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
)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.
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.
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
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: 2596
#>
#> Initializing model
#>
#> ... Updating the model until convergence
#> $EM
#> mean sd 2.5% 25% 50% 75%
#> EM[2,1] -1.08356577 0.4134275 -1.85517734 -1.39396693 -1.08301970 -0.771128029
#> EM[3,1] -0.76780323 0.3950076 -1.50458195 -1.04253244 -0.78918749 -0.489777892
#> EM[4,1] -0.35768059 0.2676751 -0.82674772 -0.53422407 -0.37301852 -0.193287387
#> EM[5,1] -0.46810429 0.3036987 -1.04915425 -0.70451723 -0.46158698 -0.226013390
#> EM[6,1] -0.16384500 0.2432555 -0.67836556 -0.31371809 -0.15772696 0.008266464
#> EM[7,1] -0.44795818 0.1631749 -0.76194361 -0.56310252 -0.44954775 -0.328996523
#> EM[8,1] -0.52183475 0.1424579 -0.79622997 -0.62157268 -0.52587720 -0.408946047
#> EM[3,2] 0.31576254 0.4429617 -0.56562623 0.01181802 0.30736298 0.631626576
#> EM[4,2] 0.72588517 0.4525660 -0.05966864 0.39689776 0.68615276 1.030211089
#> EM[5,2] 0.61546148 0.5168517 -0.41430963 0.27142292 0.59479238 0.986640219
#> EM[6,2] 0.91972077 0.4719601 0.01570235 0.57069761 0.92715167 1.230362926
#> EM[7,2] 0.63560759 0.4044169 -0.20867169 0.37524977 0.63196620 0.928151762
#> EM[8,2] 0.56173102 0.4073165 -0.28257036 0.30524133 0.55203085 0.837381271
#> EM[4,3] 0.41012264 0.4194597 -0.36550531 0.08723769 0.43992636 0.702858118
#> EM[5,3] 0.29969895 0.4862564 -0.57838624 -0.06357009 0.26663856 0.625186669
#> EM[6,3] 0.60395824 0.4631669 -0.35878088 0.30214562 0.62284596 0.930455645
#> EM[7,3] 0.31984505 0.3747346 -0.41814273 0.05668522 0.33323152 0.578874077
#> EM[8,3] 0.24596849 0.3814765 -0.51775839 -0.02190225 0.25534068 0.509264951
#> EM[5,4] -0.11042369 0.4102821 -0.83969933 -0.42329027 -0.10951213 0.177771285
#> EM[6,4] 0.19383560 0.3412380 -0.47250122 -0.03800298 0.17914769 0.429708596
#> EM[7,4] -0.09027759 0.2801199 -0.70744887 -0.27142394 -0.05698246 0.113370877
#> EM[8,4] -0.16415415 0.2850610 -0.80669580 -0.35994492 -0.12567745 0.036177831
#> EM[6,5] 0.30425929 0.3481205 -0.33696876 0.05116941 0.31130131 0.553569623
#> EM[7,5] 0.02014610 0.3163163 -0.56203541 -0.22343510 0.03592213 0.241313059
#> EM[8,5] -0.05373046 0.3090578 -0.61209270 -0.29400498 -0.03620529 0.189363754
#> EM[7,6] -0.28411319 0.2876700 -0.85174973 -0.49111331 -0.28483508 -0.075651802
#> EM[8,6] -0.35798975 0.2659676 -0.90587825 -0.53541822 -0.35966606 -0.178580954
#> EM[8,7] -0.07387656 0.1526641 -0.37883065 -0.17498151 -0.07639117 0.019376770
