006052 420
#> EM[7,6] -0.284243842 0.04705296 1.087498 31
#> EM[8,6] -0.352867888 -0.05126907 1.107591 25
#> EM[8,7] 0.046000012 0.24377872 1.030018 81
#>
#> $dev_o
#> mean sd 2.5% 25% 50% 75%
#> dev.o[1,1] 2.3781592 2.3362406 0.0074928088 0.59623249 1.7125559 3.4186954
#> dev.o[2,1] 0.9677025 1.3462197 0.0010395001 0.10086898 0.4477280 1.2801405
#> dev.o[3,1] 1.1863702 1.5443602 0.0018089309 0.13856063 0.6186585 1.6904974
#> dev.o[4,1] 0.7162283 1.0033377 0.0005864476 0.07738118 0.3260675 0.8990377
#> dev.o[5,1] 0.7391687 1.0215510 0.0009481909 0.08006467 0.3408617 1.0013088
#> dev.o[6,1] 0.9364762 1.2742631 0.0009845114 0.10955964 0.4685048 1.2741555
#> dev.o[7,1] 0.8067370 1.1361442 0.0006366889 0.07994412 0.3810305 1.0738986
#> dev.o[8,1] 0.7356023 1.0821420 0.0007070854 0.07072792 0.3215362 0.9401305
#> dev.o[9,1] 0.7416247 1.0841233 0.0005288078 0.06913609 0.3351249 0.9530078
#> dev.o[10,1] 0.6170400 0.8379483 0.0007142445 0.06873267 0.2945055 0.8323558
#> dev.o[11,1] 0.9792968 1.3215701 0.0013909247 0.10555992 0.4396493 1.3354563
#> dev.o[12,1] 1.2912889 1.5880889 0.0018016100 0.18507071 0.7142733 1.8307768
#> dev.o[13,1] 1.2898297 1.6643804 0.0018884285 0.14097851 0.6510724 1.7938726
#> dev.o[14,1] 0.9132117 1.2461620 0.0010651947 0.10202609 0.4228366 1.2259668
#> dev.o[15,1] 0.8461846 1.1264491 0.0005517014 0.08526399 0.4082004 1.1608607
#> dev.o[16,1] 1.2062596 1.6323540 0.0007069609 0.12275116 0.5572995 1.6330766
#> dev.o[17,1] 1.6418756 1.9446937 0.0030855801 0.22264217 0.9109586 2.4176909
#> dev.o[18,1] 0.9971523 1.4219907 0.0008607413 0.09865149 0.4512387 1.3587211
#> dev.o[19,1] 1.6197726 1.8031357 0.0036483993 0.27063590 1.0220878 2.3785075
#> dev.o[20,1] 0.7505731 1.0928379 0.0006211646 0.07353664 0.3472989 0.9853409
#> dev.o[21,1] 1.0763259 1.4280354 0.0011449648 0.11099901 0.5315871 1.4644610
#> dev.o[1,2] 3.2528238 1.9464467 0.6482235388 1.83865566 2.8756566 4.2717228
#> dev.o[2,2] 0.9695222 1.3081284 0.0006360070 0.09630903 0.4848161 1.3591022
#> dev.o[3,2] 1.0463302 1.3474659 0.0008398151 0.11592869 0.5246374 1.4263828
#> dev.o[4,2] 0.8286589 1.2102489 0.0007865829 0.07227079 0.3619318 1.0909261
#> dev.o[5,2] 0.6089100 0.8868392 0.0005635100 0.06223971 0.2703683 0.8000208
#> dev.o[6,2] 1.0055967 1.3362468 0.0012653573 0.10846500 0.4934576 1.3917448
#> dev.o[7,2] 0.8651850 1.1861516 0.0006449749 0.08699506 0.4236512 1.1507982
#> dev.o[8,2] 0.7133597 1.0124883 0.0004214583 0.07462075 0.3091599 0.9719862
#> dev.o[9,2] 0.6992260 1.0401658 0.0006728956 0.07500464 0.3209577 0.8692467
#> dev.o[10,2] 1.5749895 1.8691974 0.0027171419 0.22142505 0.9032006 2.2526562
#> dev.o[11,2] 0.9784296 1.4006716 0.0012880248 0.10098823 0.4518714 1.2811199
#> dev.o[12,2] 0.8178376 1.1126811 0.0007984376 0.09152524 0.3739715 1.0748010
#> dev.o[13,2] 0.9790166 1.3837251 0.0009325170 0.10405034 0.4375725 1.2925432
#> dev.o[14,2] 0.7602221 1.0879182 0.0008098462 0.07728314 0.3335235 0.9923854
#> dev.o[15,2] 0.9487169 1.2390592 0.0011571847 0.10707651 0.4807316 1.3152069
#> dev.o[16,2] 1.2072451 1.7096792 0.0011490034 0.12533895 0.5605912 1.6005865
#> dev.o[17,2] 1.9280589 1.7895219 0.0096300314 0.56522386 1.4477336 2.8198032
#> dev.o[18,2] 0.9107474 1.2662133 0.0011051204 0.10066204 0.4372561 1.2216145
#> dev.o[19,2] 0.3964745 0.5864798 0.0004450458 0.03959436 0.1746349 0.5064449
#> dev.o[20,2] 0.7371697 1.0015309 0.0009654283 0.07375008 0.3498350 0.9995161
#> dev.o[21,2] 0.9416026 1.1985451 0.0012017964 0.12145811 0.4855115 1.2913916
#> dev.o[9,3] 0.7546427 1.0339791 0.0005993761 0.07772240 0.3533254 1.0514297
#> dev.o[10,3] 0.7055757 1.0015327 0.0007278120 0.07020217 0.3328251 0.9182303
#> dev.o[12,3] 1.1922746 1.5964033 0.0017952114 0.13476229 0.5787751 1.6393526
#> dev.o[13,3] 1.0430266 1.4990127 0.0010257036 0.10869586 0.4698240 1.3769388
#> dev.o[19,3] 1.2945553 1.1420883 0.0091498935 0.43088186 1.0269545 1.8518900
#> dev.o[10,4] 1.0914692 1.3420188 0.0015454724 0.14138617 0.5846418 1.5611129
#> dev.o[12,4] 0.8287740 1.1438309 0.0006723164 0.08920458 0.3742728 1.0836134
#> dev.o[13,4] 1.1223126 1.4919084 0.0008702342 0.12305765 0.5128668 1.5621390
#> 97.5% Rhat n.eff
#> dev.o[1,1] 8.570850 1.002034 1300
#> dev.o[2,1] 4.869970 1.006262 350
#> dev.o[3,1] 5.560072 1.000623 3000
#> dev.o[4,1] 3.619871 1.002762 1300
#> dev.o[5,1] 3.702596 1.002099 3000
#> dev.o[6,1] 4.291187 1.003137 760
#> dev.o[7,1] 3.919780 1.003337 800
#> dev.o[8,1] 4.020055 1.001699 1700
#> dev.o[9,1] 3.906149 1.000569 3000
#> dev.o[10,1] 3.061910 1.001450 2100
#> dev.o[11,1] 4.906873 1.000831 3000
#> dev.o[12,1] 5.616254 1.009133 270
#> dev.o[13,1] 6.121272 1.000617 3000
#> dev.o[14,1] 4.524863 1.001343 2400
#> dev.o[15,1] 3.935522 1.002165 1600
#> dev.o[16,1] 5.996497 1.004031 630
#> dev.o[17,1] 6.989381 1.007276 330
#> dev.o[18,1] 4.811063 1.001833 2100
#> dev.o[19,1] 6.257214 1.001612 2500
#> dev.o[20,1] 3.888415 1.000683 3000
#> dev.o[21,1] 5.178243 1.000879 3000
#> dev.o[1,2] 8.108033 1.003116 770
#> dev.o[2,2] 4.618727 1.003943 580
#> dev.o[3,2] 4.970592 1.001647 1700
#> dev.o[4,2] 4.075323 1.000942 3000
#> dev.o[5,2] 3.088382 1.000674 3000
#> dev.o[6,2] 4.663466 1.000971 3000
#> dev.o[7,2] 4.182459 1.000711 3000
#> dev.o[8,2] 3.715050 1.006258 350
#> dev.o[9,2] 3.649485 1.004360 960
#> dev.o[10,2] 6.687557 1.006888 540
#> dev.o[11,2] 5.018996 1.007412 860
#> dev.o[12,2] 3.902024 1.000845 3000
#> dev.o[13,2] 5.051275 1.001787 3000
#> dev.o[14,2] 3.955349 1.000967 3000
#> dev.o[15,2] 4.520155 1.002919 1500
#> dev.o[16,2] 5.998561 1.002699 910
#> dev.o[17,2] 6.535571 1.009751 260
#> dev.o[18,2] 4.436317 1.000718 3000
#> dev.o[19,2] 2.076721 1.000718 3000
#> dev.o[20,2] 3.482819 1.000679 3000
#> dev.o[21,2] 4.275800 1.002594 960
#> dev.o[9,3] 3.640744 1.003111 1200
#> dev.o[10,3] 3.587085 1.001177 2900
#> dev.o[12,3] 5.691489 1.004141 550
#> dev.o[13,3] 5.318047 1.001506 2000
#> dev.o[19,3] 4.132635 1.003240 1500
#> dev.o[10,4] 4.627563 1.003228 1100
#> dev.o[12,4] 4.185791 1.002376 1400
#> dev.o[13,4] 5.188098 1.003110 830
#>
#> $hat_par
#> mean sd 2.5% 25% 50%
#> hat.par[1,1] 1.561427 0.7706284 0.3662796 0.9849925 1.462782
#> hat.par[2,1] 49.203802 4.7988181 39.6905431 45.9969588 49.180796
#> hat.par[3,1] 43.779836 4.6742097 34.9290590 40.4442186 43.669848
#> hat.par[4,1] 42.219089 4.6626781 33.6511434 38.8618484 42.033077
#> hat.par[5,1] 17.095147 2.5365116 12.2529891 15.3430732 17.006142
#> hat.par[6,1] 43.554272 4.1941302 35.3620110 40.7662234 43.571073
#> hat.par[7,1] 157.555705 7.5015190 143.1114568 152.4415751 157.476214
#> hat.par[8,1] 68.456949 5.5462697 57.4165276 64.7962097 68.332694
#> hat.par[9,1] 90.134388 5.1540080 79.9729570 86.5557915 90.276751
#> hat.par[10,1] 78.867790 3.7806326 71.3883614 76.3286471 78.914891
#> hat.par[11,1] 72.142410 5.5169288 61.3780249 68.4094305 72.086337
#> hat.par[12,1] 77.470999 4.2396341 69.1828405 74.6863878 77.435378
#> hat.par[13,1] 49.364823 4.7499948 40.1054279 46.0915804 49.277263
#> hat.par[14,1] 33.892331 4.6516388 25.3103659 30.6737642 33.689718
#> hat.par[15,1] 36.082603 4.5375572 27.4390838 33.0537627 36.017623
#> hat.par[16,1] 303.330535 13.2421766 277.9510559 294.3403075 303.056981
#> hat.par[17,1] 11.238427 2.5899255 6.6798941 9.3327374 11.086406
#> hat.par[18,1] 22.134921 3.2933754 15.9423755 19.8050800 22.124818
#> hat.par[19,1] 4.077727 1.3909076 1.8223568 3.0496352 3.935959
#> hat.par[20,1] 25.275856 3.6616339 18.5829993 22.7199312 25.068166
#> hat.par[21,1] 32.236698 4.3563327 24.1356712 29.2640778 32.074563
#> hat.par[1,2] 1.425590 0.7469654 0.3176340 0.8684717 1.316024
#> hat.par[2,2] 46.857132 4.9823619 37.6070527 43.3596085 46.676111
#> hat.par[3,2] 30.900562 4.0742714 23.1049232 28.0988853 30.882236
#> hat.par[4,2] 43.716867 5.2791948 33.9058211 40.1326388 43.740594
#> hat.par[5,2] 11.858009 2.1029943 7.9066821 10.3831789 11.785630
#> hat.par[6,2] 35.211722 3.9534026 27.8727277 32.5520204 35.114953
#> hat.par[7,2] 197.033154 9.9884803 177.9326681 190.1411063 196.990858
#> hat.par[8,2] 51.374094 5.0527469 41.9388585 48.0062976 51.228983
#> hat.par[9,2] 81.854852 5.7235351 70.8452322 77.9534514 81.768805
#> hat.par[10,2] 72.988705 3.9551303 65.0851469 70.3369025 73.096502
#> hat.par[11,2] 119.842055 8.2097404 104.1621520 114.2698024 119.777866
#> hat.par[12,2] 80.503147 4.8613769 70.9894048 77.0843591 80.602595
#> hat.par[13,2] 26.037456 4.4891101 17.6747573 23.0123477 25.918713
#> hat.par[14,2] 31.721757 4.4013701 23.5255550 28.7105885 31.528781
#> hat.par[15,2] 32.980432 4.5564565 24.8170795 29.7154039 32.697876
#> hat.par[16,2] 247.748870 12.4702544 223.4233340 239.3315416 247.618189
#> hat.par[17,2] 6.906834 1.8403456 3.7175309 5.6106079 6.765455
#> hat.par[18,2] 12.821288 2.4987651 8.2650180 11.0448742 12.742070
#> hat.par[19,2] 2.440677 0.9327663 1.0083055 1.7643408 2.323747
#> hat.par[20,2] 18.776538 3.0975817 13.3002870 16.5601251 18.615084
#> hat.par[21,2] 21.551418 3.4890502 15.0692225 19.1212768 21.498364
#> hat.par[9,3] 78.948658 5.6727163 67.5725853 75.1683037 78.990541
#> hat.par[10,3] 68.289725 4.3305752 59.8793483 65.3552249 68.312066
#> hat.par[12,3] 67.336957 4.8536411 57.7980055 64.0922778 67.402768
#> hat.par[13,3] 35.334099 5.2614737 25.2408058 31.7598985 35.197067
#> hat.par[19,3] 2.443813 0.9042399 0.9882637 1.7941055 2.352753
#> hat.par[10,4] 66.419634 4.3064497 57.4786199 63.6186894 66.522341
#> hat.par[12,4] 62.897842 4.5043942 53.8659665 59.8132395 62.851996
#> hat.par[13,4] 41.131911 4.8086276 31.9125268 37.8365558 40.976520
#> 75% 97.5% Rhat n.eff
#> hat.par[1,1] 2.052004 3.309770 1.002595 1000
#> hat.par[2,1] 52.438929 58.585048 1.031741 71
#> hat.par[3,1] 46.892831 53.086099 1.008328 340
#> hat.par[4,1] 45.318859 51.897281 1.000510 3000
#> hat.par[5,1] 18.781952 22.369368 1.000883 3000
#> hat.par[6,1] 46.432291 51.594127 1.006253 350
#> hat.par[7,1] 162.683379 172.089264 1.006981 330
#> hat.par[8,1] 72.090005 79.500692 1.010525 280
#> hat.par[9,1] 93.564816 99.991789 1.008714 250
#> hat.par[10,1] 81.571696 86.032703 1.008390 390
#> hat.par[11,1] 75.741863 83.440718 1.008820 240
#> hat.par[12,1] 80.340190 85.695683 1.024591 100
#> hat.par[13,1] 52.634593 58.648238 1.004430 590
#> hat.par[14,1] 36.957067 43.901549 1.010549 200
#> hat.par[15,1] 39.106583 44.919288 1.012693 190
#> hat.par[16,1] 312.366411 329.839983 1.001908 1500
#> hat.par[17,1] 13.022499 16.637254 1.007958 270
#> hat.par[18,1] 24.229119 28.769954 1.001455 2600
#> hat.par[19,1] 4.962324 7.167159 1.001122 3000
#> hat.par[20,1] 27.733846 33.020230 1.003570 650
#> hat.par[21,1] 35.038419 41.064353 1.001607 3000
#> hat.par[1,2] 1.874509 3.180407 1.003049 790
#> hat.par[2,2] 50.293579 56.638008 1.021942 95
#> hat.par[3,2] 33.549897 39.201511 1.002100 2500
#> hat.par[4,2] 47.104536 54.682661 1.000748 3000
#> hat.par[5,2] 13.224854 16.197280 1.001552 1900
#> hat.par[6,2] 37.811547 43.355891 1.001526 1900
#> hat.par[7,2] 203.918289 216.637310 1.004975 450
#> hat.par[8,2] 54.715578 61.446808 1.002531 3000
#> hat.par[9,2] 85.762117 93.307847 1.007336 340
#> hat.par[10,2] 75.741740 80.504196 1.001808 1500
#> hat.par[11,2] 125.227628 136.454114 1.007865 270
#> hat.par[12,2] 83.925862 89.716494 1.009531 220
#> hat.par[13,2] 29.037634 34.954641 1.005252 420
#> hat.par[14,2] 34.495447 41.137005 1.004508 500
#> hat.par[15,2] 35.890027 42.472098 1.004295 530
#> hat.par[16,2] 256.055134 271.808399 1.005475 400
#> hat.par[17,2] 8.095055 10.877710 1.012113 170
#> hat.par[18,2] 14.389988 18.085557 1.001142 3000
#> hat.par[19,2] 2.972008 4.634375 1.001065 3000
#> hat.par[20,2] 20.779111 25.232063 1.001412 2200
#> hat.par[21,2] 23.838917 28.571842 1.002767 880
#> hat.par[9,3] 82.832588 89.448284 1.008218 290
#> hat.par[10,3] 71.326035 76.579854 1.001721 1600
#> hat.par[12,3] 70.604059 76.802308 1.005674 390
#> hat.par[13,3] 38.839963 46.039759 1.004772 660
#> hat.par[19,3] 2.983798 4.450476 1.002234 1200
#> hat.par[10,4] 69.412314 74.210121 1.006360 350
#> hat.par[12,4] 66.145396 71.417867 1.001808 2900
#> hat.par[13,4] 44.338001 50.573515 1.008332 260
#>
#> $leverage_o
#> [1] 0.9178643 0.8510408 0.8171146 0.6693542 0.6588168 0.6805782 0.7736544
#> [8] 0.7306590 0.6557525 0.6162903 0.7746729 0.6283291 0.8233217 0.7669657
#> [15] 0.6931867 0.9208495 0.8763151 0.7690094 0.7616037 0.6661642 0.7866836
#> [22] 0.2358243 0.8499928 0.6775364 0.7839729 0.5193755 0.6851417 0.8571872
#> [29] 0.7027378 0.6987791 0.7308802 0.8727649 0.7477496 0.9789499 0.6539649
#> [36] 0.8005483 0.9077653 0.3501212 0.5679223 0.3082090 0.6439792 0.5865080
#> [43] 0.6746835 0.7024714 0.7587432 1.0388765 0.1568469 0.6770514 0.6809297
#> [50] 0.7763250
#>
#> $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] -0.295801708 0.6039683 -1.5946569 -0.6849308 -0.263458934 0.12010915
#> phi[2] 0.006954976 0.9595987 -1.9020168 -0.6191357 -0.007567471 0.65344284
#> phi[3] 0.069208857 0.9653974 -1.8443533 -0.5688018 0.093231023 0.72894653
#> phi[4] -0.950775923 0.9020072 -2.5273057 -1.5918959 -1.037160043 -0.38462971