#> 97.5% Rhat n.eff
#> EM[2,1] -0.29185159 1.010676 200
#> EM[3,1] -0.02457772 1.083741 31
#> EM[4,1] 0.21371604 1.034842 710
#> EM[5,1] 0.05960768 1.111555 23
#> EM[6,1] 0.28060349 1.059770 48
#> EM[7,1] -0.14741517 1.215029 14
#> EM[8,1] -0.25226692 1.084618 30
#> EM[3,2] 1.19450501 1.031636 68
#> EM[4,2] 1.67003405 1.026768 130
#> EM[5,2] 1.63348596 1.038147 110
#> EM[6,2] 1.84883306 1.046505 62
#> EM[7,2] 1.37118786 1.014463 320
#> EM[8,2] 1.34704273 1.006925 1700
#> EM[4,3] 1.17785784 1.088546 28
#> EM[5,3] 1.31495730 1.127278 25
#> EM[6,3] 1.43763597 1.081337 32
#> EM[7,3] 1.02427671 1.015672 140
#> EM[8,3] 0.98981057 1.035783 62
#> EM[5,4] 0.64825338 1.080247 36
#> EM[6,4] 0.83674991 1.042978 120
#> EM[7,4] 0.37837288 1.108166 26
#> EM[8,4] 0.31350617 1.061332 62
#> EM[6,5] 0.95904631 1.218532 13
#> EM[7,5] 0.61201202 1.123837 22
#> EM[8,5] 0.48534353 1.093934 27
#> EM[7,6] 0.22583522 1.142103 19
#> EM[8,6] 0.16079630 1.098612 26
#> EM[8,7] 0.22652460 1.044024 62
#>
#> $dev_o
#> mean sd 2.5% 25% 50% 75%
#> dev.o[1,1] 2.3595453 2.4879537 0.0061475717 0.53521827 1.6599339 3.4396319
#> dev.o[2,1] 0.7869031 1.1054362 0.0009774747 0.07315485 0.3574974 1.0555491
#> dev.o[3,1] 1.2049459 1.4972431 0.0010541789 0.14027180 0.6403530 1.7443521
#> dev.o[4,1] 0.6943490 1.0689509 0.0008403616 0.06544637 0.2924322 0.8787675
#> dev.o[5,1] 0.7288581 1.0233459 0.0008195145 0.07309802 0.3392500 0.9780301
#> dev.o[6,1] 0.9126697 1.2107064 0.0011193771 0.09776983 0.4469389 1.2407489
#> dev.o[7,1] 0.7232231 1.0471315 0.0005334526 0.06587194 0.3426080 0.9242639
#> dev.o[8,1] 0.6954469 0.9771132 0.0006512589 0.07319712 0.3103599 0.8873605
#> dev.o[9,1] 0.7455268 0.9865272 0.0010541278 0.08778283 0.3594195 1.0140124
#> dev.o[10,1] 0.5864596 0.8831695 0.0005206356 0.05212638 0.2360605 0.7571391
#> dev.o[11,1] 0.9247128 1.2958548 0.0011394954 0.09585769 0.4192176 1.2026921
#> dev.o[12,1] 1.3524077 1.4761989 0.0024856974 0.21767768 0.8411543 2.0167711
#> dev.o[13,1] 1.2210623 1.5235417 0.0014941421 0.15059845 0.6186794 1.6966180
#> dev.o[14,1] 0.8967458 1.1904468 0.0008260939 0.10411622 0.4674457 1.2334310
#> dev.o[15,1] 1.1071601 1.5046946 0.0013659341 0.13145848 0.5337654 1.5079248
#> dev.o[16,1] 1.5236325 1.9505102 0.0021325952 0.19119479 0.7803749 2.0981890
#> dev.o[17,1] 2.2847252 2.4818372 0.0052327117 0.39806748 1.4250930 3.3449272
#> dev.o[18,1] 1.1424774 1.5709462 0.0010359639 0.12183543 0.5307297 1.5394803
#> dev.o[19,1] 1.6851581 1.7416886 0.0045924229 0.35334638 1.1357949 2.4587850
#> dev.o[20,1] 0.7250381 1.0280016 0.0004627470 0.06956439 0.3279362 0.9425122