#> phi[5] -0.643154747 0.9475764 -2.4771181 -1.2371010 -0.661152659 -0.05066862
#> phi[6] 0.923465106 0.7818165 -0.8132052 0.4560011 0.950357388 1.42622205
#> phi[7] -0.305660490 0.6711014 -1.6982961 -0.7457354 -0.284004741 0.15086061
#> phi[8] -0.271627019 0.8985750 -2.0415904 -0.8880262 -0.269958736 0.32515615
#> 97.5% Rhat n.eff
#> phi[1] 0.7794479 1.072094 73
#> phi[2] 1.9341151 1.020814 110
#> phi[3] 1.8983370 1.014165 160
#> phi[4] 1.0240063 1.085902 29
#> phi[5] 1.3442365 1.006265 1900
#> phi[6] 2.4332065 1.084614 30
#> phi[7] 0.9541814 1.001147 3000
#> phi[8] 1.5542673 1.012024 180
#>
#> $model_assessment
#> DIC pD dev n_data
#> 1 88.1037 35.46406 52.63963 50
#>
#> $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
#>
#> $measure
#> [1] "OR"
#>
#> $model
#> [1] "RE"
#>
#> $assumption
#> [1] "IDE-ARM"
#>
#> $mean_misspar
#> [1] 1e-04 1e-04
#>
#> $var_misspar
#> [1] 1
#>
#> $D
#> [1] 0
#>
#> $ref
#> [1] 1
#>
#> $indic
#> [,1] [,2] [,3] [,4]
#> [1,] 1 1 NA NA
#> [2,] 1 1 NA NA
#> [3,] 1 1 NA NA
#> [4,] 1 1 NA NA
#> [5,] 1 1 NA NA
#> [6,] 1 1 NA NA
#> [7,] 1 1 NA NA
#> [8,] 1 1 NA NA
#> [9,] 1 1 1 NA
#> [10,] 1 1 1 1
#> [11,] 1 1 NA NA
#> [12,] 1 1 1 1
#> [13,] 1 1 1 1
#> [14,] 1 1 NA NA
#> [15,] 1 1 NA NA
#> [16,] 1 1 NA NA
#> [17,] 1 1 NA NA
#> [18,] 1 1 NA NA
#> [19,] 1 1 1 NA
#> [20,] 1 1 NA NA
#> [21,] 1 1 NA NA
#>
#> $jagsfit
#> Inference for Bugs model at "5", 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] -0.943 0.428 -1.770 -1.228 -0.954 -0.672 -0.050
#> EM[3,1] -0.661 0.433 -1.577 -0.939 -0.640 -0.376 0.132
#> EM[4,1] -0.244 0.325 -0.798 -0.476 -0.275 -0.021 0.438
#> EM[5,1] -0.513 0.283 -1.052 -0.686 -0.535 -0.347 0.130
#> EM[6,1] -0.023 0.248 -0.552 -0.190 -0.001 0.166 0.397
#> EM[7,1] -0.464 0.182 -0.796 -0.595 -0.476 -0.341 -0.087
#> EM[8,1] -0.530 0.164 -0.856 -0.636 -0.529 -0.413 -0.218
#> EM[3,2] 0.282 0.485 -0.664 -0.054 0.281 0.630 1.222
#> EM[4,2] 0.700 0.462 -0.239 0.393 0.675 1.018 1.632
#> EM[5,2] 0.431 0.472 -0.545 0.125 0.421 0.726 1.376
#> EM[6,2] 0.920 0.449 -0.010 0.645 0.939 1.220 1.733
#> EM[7,2] 0.479 0.436 -0.368 0.194 0.474 0.765 1.314
#> EM[8,2] 0.413 0.433 -0.480 0.141 0.431 0.717 1.199
#> EM[4,3] 0.417 0.464 -0.470 0.118 0.403 0.694 1.392
#> EM[5,3] 0.148 0.473 -0.803 -0.164 0.149 0.465 1.119
#> EM[6,3] 0.638 0.440 -0.244 0.332 0.662 0.926 1.539
#> EM[7,3] 0.196 0.411 -0.583 -0.074 0.212 0.464 1.016
#> EM[8,3] 0.130 0.420 -0.674 -0.150 0.141 0.408 0.976
#> EM[5,4] -0.269 0.377 -0.999 -0.523 -0.271 -0.016 0.484
#> EM[6,4] 0.220 0.350 -0.464 -0.016 0.229 0.467 0.826
#> EM[7,4] -0.221 0.299 -0.845 -0.414 -0.203 0.002 0.313
#> EM[8,4] -0.287 0.320 -0.981 -0.494 -0.258 -0.043 0.250
#> EM[6,5] 0.490 0.335 -0.242 0.287 0.506 0.717 1.116
#> EM[7,5] 0.048 0.295 -0.568 -0.147 0.045 0.239 0.597
#> EM[8,5] -0.018 0.289 -0.630 -0.190 -0.008 0.179 0.502
#> EM[7,6] -0.441 0.240 -0.886 -0.610 -0.448 -0.284 0.047
#> EM[8,6] -0.507 0.230 -0.960 -0.654 -0.513 -0.353 -0.051
#> EM[8,7] -0.066 0.164 -0.387 -0.174 -0.060 0.046 0.244
#> EM.pred[2,1] -0.934 0.470 -1.861 -1.242 -0.946 -0.630 0.032
#> EM.pred[3,1] -0.654 0.469 -1.635 -0.939 -0.636 -0.359 0.242
#> EM.pred[4,1] -0.243 0.378 -0.926 -0.501 -0.256 0.004 0.506
#> EM.pred[5,1] -0.512 0.345 -1.223 -0.718 -0.518 -0.313 0.206
#> EM.pred[6,1] -0.025 0.317 -0.702 -0.229 0.006 0.194 0.544
#> EM.pred[7,1] -0.464 0.261 -0.974 -0.636 -0.464 -0.293 0.028
#> EM.pred[8,1] -0.534 0.256 -1.083 -0.682 -0.519 -0.373 -0.052
#> EM.pred[3,2] 0.282 0.523 -0.757 -0.077 0.280 0.651 1.286
#> EM.pred[4,2] 0.699 0.503 -0.312 0.359 0.677 1.044 1.681
#> EM.pred[5,2] 0.428 0.511 -0.658 0.090 0.428 0.754 1.439
#> EM.pred[6,2] 0.920 0.490 -0.112 0.611 0.940 1.252 1.827
#> EM.pred[7,2] 0.475 0.477 -0.468 0.185 0.472 0.791 1.381
#> EM.pred[8,2] 0.413 0.475 -0.560 0.111 0.435 0.732 1.299
#> EM.pred[4,3] 0.422 0.500 -0.569 0.105 0.422 0.709 1.512
#> EM.pred[5,3] 0.154 0.505 -0.843 -0.178 0.156 0.485 1.179
#> EM.pred[6,3] 0.638 0.479 -0.312 0.310 0.661 0.946 1.587
#> EM.pred[7,3] 0.195 0.457 -0.704 -0.095 0.206 0.481 1.133
#> EM.pred[8,3] 0.125 0.459 -0.796 -0.164 0.130 0.413 1.052
#> EM.pred[5,4] -0.261 0.418 -1.082 -0.536 -0.255 0.016 0.569
#> EM.pred[6,4] 0.226 0.397 -0.562 -0.031 0.233 0.504 0.956
#> EM.pred[7,4] -0.229 0.360 -0.983 -0.453 -0.217 0.020 0.461
#> EM.pred[8,4] -0.289 0.369 -1.072 -0.521 -0.265 -0.019 0.350
#> EM.pred[6,5] 0.484 0.382 -0.361 0.253 0.507 0.733 1.210
#> EM.pred[7,5] 0.047 0.344 -0.648 -0.171 0.051 0.264 0.717
#> EM.pred[8,5] -0.013 0.346 -0.729 -0.222 0.002 0.210 0.645
#> EM.pred[7,6] -0.447 0.308 -1.043 -0.645 -0.458 -0.253 0.186
#> EM.pred[8,6] -0.510 0.306 -1.135 -0.701 -0.519 -0.307 0.109
#> EM.pred[8,7] -0.064 0.245 -0.557 -0.204 -0.062 0.085 0.411
#> SUCRA[1] 0.123 0.120 0.000 0.000 0.143 0.143 0.429
#> SUCRA[2] 0.879 0.208 0.286 0.857 1.000 1.000 1.000
#> SUCRA[3] 0.703 0.276 0.000 0.571 0.857 0.857 1.000
#> SUCRA[4] 0.346 0.242 0.000 0.143 0.286 0.429 0.857
#> SUCRA[5] 0.603 0.250 0.143 0.429 0.571 0.857 1.000
#> SUCRA[6] 0.143 0.161 0.000 0.000 0.143 0.286 0.571
#> SUCRA[7] 0.561 0.176 0.286 0.429 0.571 0.714 0.857
#> SUCRA[8] 0.642 0.182 0.286 0.571 0.714 0.714 1.000
#> abs_risk[1] 0.392 0.000 0.392 0.392 0.392 0.392 0.392
#> abs_risk[2] 0.209 0.071 0.099 0.159 0.199 0.247 0.380
#> abs_risk[3] 0.258 0.079 0.117 0.201 0.253 0.307 0.424
#> abs_risk[4] 0.339 0.073 0.225 0.286 0.328 0.387 0.499
#> abs_risk[5] 0.282 0.057 0.184 0.245 0.274 0.313 0.423
#> abs_risk[6] 0.388 0.057 0.271 0.347 0.391 0.432 0.489
#> abs_risk[7] 0.290 0.037 0.225 0.262 0.286 0.314 0.371
#> abs_risk[8] 0.276 0.032 0.215 0.254 0.275 0.299 0.341
#> 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] -0.304 0.371 -0.980 -0.560 -0.320 -0.062 0.438
#> delta[2,2] -0.285 0.302 -0.813 -0.506 -0.306 -0.080 0.337
#> delta[3,2] -0.547 0.229 -1.007 -0.694 -0.553 -0.383 -0.115
#> delta[4,2] -0.484 0.182 -0.861 -0.606 -0.478 -0.359 -0.149
#> delta[5,2] -0.486 0.248 -0.959 -0.653 -0.491 -0.316 -0.021
#> delta[6,2] -0.381 0.221 -0.787 -0.539 -0.388 -0.241 0.061
#> delta[7,2] -0.486 0.176 -0.830 -0.607 -0.482 -0.370 -0.145
#> delta[8,2] -0.437 0.191 -0.819 -0.570 -0.438 -0.309 -0.064
#> delta[9,2] -0.477 0.201 -0.849 -0.614 -0.484 -0.350 -0.075
#> delta[10,2] -0.165 0.373 -0.805 -0.429 -0.193 0.069 0.671
#> delta[11,2] -0.097 0.258 -0.614 -0.276 -0.083 0.102 0.335
#> delta[12,2] -0.198 0.332 -0.768 -0.437 -0.228 0.029 0.482
#> delta[13,2] -1.002 0.409 -1.831 -1.262 -1.008 -0.745 -0.166
#> delta[14,2] -0.114 0.196 -0.526 -0.242 -0.105 0.017 0.251
#> delta[15,2] 0.043 0.244 -0.466 -0.118 0.053 0.210 0.504
#> delta[16,2] -0.351 0.135 -0.608 -0.442 -0.353 -0.262 -0.080
#> delta[17,2] -0.620 0.271 -1.203 -0.773 -0.613 -0.439 -0.121
#> delta[18,2] -0.573 0.302 -1.169 -0.759 -0.573 -0.398 0.062
#> delta[19,2] -0.554 0.332 -1.178 -0.762 -0.558 -0.356 0.130
#> delta[20,2] -0.434 0.224 -0.870 -0.592 -0.433 -0.282 -0.009
#> delta[21,2] -0.587 0.215 -1.038 -0.722 -0.579 -0.439 -0.196
#> delta[9,3] -0.576 0.191 -0.961 -0.702 -0.574 -0.445 -0.197
#> delta[10,3] -0.516 0.312 -1.116 -0.708 -0.527 -0.341 0.194
#> delta[12,3] -0.395 0.302 -0.934 -0.585 -0.421 -0.230 0.277
#> delta[13,3] -0.718 0.411 -1.628 -0.982 -0.689 -0.445 0.027
#> delta[19,3] -0.522 0.268 -1.084 -0.679 -0.512 -0.346 -0.044
#> delta[10,4] -0.516 0.248 -1.033 -0.669 -0.514 -0.343 -0.058
#> delta[12,4] -0.371 0.235 -0.809 -0.534 -0.386 -0.220 0.140
#> delta[13,4] -0.135 0.308 -0.784 -0.344 -0.097 0.092 0.364
#> dev.o[1,1] 2.378 2.336 0.007 0.596 1.713 3.419 8.571
#> dev.o[2,1] 0.968 1.346 0.001 0.101 0.448 1.280 4.870
#> dev.o[3,1] 1.186 1.544 0.002 0.139 0.619 1.690 5.560
#> dev.o[4,1] 0.716 1.003 0.001 0.077 0.326 0.899 3.620
#> dev.o[5,1] 0.739 1.022 0.001 0.080 0.341 1.001 3.703
#> dev.o[6,1] 0.936 1.274 0.001 0.110 0.469 1.274 4.291
#> dev.o[7,1] 0.807 1.136 0.001 0.080 0.381 1.074 3.920
#> dev.o[8,1] 0.736 1.082 0.001 0.071 0.322 0.940 4.020
#> dev.o[9,1] 0.742 1.084 0.001 0.069 0.335 0.953 3.906
#> dev.o[10,1] 0.617 0.838 0.001 0.069 0.295 0.832 3.062
#> dev.o[11,1] 0.979 1.322 0.001 0.106 0.440 1.335 4.907
#> dev.o[12,1] 1.291 1.588 0.002 0.185 0.714 1.831 5.616
#> dev.o[13,1] 1.290 1.664 0.002 0.141 0.651 1.794 6.121
#> dev.o[14,1] 0.913 1.246 0.001 0.102 0.423 1.226 4.525
#> dev.o[15,1] 0.846 1.126 0.001 0.085 0.408 1.161 3.936
#> dev.o[16,1] 1.206 1.632 0.001 0.123 0.557 1.633 5.996
#> dev.o[17,1] 1.642 1.945 0.003 0.223 0.911 2.418 6.989
#> dev.o[18,1] 0.997 1.422 0.001 0.099 0.451 1.359 4.811
#> dev.o[19,1] 1.620 1.803 0.004 0.271 1.022 2.379 6.257
#> dev.o[20,1] 0.751 1.093 0.001 0.074 0.347 0.985 3.888
#> dev.o[21,1] 1.076 1.428 0.001 0.111 0.532 1.464 5.178
#> dev.o[1,2] 3.253 1.946 0.648 1.839 2.876 4.272 8.108
#> dev.o[2,2] 0.970 1.308 0.001 0.096 0.485 1.359 4.619
#> dev.o[3,2] 1.046 1.347 0.001 0.116 0.525 1.426 4.971
#> dev.o[4,2] 0.829 1.210 0.001 0.072 0.362 1.091 4.075
#> dev.o[5,2] 0.609 0.887 0.001 0.062 0.270 0.800 3.088
#> dev.o[6,2] 1.006 1.336 0.001 0.108 0.493 1.392 4.663
#> dev.o[7,2] 0.865 1.186 0.001 0.087 0.424 1.151 4.182
#> dev.o[8,2] 0.713 1.012 0.000 0.075 0.309 0.972 3.715
#> dev.o[9,2] 0.699 1.040 0.001 0.075 0.321 0.869 3.650
#> dev.o[10,2] 1.575 1.869 0.003 0.221 0.903 2.253 6.688
#> dev.o[11,2] 0.978 1.401 0.001 0.101 0.452 1.281 5.019
#> dev.o[12,2] 0.818 1.113 0.001 0.092 0.374 1.075 3.902
#> dev.o[13,2] 0.979 1.384 0.001 0.104 0.438 1.293 5.051
#> dev.o[14,2] 0.760 1.088 0.001 0.077 0.334 0.992 3.955
#> dev.o[15,2] 0.949 1.239 0.001 0.107 0.481 1.315 4.520
#> dev.o[16,2] 1.207 1.710 0.001 0.125 0.561 1.601 5.999
#> dev.o[17,2] 1.928 1.790 0.010 0.565 1.448 2.820 6.536
#> dev.o[18,2] 0.911 1.266 0.001 0.101 0.437 1.222 4.436
#> dev.o[19,2] 0.396 0.586 0.000 0.040 0.175 0.506 2.077
#> dev.o[20,2] 0.737 1.002 0.001 0.074 0.350 1.000 3.483
#> dev.o[21,2] 0.942 1.199 0.001 0.121 0.486 1.291 4.276
#> dev.o[9,3] 0.755 1.034 0.001 0.078 0.353 1.051 3.641
#> dev.o[10,3] 0.706 1.002 0.001 0.070 0.333 0.918 3.587
#> dev.o[12,3] 1.192 1.596 0.002 0.135 0.579 1.639 5.691
#> dev.o[13,3] 1.043 1.499 0.001 0.109 0.470 1.377 5.318
#> dev.o[19,3] 1.295 1.142 0.009 0.431 1.027 1.852 4.133
#> dev.o[10,4] 1.091 1.342 0.002 0.141 0.585 1.561 4.628
#> dev.o[12,4] 0.829 1.144 0.001 0.089 0.374 1.084 4.186
#> dev.o[13,4] 1.122 1.492 0.001 0.123 0.513 1.562 5.188
#> effectiveness[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,1] 0.611 0.488 0.000 0.000 1.000 1.000 1.000
#> effectiveness[3,1] 0.224 0.417 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,1] 0.008 0.091 0.000 0.000 0.000 0.000 0.000
#> effectiveness[5,1] 0.080 0.272 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,1] 0.001 0.036 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,1] 0.017 0.129 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,1] 0.058 0.234 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,2] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,2] 0.210 0.407 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,2] 0.305 0.460 0.000 0.000 0.000 1.000 1.000
#> effectiveness[4,2] 0.044 0.206 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,2] 0.197 0.398 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,2] 0.002 0.041 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,2] 0.086 0.280 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,2] 0.157 0.364 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,3] 0.001 0.032 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,3] 0.055 0.229 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,3] 0.122 0.327 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,3] 0.089 0.284 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,3] 0.220 0.414 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,3] 0.007 0.085 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,3] 0.217 0.412 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,3] 0.289 0.454 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,4] 0.003 0.052 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,4] 0.037 0.190 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,4] 0.106 0.308 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,4] 0.104 0.305 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,4] 0.145 0.352 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,4] 0.024 0.153 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,4] 0.305 0.460 0.000 0.000 0.000 1.000 1.000
#> effectiveness[8,4] 0.275 0.447 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,5] 0.034 0.182 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,5] 0.047 0.212 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,5] 0.104 0.305 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,5] 0.175 0.380 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,5] 0.165 0.372 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,5] 0.068 0.251 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,5] 0.248 0.432 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,5] 0.159 0.365 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,6] 0.166 0.372 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,6] 0.019 0.138 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,6] 0.069 0.254 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,6] 0.287 0.452 0.000 0.000 0.000 1.000 1.000