#> dev.o[21,1] 1.0898010 1.5086706 0.0009043592 0.11622786 0.5130651 1.4539702
#> dev.o[1,2] 3.1887063 1.9334222 0.6307041515 1.73076501 2.8439517 4.1948526
#> dev.o[2,2] 0.7981675 1.1179932 0.0010506216 0.08235215 0.3590675 1.0657616
#> dev.o[3,2] 1.1892850 1.5054337 0.0011917280 0.14815460 0.6372120 1.6692379
#> dev.o[4,2] 0.7587825 1.0487330 0.0007346132 0.07618996 0.3434236 1.0484265
#> dev.o[5,2] 0.6501080 0.9316078 0.0005491074 0.06620975 0.2924598 0.8866004
#> dev.o[6,2] 0.9662009 1.3138392 0.0010614747 0.10823103 0.4626961 1.2914926
#> dev.o[7,2] 0.8390667 1.1983985 0.0007629653 0.08634170 0.3770590 1.1030322
#> dev.o[8,2] 0.6304144 0.8823872 0.0006741771 0.06642348 0.2994503 0.8246915
#> dev.o[9,2] 0.6452567 0.9138424 0.0005190007 0.05898926 0.2845138 0.8344876
#> dev.o[10,2] 1.6223357 1.9203324 0.0023517832 0.20532481 0.9117656 2.3688636
#> dev.o[11,2] 0.9633826 1.3877900 0.0005942917 0.10024965 0.4273175 1.2704482
#> dev.o[12,2] 0.7959089 1.1456262 0.0005503570 0.07998954 0.3491108 0.9958017
#> dev.o[13,2] 1.0070397 1.4250089 0.0012321073 0.10004669 0.4524245 1.3249341
#> dev.o[14,2] 0.8000532 1.1102697 0.0010312894 0.08088683 0.3597813 1.0638137
#> dev.o[15,2] 1.1983059 1.5904842 0.0020142857 0.15295949 0.6160505 1.6242247
#> dev.o[16,2] 1.5323485 1.9679823 0.0019403368 0.18865162 0.7905705 2.1085405
#> dev.o[17,2] 2.6001708 2.2455649 0.0391263675 0.88955415 2.0301707 3.7581939
#> dev.o[18,2] 1.0312410 1.3450017 0.0013665919 0.11218881 0.5465865 1.4493747
#> dev.o[19,2] 0.4278334 0.6151845 0.0003167565 0.03980365 0.1910982 0.5457929
#> dev.o[20,2] 0.6934293 0.9423857 0.0013155656 0.07342778 0.3354219 0.9642190
#> dev.o[21,2] 1.0680006 1.3924807 0.0014959534 0.12969690 0.5440334 1.4957087
#> dev.o[9,3] 0.8031434 1.1110664 0.0009339477 0.08043373 0.3650001 1.0444308
#> dev.o[10,3] 0.6859092 0.9695806 0.0005318669 0.06743659 0.3023108 0.9225433
#> dev.o[12,3] 1.1109131 1.4336065 0.0015326213 0.13284053 0.5602833 1.5227698
#> dev.o[13,3] 0.9877133 1.3973112 0.0006511930 0.08567996 0.4345929 1.3025425
#> dev.o[19,3] 1.3359981 1.2045092 0.0214585313 0.46385165 1.0154903 1.9067441
#> dev.o[10,4] 1.2344793 1.5032964 0.0017410188 0.17507505 0.7081806 1.7551562
#> dev.o[12,4] 0.8037942 1.0842771 0.0011030756 0.09452639 0.3892033 1.0644325
#> dev.o[13,4] 1.2890297 1.6840654 0.0013061326 0.14999724 0.6594104 1.7575939
#> 97.5% Rhat n.eff
#> dev.o[1,1] 8.528505 1.000931 3000
#> dev.o[2,1] 3.979959 1.001939 1400
#> dev.o[3,1] 5.163079 1.015317 230
#> dev.o[4,1] 3.629947 1.001432 2100
#> dev.o[5,1] 3.725363 1.000577 3000