#> effectiveness[5,6] 0.122 0.327 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,6] 0.159 0.366 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,6] 0.119 0.324 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,6] 0.059 0.235 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,7] 0.411 0.492 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,7] 0.013 0.115 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,7] 0.041 0.198 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,7] 0.141 0.348 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,7] 0.057 0.232 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,7] 0.326 0.469 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,7] 0.008 0.089 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,7] 0.003 0.052 0.000 0.000 0.000 0.000 0.000
#> effectiveness[1,8] 0.385 0.487 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,8] 0.007 0.083 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,8] 0.029 0.169 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,8] 0.152 0.359 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,8] 0.014 0.116 0.000 0.000 0.000 0.000 0.000
#> effectiveness[6,8] 0.413 0.492 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,8] 0.000 0.018 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,8] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> hat.par[1,1] 1.561 0.771 0.366 0.985 1.463 2.052 3.310
#> hat.par[2,1] 49.204 4.799 39.691 45.997 49.181 52.439 58.585
#> hat.par[3,1] 43.780 4.674 34.929 40.444 43.670 46.893 53.086
#> hat.par[4,1] 42.219 4.663 33.651 38.862 42.033 45.319 51.897
#> hat.par[5,1] 17.095 2.537 12.253 15.343 17.006 18.782 22.369
#> hat.par[6,1] 43.554 4.194 35.362 40.766 43.571 46.432 51.594
#> hat.par[7,1] 157.556 7.502 143.111 152.442 157.476 162.683 172.089
#> hat.par[8,1] 68.457 5.546 57.417 64.796 68.333 72.090 79.501
#> hat.par[9,1] 90.134 5.154 79.973 86.556 90.277 93.565 99.992
#> hat.par[10,1] 78.868 3.781 71.388 76.329 78.915 81.572 86.033
#> hat.par[11,1] 72.142 5.517 61.378 68.409 72.086 75.742 83.441
#> hat.par[12,1] 77.471 4.240 69.183 74.686 77.435 80.340 85.696
#> hat.par[13,1] 49.365 4.750 40.105 46.092 49.277 52.635 58.648
#> hat.par[14,1] 33.892 4.652 25.310 30.674 33.690 36.957 43.902
#> hat.par[15,1] 36.083 4.538 27.439 33.054 36.018 39.107 44.919
#> hat.par[16,1] 303.331 13.242 277.951 294.340 303.057 312.366 329.840
#> hat.par[17,1] 11.238 2.590 6.680 9.333 11.086 13.022 16.637
#> hat.par[18,1] 22.135 3.293 15.942 19.805 22.125 24.229 28.770
#> hat.par[19,1] 4.078 1.391 1.822 3.050 3.936 4.962 7.167
#> hat.par[20,1] 25.276 3.662 18.583 22.720 25.068 27.734 33.020
#> hat.par[21,1] 32.237 4.356 24.136 29.264 32.075 35.038 41.064
#> hat.par[1,2] 1.426 0.747 0.318 0.868 1.316 1.875 3.180
#> hat.par[2,2] 46.857 4.982 37.607 43.360 46.676 50.294 56.638
#> hat.par[3,2] 30.901 4.074 23.105 28.099 30.882 33.550 39.202
#> hat.par[4,2] 43.717 5.279 33.906 40.133 43.741 47.105 54.683
#> hat.par[5,2] 11.858 2.103 7.907 10.383 11.786 13.225 16.197
#> hat.par[6,2] 35.212 3.953 27.873 32.552 35.115 37.812 43.356
#> hat.par[7,2] 197.033 9.988 177.933 190.141 196.991 203.918 216.637
#> hat.par[8,2] 51.374 5.053 41.939 48.006 51.229 54.716 61.447
#> hat.par[9,2] 81.855 5.724 70.845 77.953 81.769 85.762 93.308
#> hat.par[10,2] 72.989 3.955 65.085 70.337 73.097 75.742 80.504
#> hat.par[11,2] 119.842 8.210 104.162 114.270 119.778 125.228 136.454
#> hat.par[12,2] 80.503 4.861 70.989 77.084 80.603 83.926 89.716
#> hat.par[13,2] 26.037 4.489 17.675 23.012 25.919 29.038 34.955
#> hat.par[14,2] 31.722 4.401 23.526 28.711 31.529 34.495 41.137
#> hat.par[15,2] 32.980 4.556 24.817 29.715 32.698 35.890 42.472
#> hat.par[16,2] 247.749 12.470 223.423 239.332 247.618 256.055 271.808
#> hat.par[17,2] 6.907 1.840 3.718 5.611 6.765 8.095 10.878
#> hat.par[18,2] 12.821 2.499 8.265 11.045 12.742 14.390 18.086
#> hat.par[19,2] 2.441 0.933 1.008 1.764 2.324 2.972 4.634
#> hat.par[20,2] 18.777 3.098 13.300 16.560 18.615 20.779 25.232
#> hat.par[21,2] 21.551 3.489 15.069 19.121 21.498 23.839 28.572
#> hat.par[9,3] 78.949 5.673 67.573 75.168 78.991 82.833 89.448
#> hat.par[10,3] 68.290 4.331 59.879 65.355 68.312 71.326 76.580
#> hat.par[12,3] 67.337 4.854 57.798 64.092 67.403 70.604 76.802
#> hat.par[13,3] 35.334 5.261 25.241 31.760 35.197 38.840 46.040
#> hat.par[19,3] 2.444 0.904 0.988 1.794 2.353 2.984 4.450
#> hat.par[10,4] 66.420 4.306 57.479 63.619 66.522 69.412 74.210
#> hat.par[12,4] 62.898 4.504 53.866 59.813 62.852 66.145 71.418
#> hat.par[13,4] 41.132 4.809 31.913 37.837 40.977 44.338 50.574
#> phi[1] -0.296 0.604 -1.595 -0.685 -0.263 0.120 0.779
#> phi[2] 0.007 0.960 -1.902 -0.619 -0.008 0.653 1.934
#> phi[3] 0.069 0.965 -1.844 -0.569 0.093 0.729 1.898
#> phi[4] -0.951 0.902 -2.527 -1.592 -1.037 -0.385 1.024
#> phi[5] -0.643 0.948 -2.477 -1.237 -0.661 -0.051 1.344
#> phi[6] 0.923 0.782 -0.813 0.456 0.950 1.426 2.433
#> phi[7] -0.306 0.671 -1.698 -0.746 -0.284 0.151 0.954
#> phi[8] -0.272 0.899 -2.042 -0.888 -0.270 0.325 1.554
#> tau 0.172 0.085 0.040 0.108 0.164 0.220 0.367
#> totresdev.o 52.640 8.844 36.561 46.532 52.133 58.226 70.970
#> deviance 580.351 13.448 556.084 570.663 579.747 588.987 608.105
#> Rhat n.eff
#> EM[2,1] 1.084 35
#> EM[3,1] 1.063 52
#> EM[4,1] 1.130 21
#> EM[5,1] 1.024 770
#> EM[6,1] 1.131 23
#> EM[7,1] 1.039 120
#> EM[8,1] 1.023 350
#> EM[3,2] 1.007 1300
#> EM[4,2] 1.020 210
#> EM[5,2] 1.042 57
#> EM[6,2] 1.049 58
#> EM[7,2] 1.050 59
#> EM[8,2] 1.078 35
#> EM[4,3] 1.019 290
#> EM[5,3] 1.028 89
#> EM[6,3] 1.046 66
#> EM[7,3] 1.038 87
#> EM[8,3] 1.055 46
#> EM[5,4] 1.076 36
#> EM[6,4] 1.136 20
#> EM[7,4] 1.092 27
#> EM[8,4] 1.148 18
#> EM[6,5] 1.046 50
#> EM[7,5] 1.010 2000
#> EM[8,5] 1.006 420
#> EM[7,6] 1.087 31
#> EM[8,6] 1.108 25
#> EM[8,7] 1.030 81
#> EM.pred[2,1] 1.072 41
#> EM.pred[3,1] 1.055 63
#> EM.pred[4,1] 1.102 26
#> EM.pred[5,1] 1.020 1800
#> EM.pred[6,1] 1.066 46
#> EM.pred[7,1] 1.028 210
#> EM.pred[8,1] 1.015 520
#> EM.pred[3,2] 1.008 980
#> EM.pred[4,2] 1.018 250
#> EM.pred[5,2] 1.036 68
#> EM.pred[6,2] 1.048 55
#> EM.pred[7,2] 1.045 64
#> EM.pred[8,2] 1.060 44
#> EM.pred[4,3] 1.014 330
#> EM.pred[5,3] 1.030 88