#> dev.o[6,1] 4.327977 1.004313 530
#> dev.o[7,1] 3.779407 1.000869 3000
#> dev.o[8,1] 3.507480 1.000866 3000
#> dev.o[9,1] 3.568125 1.007101 360
#> dev.o[10,1] 3.195597 1.003261 910
#> dev.o[11,1] 4.906933 1.002744 890
#> dev.o[12,1] 5.154938 1.030220 93
#> dev.o[13,1] 5.520087 1.004064 870
#> dev.o[14,1] 4.227254 1.000742 3000
#> dev.o[15,1] 5.277548 1.002004 1700
#> dev.o[16,1] 7.315382 1.003684 1100
#> dev.o[17,1] 8.983146 1.022521 110
#> dev.o[18,1] 5.557218 1.004387 520
#> dev.o[19,1] 6.371501 1.001711 1700
#> dev.o[20,1] 3.626120 1.001339 2400
#> dev.o[21,1] 5.669149 1.001690 1700
#> dev.o[1,2] 7.972489 1.006945 1400
#> dev.o[2,2] 4.144985 1.004306 660
#> dev.o[3,2] 5.359176 1.009050 240
#> dev.o[4,2] 3.816099 1.003037 1100
#> dev.o[5,2] 3.172401 1.000558 3000
#> dev.o[6,2] 4.879069 1.003060 780
#> dev.o[7,2] 4.038718 1.001945 1400
#> dev.o[8,2] 3.238918 1.002819 860
#> dev.o[9,2] 3.296262 1.002725 900
#> dev.o[10,2] 6.822178 1.001521 2100
#> dev.o[11,2] 5.043810 1.000774 3000
#> dev.o[12,2] 4.187402 1.003197 1200
#> dev.o[13,2] 4.989039 1.001154 3000
#> dev.o[14,2] 3.959716 1.000714 3000
#> dev.o[15,2] 5.440927 1.002556 970
#> dev.o[16,2] 7.143053 1.003424 740
#> dev.o[17,2] 8.393960 1.010732 230
#> dev.o[18,2] 4.738652 1.016503 130
#> dev.o[19,2] 2.258729 1.004934 450
#> dev.o[20,2] 3.228714 1.001538 1900
#> dev.o[21,2] 5.019969 1.004937 450
#> dev.o[9,3] 3.895831 1.002127 1200
#> dev.o[10,3] 3.290821 1.000652 3000
#> dev.o[12,3] 4.992169 1.006601 330
#> dev.o[13,3] 5.104206 1.001215 2800
#> dev.o[19,3] 4.471436 1.012127 170
#> dev.o[10,4] 5.313332 1.009190 370
#> dev.o[12,4] 3.940318 1.000999 3000
#> dev.o[13,4] 6.027898 1.001524 2000
#>
#> $hat_par
#> mean sd 2.5% 25% 50%
#> hat.par[1,1] 1.588308 0.7842345 0.3690817 0.9806083 1.481083
#> hat.par[2,1] 49.915048 4.5062949 41.2083772 46.9050813 49.984895
#> hat.par[3,1] 43.287557 4.3461301 35.3528515 40.2168180 43.154866
#> hat.par[4,1] 42.264524 4.5809089 33.7539984 39.0444391 42.021593
#> hat.par[5,1] 17.053496 2.4970547 12.1679003 15.3054571 17.014113
#> hat.par[6,1] 43.654943 4.0554506 35.5296481 40.9596246 43.668190
#> hat.par[7,1] 157.126072 7.1697773 142.6176902 152.3786412 156.989648
#> hat.par[8,1] 68.316982 5.4015526 57.7072273 64.5652326 68.319077
#> hat.par[9,1] 89.507471 4.9058160 80.1943389 86.2552140 89.335900
#> hat.par[10,1] 78.518227 3.6827288 71.1176017 76.2411922 78.615760
#> hat.par[11,1] 72.753380 5.6071198 61.4006606 69.0351933 72.706356
#> hat.par[12,1] 77.815889 4.0786463 69.6769072 75.0793957 77.955134
#> hat.par[13,1] 49.260775 4.4660298 40.7316684 46.2708354 49.306784
#> hat.par[14,1] 33.684127 4.5083847 25.7169650 30.6060589 33.369106
#> hat.par[15,1] 37.385899 4.7245596 28.4460978 34.1429893 37.236089
#> hat.par[16,1] 306.296415 13.5842040 280.2877692 297.0199736 306.291243
#> hat.par[17,1] 10.457420 2.5720079 5.9392943 8.6040624 10.328557
#> hat.par[18,1] 21.818390 3.3904760 15.4013457 19.4604804 21.778221
#> hat.par[19,1] 3.988104 1.3583922 1.7984224 3.0116964 3.832037
#> hat.par[20,1] 25.270464 3.5834962 18.5408700 22.8586773 25.141514
#> hat.par[21,1] 32.091813 4.2820987 23.7528732 29.1361995 31.911754
#> hat.par[1,2] 1.399704 0.7471643 0.3092175 0.8202201 1.302766
#> hat.par[2,2] 46.055238 4.6746072 37.0157226 42.8572043 46.009008
#> hat.par[3,2] 31.638046 3.9535004 24.0439777 28.9178211 31.582194
#> hat.par[4,2] 43.908168 5.1110603 34.4835335 40.4151947 43.673062
#> hat.par[5,2] 12.004034 2.1396041 8.0430032 10.4926945 11.892415
#> hat.par[6,2] 35.176926 3.8057335 27.5951282 32.6659991 35.155499
#> hat.par[7,2] 196.819068 9.8092409 177.8769326 190.1657559 196.788064
#> hat.par[8,2] 51.683835 4.7910382 42.5606036 48.3576603 51.562704
#> hat.par[9,2] 81.952913 5.5028786 71.4075376 78.1786553 82.002560
#> hat.par[10,2] 72.865242 3.9595704 64.9573500 70.1982077 73.010466
#> hat.par[11,2] 119.271026 8.3057013 103.6341366 113.6310303 119.041686
#> hat.par[12,2] 80.047824 4.9188298 70.1591275 76.7788921 80.114645
#> hat.par[13,2] 25.673719 4.5146013 17.3398221 22.6040777 25.459968
#> hat.par[14,2] 32.184060 4.3702293 23.7000522 29.1710146 32.090022
#> hat.par[15,2] 31.770697 4.4386184 23.8108025 28.7169746 31.537110
#> hat.par[16,2] 245.281356 12.9702488 220.3935982 236.2485394 245.034312
#> hat.par[17,2] 7.567388 2.0277433 4.1179218 6.0902718 7.368195
#> hat.par[18,2] 13.161623 2.5153262 8.6340149 11.3438325 13.051873
#> hat.par[19,2] 2.524509 0.9560512 1.0639702 1.8259296 2.371632
#> hat.par[20,2] 18.827248 3.0262538 13.5619473 16.7460748 18.670465
#> hat.par[21,2] 22.100714 3.4800623 15.7328843 19.7123403 21.951405
#> hat.par[9,3] 79.940463 5.4715570 69.3906807 76.3333563 79.801474
#> hat.par[10,3] 68.783361 4.1950974 60.3021730 66.0620605 68.870677
#> hat.par[12,3] 67.507023 4.7168826 58.5523131 64.3355205 67.408580
#> hat.par[13,3] 35.106040 5.1293539 25.2206609 31.6617570 34.871076
#> hat.par[19,3] 2.484442 0.9162668 1.1304128 1.8302979 2.343172
#> hat.par[10,4] 67.182071 4.0726492 58.9675825 64.5130301 67.237722
#> hat.par[12,4] 62.641838 4.2915545 54.3071716 59.7770894 62.582106
#> hat.par[13,4] 41.866085 4.8204451 32.4936470 38.5522003 41.765999
#> 75% 97.5% Rhat n.eff
#> hat.par[1,1] 2.096001 3.297560 1.010109 640