#> EM.pred[6,3] 1.043 73
#> EM.pred[7,3] 1.030 110
#> EM.pred[8,3] 1.050 53
#> EM.pred[5,4] 1.056 49
#> EM.pred[6,4] 1.088 28
#> EM.pred[7,4] 1.067 36
#> EM.pred[8,4] 1.103 25
#> EM.pred[6,5] 1.042 58
#> EM.pred[7,5] 1.011 2500
#> EM.pred[8,5] 1.006 550
#> EM.pred[7,6] 1.056 53
#> EM.pred[8,6] 1.066 39
#> EM.pred[8,7] 1.018 200
#> SUCRA[1] 1.071 51
#> SUCRA[2] 1.053 76
#> SUCRA[3] 1.021 130
#> SUCRA[4] 1.072 32
#> SUCRA[5] 1.007 330
#> SUCRA[6] 1.130 24
#> SUCRA[7] 1.020 110
#> SUCRA[8] 1.072 33
#> abs_risk[1] 1.000 1
#> abs_risk[2] 1.086 34
#> abs_risk[3] 1.068 52
#> abs_risk[4] 1.129 21
#> abs_risk[5] 1.024 730
#> abs_risk[6] 1.141 22
#> abs_risk[7] 1.041 120
#> abs_risk[8] 1.025 310
#> 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] 1.085 32
#> delta[2,2] 1.112 23
#> delta[3,2] 1.018 320
#> delta[4,2] 1.004 2500
#> delta[5,2] 1.029 360
#> delta[6,2] 1.020 210
#> delta[7,2] 1.014 1600
#> delta[8,2] 1.028 140
#> delta[9,2] 1.025 320
#> delta[10,2] 1.151 19
#> delta[11,2] 1.116 25
#> delta[12,2] 1.154 18
#> delta[13,2] 1.096 33
#> delta[14,2] 1.027 81
#> delta[15,2] 1.094 30
#> delta[16,2] 1.006 930
#> delta[17,2] 1.072 40
#> delta[18,2] 1.019 960
#> delta[19,2] 1.020 2600
#> delta[20,2] 1.044 70
#> delta[21,2] 1.012 290
#> delta[9,3] 1.021 280
#> delta[10,3] 1.037 390
#> delta[12,3] 1.037 140
#> delta[13,3] 1.074 51
#> delta[19,3] 1.029 430
#> delta[10,4] 1.055 140
#> delta[12,4] 1.049 59
#> delta[13,4] 1.133 22
#> dev.o[1,1] 1.002 1300
#> dev.o[2,1] 1.006 350
#> dev.o[3,1] 1.001 3000
#> dev.o[4,1] 1.003 1300
#> dev.o[5,1] 1.002 3000
#> dev.o[6,1] 1.003 760
#> dev.o[7,1] 1.003 800
#> dev.o[8,1] 1.002 1700
#> dev.o[9,1] 1.001 3000
#> dev.o[10,1] 1.001 2100
#> dev.o[11,1] 1.001 3000
#> dev.o[12,1] 1.009 270
#> dev.o[13,1] 1.001 3000
#> dev.o[14,1] 1.001 2400
#> dev.o[15,1] 1.002 1600
#> dev.o[16,1] 1.004 630
#> dev.o[17,1] 1.007 330
#> dev.o[18,1] 1.002 2100
#> dev.o[19,1] 1.002 2500
#> dev.o[20,1] 1.001 3000
#> dev.o[21,1] 1.001 3000
#> dev.o[1,2] 1.003 770
#> dev.o[2,2] 1.004 580
#> dev.o[3,2] 1.002 1700
#> dev.o[4,2] 1.001 3000
#> dev.o[5,2] 1.001 3000
#> dev.o[6,2] 1.001 3000
#> dev.o[7,2] 1.001 3000
#> dev.o[8,2] 1.006 350
#> dev.o[9,2] 1.004 960
#> dev.o[10,2] 1.007 540
#> dev.o[11,2] 1.007 860
#> dev.o[12,2] 1.001 3000
#> dev.o[13,2] 1.002 3000
#> dev.o[14,2] 1.001 3000
#> dev.o[15,2] 1.003 1500
#> dev.o[16,2] 1.003 910
#> dev.o[17,2] 1.010 260
#> dev.o[18,2] 1.001 3000
#> dev.o[19,2] 1.001 3000
#> dev.o[20,2] 1.001 3000
#> dev.o[21,2] 1.003 960
#> dev.o[9,3] 1.003 1200
#> dev.o[10,3] 1.001 2900
#> dev.o[12,3] 1.004 550
#> dev.o[13,3] 1.002 2000
#> dev.o[19,3] 1.003 1500
#> dev.o[10,4] 1.003 1100
#> dev.o[12,4] 1.002 1400
#> dev.o[13,4] 1.003 830
#> effectiveness[1,1] 1.000 1
#> effectiveness[2,1] 1.012 170
#> effectiveness[3,1] 1.002 1700
#> effectiveness[4,1] 1.108 470
#> effectiveness[5,1] 1.046 140
#> effectiveness[6,1] 1.293 750
#> effectiveness[7,1] 1.107 240
#> effectiveness[8,1] 1.065 130
#> effectiveness[1,2] 1.000 1
#> effectiveness[2,2] 1.003 840
#> effectiveness[3,2] 1.013 180
#> effectiveness[4,2] 1.029 390
#> effectiveness[5,2] 1.001 3000
#> effectiveness[6,2] 1.189 1200
#> effectiveness[7,2] 1.027 230
#> effectiveness[8,2] 1.063 61
#> effectiveness[1,3] 1.292 1000
#> effectiveness[2,3] 1.047 190
#> effectiveness[3,3] 1.007 620
#> effectiveness[4,3] 1.026 230
#> effectiveness[5,3] 1.002 1100
#> effectiveness[6,3] 1.121 470
#> effectiveness[7,3] 1.002 1500
#> effectiveness[8,3] 1.007 350
#> effectiveness[1,4] 1.295 370
#> effectiveness[2,4] 1.040 330
#> effectiveness[3,4] 1.032 160
#> effectiveness[4,4] 1.038 140
#> effectiveness[5,4] 1.009 440
#> effectiveness[6,4] 1.071 270
#> effectiveness[7,4] 1.002 1100
#> effectiveness[8,4] 1.006 400
#> effectiveness[1,5] 1.129 100
#> effectiveness[2,5] 1.072 150
#> effectiveness[3,5] 1.043 120
#> effectiveness[4,5] 1.006 510
#> effectiveness[5,5] 1.004 900
#> effectiveness[6,5] 1.165 43
#> effectiveness[7,5] 1.015 170
#> effectiveness[8,5] 1.040 96
#> effectiveness[1,6] 1.097 39
#> effectiveness[2,6] 1.040 620
#> effectiveness[3,6] 1.004 1600
#> effectiveness[4,6] 1.002 1700
#> effectiveness[5,6] 1.078 60
#> effectiveness[6,6] 1.013 280
#> effectiveness[7,6] 1.017 260
#> effectiveness[8,6] 1.059 140
#> effectiveness[1,7] 1.068 35
#> effectiveness[2,7] 1.071 480
#> effectiveness[3,7] 1.056 210
#> effectiveness[4,7] 1.042 96
#> effectiveness[5,7] 1.024 370
#> effectiveness[6,7] 1.009 250
#> effectiveness[7,7] 1.115 460
#> effectiveness[8,7] 1.088 1800
#> effectiveness[1,8] 1.054 44
#> effectiveness[2,8] 1.198 260
#> effectiveness[3,8] 1.040 420
#> effectiveness[4,8] 1.063 62
#> effectiveness[5,8] 1.019 1800
#> effectiveness[6,8] 1.077 31
#> effectiveness[7,8] 1.291 3000
#> effectiveness[8,8] 1.000 1
#> hat.par[1,1] 1.003 1000
#> hat.par[2,1] 1.032 71
#> hat.par[3,1] 1.008 340
#> hat.par[4,1] 1.001 3000
#> hat.par[5,1] 1.001 3000
#> hat.par[6,1] 1.006 350
#> hat.par[7,1] 1.007 330
#> hat.par[8,1] 1.011 280
#> hat.par[9,1] 1.009 250
#> hat.par[10,1] 1.008 390
#> hat.par[11,1] 1.009 240
#> hat.par[12,1] 1.025 100
#> hat.par[13,1] 1.004 590
#> hat.par[14,1] 1.011 200
#> hat.par[15,1] 1.013 190
#> hat.par[16,1] 1.002 1500
#> hat.par[17,1] 1.008 270
#> hat.par[18,1] 1.001 2600
#> hat.par[19,1] 1.001 3000
#> hat.par[20,1] 1.004 650
#> hat.par[21,1] 1.002 3000
#> hat.par[1,2] 1.003 790
#> hat.par[2,2] 1.022 95
#> hat.par[3,2] 1.002 2500
#> hat.par[4,2] 1.001 3000
#> hat.par[5,2] 1.002 1900
#> hat.par[6,2] 1.002 1900
#> hat.par[7,2] 1.005 450
#> hat.par[8,2] 1.003 3000
#> hat.par[9,2] 1.007 340
#> hat.par[10,2] 1.002 1500
#> hat.par[11,2] 1.008 270
#> hat.par[12,2] 1.010 220
#> hat.par[13,2] 1.005 420
#> hat.par[14,2] 1.005 500
#> hat.par[15,2] 1.004 530
#> hat.par[16,2] 1.005 400
#> hat.par[17,2] 1.012 170
#> hat.par[18,2] 1.001 3000
#> hat.par[19,2] 1.001 3000
#> hat.par[20,2] 1.001 2200
#> hat.par[21,2] 1.003 880
#> hat.par[9,3] 1.008 290
#> hat.par[10,3] 1.002 1600
#> hat.par[12,3] 1.006 390
#> hat.par[13,3] 1.005 660
#> hat.par[19,3] 1.002 1200
#> hat.par[10,4] 1.006 350
#> hat.par[12,4] 1.002 2900
#> hat.par[13,4] 1.008 260
#> phi[1] 1.072 73
#> phi[2] 1.021 110
#> phi[3] 1.014 160
#> phi[4] 1.086 29
#> phi[5] 1.006 1900
#> phi[6] 1.085 30
#> phi[7] 1.001 3000
#> phi[8] 1.012 180
#> tau 1.103 28
#> totresdev.o 1.008 270
#> deviance 1.004 540
#>
#> 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 = 90.1 and DIC = 670.5
#> DIC is an estimate of expected predictive error (lower deviance is better).
#>
#> $n_chains
#> [1] 3
#>
#> $n_iter
#> [1] 1000
#>
#> $n_burnin
#> [1] 100
#>
#> $n_thin
#> [1] 1
#>
#> $EM_pred
#> mean sd 2.5% 25% 50%
#> EM.pred[2,1] -0.93439096 0.4704194 -1.8614694 -1.24168961 -0.945917720
#> EM.pred[3,1] -0.65447817 0.4689424 -1.6348849 -0.93892930 -0.636007958
#> EM.pred[4,1] -0.24266729 0.3775789 -0.9263943 -0.50086641 -0.255940883
#> EM.pred[5,1] -0.51249373 0.3446975 -1.2232883 -0.71768736 -0.517654534
#> EM.pred[6,1] -0.02499362 0.3171654 -0.7019794 -0.22923936 0.006346857
#> EM.pred[7,1] -0.46408359 0.2605853 -0.9741160 -0.63626871 -0.463969222
#> EM.pred[8,1] -0.53440540 0.2559723 -1.0834746 -0.68196860 -0.519379512
#> EM.pred[3,2] 0.28239358 0.5225964 -0.7572123 -0.07682769 0.280168323
#> EM.pred[4,2] 0.69948748 0.5027392 -0.3119523 0.35894421 0.676560974
#> EM.pred[5,2] 0.42752658 0.5114875 -0.6577451 0.09042370 0.428445960
#> EM.pred[6,2] 0.92019240 0.4901500 -0.1117104 0.61132289 0.939923249
#> EM.pred[7,2] 0.47512310 0.4768725 -0.4675294 0.18538482 0.471539352
#> EM.pred[8,2] 0.41340263 0.4747481 -0.5604168 0.11069493 0.434811085
#> EM.pred[4,3] 0.42229359 0.4997595 -0.5691731 0.10486654 0.421767299
#> EM.pred[5,3] 0.15374842 0.5053933 -0.8432533 -0.17764469 0.156086470
#> EM.pred[6,3] 0.63760030 0.4791304 -0.3121988 0.30982717 0.661371142
#> EM.pred[7,3] 0.19457240 0.4567659 -0.7041167 -0.09526075 0.206255204
#> EM.pred[8,3] 0.12483266 0.4594094 -0.7960228 -0.16358152 0.130299035
#> EM.pred[5,4] -0.26064612 0.4181152 -1.0821877 -0.53579344 -0.255345586
#> EM.pred[6,4] 0.22612849 0.3972014 -0.5622487 -0.03102765 0.233178567
#> EM.pred[7,4] -0.22891762 0.3601705 -0.9825723 -0.45269435 -0.216638600
#> EM.pred[8,4] -0.28885883 0.3685978 -1.0722271 -0.52116624 -0.265429240
#> EM.pred[6,5] 0.48355438 0.3820296 -0.3609312 0.25321169 0.506640986
#> EM.pred[7,5] 0.04695220 0.3440098 -0.6480747 -0.17090491 0.051018359
#> EM.pred[8,5] -0.01261039 0.3456008 -0.7292571 -0.22240927 0.002055410
#> EM.pred[7,6] -0.44734297 0.3081427 -1.0432561 -0.64515355 -0.458080275
#> EM.pred[8,6] -0.50989819 0.3057031 -1.1345109 -0.70100857 -0.519196894
#> EM.pred[8,7] -0.06412286 0.2454193 -0.5571835 -0.20392534 -0.062210405
#> 75% 97.5% Rhat n.eff
#> EM.pred[2,1] -0.630458325 0.03164389 1.072033 41
#> EM.pred[3,1] -0.359170256 0.24194114 1.054582 63
#> EM.pred[4,1] 0.004391119 0.50559384 1.101752 26
#> EM.pred[5,1] -0.313086964 0.20585867 1.020245 1800
#> EM.pred[6,1] 0.194074138 0.54351576 1.066256 46
#> EM.pred[7,1] -0.293386855 0.02808350 1.028330 210
#> EM.pred[8,1] -0.372937561 -0.05243824 1.014955 520
#> EM.pred[3,2] 0.650558005 1.28555630 1.008459 980
#> EM.pred[4,2] 1.043868560 1.68069533 1.018297 250
#> EM.pred[5,2] 0.753830869 1.43865029 1.035836 68
#> EM.pred[6,2] 1.251872459 1.82671052 1.048476 55
#> EM.pred[7,2] 0.790803087 1.38061819 1.044528 64
#> EM.pred[8,2] 0.732011018 1.29857088 1.060415 44
#> EM.pred[4,3] 0.708619680 1.51178973 1.014048 330
#> EM.pred[5,3] 0.484972329 1.17886513 1.030255 88
#> EM.pred[6,3] 0.945822482 1.58678844 1.043321 73
#> EM.pred[7,3] 0.480999282 1.13309407 1.029974 110
#> EM.pred[8,3] 0.412834899 1.05224282 1.050467 53
#> EM.pred[5,4] 0.015697714 0.56894551 1.056435 49
#> EM.pred[6,4] 0.504158712 0.95553147 1.088144 28
#> EM.pred[7,4] 0.019801957 0.46145679 1.067302 36
#> EM.pred[8,4] -0.018816292 0.34973276 1.103475 25
#> EM.pred[6,5] 0.733370853 1.21049586 1.041712 58
#> EM.pred[7,5] 0.264470553 0.71716542 1.010742 2500
#> EM.pred[8,5] 0.209783535 0.64480703 1.005913 550
#> EM.pred[7,6] -0.253412601 0.18610010 1.055625 53
#> EM.pred[8,6] -0.307304124 0.10911158 1.065884 39
#> EM.pred[8,7] 0.085292484 0.41086400 1.017754 200
#>
#> $tau
#> mean sd 2.5% 25% 50% 75%
#> 0.17204709 0.08503728 0.04034496 0.10812468 0.16447152 0.21951310
#> 97.5% Rhat n.eff
#> 0.36724217 1.10256287 28.00000000
#>
#> $delta
#> mean sd 2.5% 25% 50% 75%
#> delta[1,2] -0.30360684 0.3705316 -0.9804842 -0.5597374 -0.31978681 -0.06163109
#> delta[2,2] -0.28513924 0.3018242 -0.8130273 -0.5055970 -0.30631918 -0.07979417
#> delta[3,2] -0.54745428 0.2287103 -1.0068736 -0.6940086 -0.55290765 -0.38317315
#> delta[4,2] -0.48415564 0.1821835 -0.8608569 -0.6056770 -0.47771304 -0.35942222
#> delta[5,2] -0.48596682 0.2479148 -0.9590630 -0.6530709 -0.49119085 -0.31613492
#> delta[6,2] -0.38105303 0.2206252 -0.7865092 -0.5385429 -0.38831676 -0.24094187
#> delta[7,2] -0.48589965 0.1763856 -0.8303081 -0.6072086 -0.48169695 -0.36970840
#> delta[8,2] -0.43730798 0.1910446 -0.8185150 -0.5698080 -0.43788158 -0.30856391
#> delta[9,2] -0.47715431 0.2011313 -0.8493187 -0.6139492 -0.48351989 -0.34977536
#> delta[10,2] -0.16530047 0.3733135 -0.8053398 -0.4293337 -0.19267827 0.06923352
#> delta[11,2] -0.09715015 0.2577933 -0.6138716 -0.2764856 -0.08340293 0.10217794
#> delta[12,2] -0.19799537 0.3318056 -0.7679960 -0.4374009 -0.22777140 0.02932860
#> delta[13,2] -1.00174973 0.4087018 -1.8306606 -1.2616452 -1.00849746 -0.74547967
#> delta[14,2] -0.11429727 0.1956355 -0.5263633 -0.2424923 -0.10480327 0.01747769
#> delta[15,2] 0.04260936 0.2440289 -0.4658495 -0.1183496 0.05338254 0.20964446
#> delta[16,2] -0.35114501 0.1350339 -0.6082381 -0.4422329 -0.35289596 -0.26237493
#> delta[17,2] -0.62022687 0.2714952 -1.2030210 -0.7734245 -0.61272807 -0.43873494
#> delta[18,2] -0.57270486 0.3021451 -1.1691907 -0.7586894 -0.57280096 -0.39846036
#> delta[19,2] -0.55376438 0.3319558 -1.1781687 -0.7620615 -0.55757814 -0.35587001
#> delta[20,2] -0.43363439 0.2240522 -0.8701833 -0.5921158 -0.43263091 -0.28222668
#> delta[21,2] -0.58738616 0.2145471 -1.0384818 -0.7216971 -0.57938474 -0.43913582
#> delta[9,3] -0.57551145 0.1913594 -0.9606819 -0.7016396 -0.57360520 -0.44480359
#> delta[10,3] -0.51557133 0.3121005 -1.1159240 -0.7078375 -0.52737107 -0.34070904
#> delta[12,3] -0.39525223 0.3018513 -0.9338872 -0.5849580 -0.42114774 -0.23019241
#> delta[13,3] -0.71797138 0.4112892 -1.6281570 -0.9815761 -0.68935699 -0.44485897
#> delta[19,3] -0.52232202 0.2681658 -1.0837366 -0.6789361 -0.51155399 -0.34622689
#> delta[10,4] -0.51571395 0.2482143 -1.0333548 -0.6694216 -0.51443932 -0.34303701
#> delta[12,4] -0.37081619 0.2354454 -0.8090300 -0.5339618 -0.38591251 -0.22017771
#> delta[13,4] -0.13511926 0.3082854 -0.7838225 -0.3441567 -0.09722973 0.09196899
#> 97.5% Rhat n.eff
#> delta[1,2] 0.43760712 1.085278 32
#> delta[2,2] 0.33651124 1.111735 23
#> delta[3,2] -0.11458314 1.018275 320
#> delta[4,2] -0.14854008 1.003847 2500
#> delta[5,2] -0.02148006 1.029304 360
#> delta[6,2] 0.06067270 1.019850 210
#> delta[7,2] -0.14514264 1.014450 1600
#> delta[8,2] -0.06389999 1.027682 140
#> delta[9,2] -0.07491692 1.024779 320
#> delta[10,2] 0.67052254 1.150931 19
#> delta[11,2] 0.33466107 1.115638 25
#> delta[12,2] 0.48189918 1.153628 18
#> delta[13,2] -0.16566961 1.096418 33
#> delta[14,2] 0.25136207 1.027155 81
#> delta[15,2] 0.50421575 1.093609 30
#> delta[16,2] -0.08046684 1.006233 930
#> delta[17,2] -0.12148618 1.072150 40
#> delta[18,2] 0.06176846 1.018599 960
#> delta[19,2] 0.12997628 1.019847 2600
#> delta[20,2] -0.00868698 1.044186 70
#> delta[21,2] -0.19607312 1.011896 290
#> delta[9,3] -0.19669213 1.021363 280
#> delta[10,3] 0.19438038 1.037480 390
#> delta[12,3] 0.27744021 1.036949 140
#> delta[13,3] 0.02707925 1.074299 51
#> delta[19,3] -0.04421840 1.029426 430
#> delta[10,4] -0.05803979 1.054922 140
#> delta[12,4] 0.14045257 1.049369 59
#> delta[13,4] 0.36382253 1.132654 22
#>
#> $heter_prior
#> [1] 0 1 1
#>
#> $SUCRA
#> mean sd 2.5% 25% 50% 75% 97.5%
#> SUCRA[1] 0.1230000 0.1203099 0.0000000 0.0000000 0.1428571 0.1428571 0.4285714
#> SUCRA[2] 0.8791429 0.2075278 0.2857143 0.8571429 1.0000000 1.0000000 1.0000000
#> SUCRA[3] 0.7030000 0.2757550 0.0000000 0.5714286 0.8571429 0.8571429 1.0000000
#> SUCRA[4] 0.3462857 0.2422618 0.0000000 0.1428571 0.2857143 0.4285714 0.8571429
#> SUCRA[5] 0.6027143 0.2501997 0.1428571 0.4285714 0.5714286 0.8571429 1.0000000
#> SUCRA[6] 0.1427619 0.1607225 0.0000000 0.0000000 0.1428571 0.2857143 0.5714286
#> SUCRA[7] 0.5610952 0.1755616 0.2857143 0.4285714 0.5714286 0.7142857 0.8571429
#> SUCRA[8] 0.6420000 0.1815187 0.2857143 0.5714286 0.7142857 0.7142857 1.0000000
#> Rhat n.eff
#> SUCRA[1] 1.071489 51
#> SUCRA[2] 1.053231 76
#> SUCRA[3] 1.020851 130
#> SUCRA[4] 1.072432 32
#> SUCRA[5] 1.006562 330
#> SUCRA[6] 1.129914 24
#> SUCRA[7] 1.020369 110
#> SUCRA[8] 1.071823 33
#>
#> $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] 0.6110000000 0.48760461 0 0 1 1 1 1.012093
#> effectiveness[3,1] 0.2240000000 0.41699156 0 0 0 0 1 1.001654
#> effectiveness[4,1] 0.0083333333 0.09092109 0 0 0 0 0 1.107615
#> effectiveness[5,1] 0.0803333333 0.27185386 0 0 0 0 1 1.046365
#> effectiveness[6,1] 0.0013333333 0.03649657 0 0 0 0 0 1.292577
#> effectiveness[7,1] 0.0170000000 0.12929258 0 0 0 0 0 1.107453
#> effectiveness[8,1] 0.0580000000 0.23378242 0 0 0 0 1 1.064789
#> effectiveness[1,2] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> effectiveness[2,2] 0.2096666667 0.40713856 0 0 0 0 1 1.003097
#> effectiveness[3,2] 0.3046666667 0.46034284 0 0 0 1 1 1.012982
#> effectiveness[4,2] 0.0443333333 0.20586893 0 0 0 0 1 1.028807
#> effectiveness[5,2] 0.1966666667 0.39754442 0 0 0 0 1 1.000567
#> effectiveness[6,2] 0.0016666667 0.04079759 0 0 0 0 0 1.189187
#> effectiveness[7,2] 0.0856666667 0.27991786 0 0 0 0 1 1.026792
#> effectiveness[8,2] 0.1573333333 0.36417546 0 0 0 0 1 1.063245
#> effectiveness[1,3] 0.0010000000 0.03161223 0 0 0 0 0 1.292018
#> effectiveness[2,3] 0.0553333333 0.22866785 0 0 0 0 1 1.046989
#> effectiveness[3,3] 0.1216666667 0.32695492 0 0 0 0 1 1.007121
#> effectiveness[4,3] 0.0886666667 0.28430940 0 0 0 0 1 1.026068
#> effectiveness[5,3] 0.2196666667 0.41408982 0 0 0 0 1 1.002310
#> effectiveness[6,3] 0.0073333333 0.08533454 0 0 0 0 0 1.120680
#> effectiveness[7,3] 0.2170000000 0.41227134 0 0 0 0 1 1.001806
#> effectiveness[8,3] 0.2893333333 0.45352852 0 0 0 1 1 1.006567
#> effectiveness[1,4] 0.0026666667 0.05157948 0 0 0 0 0 1.294827
#> effectiveness[2,4] 0.0373333333 0.18960891 0 0 0 0 1 1.039633
#> effectiveness[3,4] 0.1063333333 0.30831517 0 0 0 0 1 1.032325
#> effectiveness[4,4] 0.1040000000 0.30531143 0 0 0 0 1 1.037858
#> effectiveness[5,4] 0.1453333333 0.35249535 0 0 0 0 1 1.008628
#> effectiveness[6,4] 0.0240000000 0.15307453 0 0 0 0 0 1.071314
#> effectiveness[7,4] 0.3050000000 0.46048418 0 0 0 1 1 1.002362
#> effectiveness[8,4] 0.2753333333 0.44675655 0 0 0 1 1 1.005782
#> effectiveness[1,5] 0.0343333333 0.18211428 0 0 0 0 1 1.128849
#> effectiveness[2,5] 0.0470000000 0.21167413 0 0 0 0 1 1.071537
#> effectiveness[3,5] 0.1040000000 0.30531143 0 0 0 0 1 1.042713
#> effectiveness[4,5] 0.1753333333 0.38031535 0 0 0 0 1 1.006315
#> effectiveness[5,5] 0.1653333333 0.37154305 0 0 0 0 1 1.003547
#> effectiveness[6,5] 0.0676666667 0.25121490 0 0 0 0 1 1.165340
#> effectiveness[7,5] 0.2476666667 0.43172910 0 0 0 0 1 1.015329
#> effectiveness[8,5] 0.1586666667 0.36542587 0 0 0 0 1 1.039602
#> effectiveness[1,6] 0.1656666667 0.37184313 0 0 0 0 1 1.096627
#> effectiveness[2,6] 0.0193333333 0.13771666 0 0 0 0 0 1.039942
#> effectiveness[3,6] 0.0693333333 0.25406247 0 0 0 0 1 1.004379
#> effectiveness[4,6] 0.2866666667 0.45227986 0 0 0 1 1 1.001648
#> effectiveness[5,6] 0.1220000000 0.32734037 0 0 0 0 1 1.077560
#> effectiveness[6,6] 0.1590000000 0.36573705 0 0 0 0 1 1.012850
#> effectiveness[7,6] 0.1193333333 0.32423438 0 0 0 0 1 1.017284
#> effectiveness[8,6] 0.0586666667 0.23503894 0 0 0 0 1 1.059141
#> effectiveness[1,7] 0.4110000000 0.49209727 0 0 0 1 1 1.067836
#> effectiveness[2,7] 0.0133333333 0.11471679 0 0 0 0 0 1.070838
#> effectiveness[3,7] 0.0406666667 0.19754973 0 0 0 0 1 1.056379
#> effectiveness[4,7] 0.1410000000 0.34807957 0 0 0 0 1 1.042102
#> effectiveness[5,7] 0.0570000000 0.23188127 0 0 0 0 1 1.023654
#> effectiveness[6,7] 0.3263333333 0.46894903 0 0 0 1 1 1.008905
#> effectiveness[7,7] 0.0080000000 0.08909908 0 0 0 0 0 1.115065
#> effectiveness[8,7] 0.0026666667 0.05157948 0 0 0 0 0 1.088408
#> effectiveness[1,8] 0.3853333333 0.48675511 0 0 0 1 1 1.053983
#> effectiveness[2,8] 0.0070000000 0.08338656 0 0 0 0 0 1.197953
#> effectiveness[3,8] 0.0293333333 0.16876725 0 0 0 0 1 1.039539
#> effectiveness[4,8] 0.1516666667 0.35875729 0 0 0 0 1 1.062882
#> effectiveness[5,8] 0.0136666667 0.11612228 0 0 0 0 0 1.019439
#> effectiveness[6,8] 0.4126666667 0.49239588 0 0 0 1 1 1.076706
#> effectiveness[7,8] 0.0003333333 0.01825742 0 0 0 0 0 1.290904
#> effectiveness[8,8] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> n.eff
#> effectiveness[1,1] 1
#> effectiveness[2,1] 170
#> effectiveness[3,1] 1700
#> effectiveness[4,1] 470
#> effectiveness[5,1] 140
#> effectiveness[6,1] 750
#> effectiveness[7,1] 240
#> effectiveness[8,1] 130
#> effectiveness[1,2] 1
#> effectiveness[2,2] 840
#> effectiveness[3,2] 180
#> effectiveness[4,2] 390
#> effectiveness[5,2] 3000
#> effectiveness[6,2] 1200
#> effectiveness[7,2] 230
#> effectiveness[8,2] 61
#> effectiveness[1,3] 1000
#> effectiveness[2,3] 190
#> effectiveness[3,3] 620
#> effectiveness[4,3] 230
#> effectiveness[5,3] 1100
#> effectiveness[6,3] 470
#> effectiveness[7,3] 1500
#> effectiveness[8,3] 350
#> effectiveness[1,4] 370
#> effectiveness[2,4] 330
#> effectiveness[3,4] 160
#> effectiveness[4,4] 140
#> effectiveness[5,4] 440
#> effectiveness[6,4] 270
#> effectiveness[7,4] 1100
#> effectiveness[8,4] 400
#> effectiveness[1,5] 100
#> effectiveness[2,5] 150
#> effectiveness[3,5] 120
#> effectiveness[4,5] 510
#> effectiveness[5,5] 900
#> effectiveness[6,5] 43
#> effectiveness[7,5] 170
#> effectiveness[8,5] 96
#> effectiveness[1,6] 39
#> effectiveness[2,6] 620
#> effectiveness[3,6] 1600
#> effectiveness[4,6] 1700
#> effectiveness[5,6] 60
#> effectiveness[6,6] 280
#> effectiveness[7,6] 260
#> effectiveness[8,6] 140
#> effectiveness[1,7] 35
#> effectiveness[2,7] 480
#> effectiveness[3,7] 210
#> effectiveness[4,7] 96
#> effectiveness[5,7] 370
#> effectiveness[6,7] 250
#> effectiveness[7,7] 460
#> effectiveness[8,7] 1800
#> effectiveness[1,8] 44
#> effectiveness[2,8] 260
#> effectiveness[3,8] 420
#> effectiveness[4,8] 62
#> effectiveness[5,8] 1800
#> effectiveness[6,8] 31
#> effectiveness[7,8] 3000
#> effectiveness[8,8] 1
#>
#> $abs_risk
#> mean sd 2.5% 25% 50% 75%
#> abs_risk[1] 0.3916667 0.00000000 0.39166667 0.3916667 0.3916667 0.3916667
#> abs_risk[2] 0.2088031 0.07097138 0.09885475 0.1586033 0.1987535 0.2474813
#> abs_risk[3] 0.2578731 0.07894550 0.11744140 0.2011504 0.2534021 0.3065802
#> abs_risk[4] 0.3389695 0.07255406 0.22478322 0.2856439 0.3284934 0.3866303
#> abs_risk[5] 0.2816933 0.05742292 0.18360515 0.2448308 0.2738741 0.3127171
#> abs_risk[6] 0.3878941 0.05749216 0.27050497 0.3474270 0.3913537 0.4317447
#> abs_risk[7] 0.2895043 0.03738017 0.22509005 0.2620931 0.2856642 0.3139350
#> abs_risk[8] 0.2759401 0.03236339 0.21473431 0.2541979 0.2750576 0.2986384
#> 97.5% Rhat n.eff
#> abs_risk[1] 0.3916667 1.000000 1
#> abs_risk[2] 0.3798613 1.085941 34
#> abs_risk[3] 0.4236047 1.068186 52
#> abs_risk[4] 0.4994336 1.128924 21
#> abs_risk[5] 0.4230378 1.024433 730
#> abs_risk[6] 0.4891279 1.140591 22
#> abs_risk[7] 0.3710775 1.040769 120
#> abs_risk[8] 0.3410702 1.024887 310
#>
#> $base_risk
#> [1] 0.3916667
#>
#> attr(,"class")
#> [1] "run_model"
# }