#> hat.par[2,1] 52.946650 58.544593 1.004563 590
#> hat.par[3,1] 46.270138 51.948100 1.020294 100
#> hat.par[4,1] 45.243676 51.796554 1.007339 330
#> hat.par[5,1] 18.709540 22.057966 1.005875 370
#> hat.par[6,1] 46.333030 51.499535 1.003616 1900
#> hat.par[7,1] 162.142260 171.236697 1.014188 150
#> hat.par[8,1] 71.867322 79.053228 1.006103 360
#> hat.par[9,1] 92.739287 99.542818 1.039857 56
#> hat.par[10,1] 80.962099 85.604137 1.008900 490
#> hat.par[11,1] 76.585175 83.798685 1.002515 990
#> hat.par[12,1] 80.709443 85.185774 1.039413 60
#> hat.par[13,1] 52.230180 57.949802 1.005927 450
#> hat.par[14,1] 36.474856 43.718159 1.001340 2400
#> hat.par[15,1] 40.441786 47.054721 1.010971 200
#> hat.par[16,1] 315.392398 333.548646 1.007115 420
#> hat.par[17,1] 12.136335 15.866731 1.033813 65
#> hat.par[18,1] 24.085935 28.825294 1.017231 120
#> hat.par[19,1] 4.781125 7.208238 1.004289 530
#> hat.par[20,1] 27.581500 32.621074 1.010527 200
#> hat.par[21,1] 34.898877 40.561816 1.004931 450
#> hat.par[1,2] 1.845009 3.139404 1.007857 1200
#> hat.par[2,2] 49.214628 55.637922 1.004544 1100
#> hat.par[3,2] 34.242354 39.691126 1.025505 84
#> hat.par[4,2] 47.162248 54.539999 1.006696 320
#> hat.par[5,2] 13.385703 16.259534 1.003433 680
#> hat.par[6,2] 37.646249 42.853542 1.004682 550
#> hat.par[7,2] 203.343605 217.109228 1.001094 3000
#> hat.par[8,2] 55.074874 61.403326 1.027980 78
#> hat.par[9,2] 85.577101 93.063370 1.011326 200
#> hat.par[10,2] 75.648303 80.212937 1.003658 640
#> hat.par[11,2] 124.691534 136.107836 1.005653 390
#> hat.par[12,2] 83.248159 89.790745 1.004042 570
#> hat.par[13,2] 28.558196 35.053639 1.007580 280
#> hat.par[14,2] 35.021873 41.320498 1.001550 1900
#> hat.par[15,2] 34.542548 40.952776 1.006551 330
#> hat.par[16,2] 254.063149 271.506491 1.003805 620
#> hat.par[17,2] 8.874005 12.067509 1.015047 150
#> hat.par[18,2] 14.811180 18.385537 1.020099 110
#> hat.par[19,2] 3.056149 4.745964 1.015869 130
#> hat.par[20,2] 20.758200 25.289949 1.001974 1400
#> hat.par[21,2] 24.355034 29.452245 1.013336 160
#> hat.par[9,3] 83.475999 90.462181 1.005742 430
#> hat.par[10,3] 71.655088 76.750130 1.001289 2500
#> hat.par[12,3] 70.722452 76.891887 1.023234 90
#> hat.par[13,3] 38.471849 45.758885 1.017833 120
#> hat.par[19,3] 3.022787 4.650562 1.016481 130
#> hat.par[10,4] 69.930866 74.983116 1.009170 290
#> hat.par[12,4] 65.460803 70.974870 1.003301 720
#> hat.par[13,4] 44.991517 51.623390 1.005295 560
#>
#> $leverage_o
#> [1] 0.9620279 0.7444184 0.7126572 0.6439405 0.6408963 0.6363404 0.7058946
#> [8] 0.6930669 0.5924060 0.5765438 0.7983535 0.5819577 0.7273136 0.7195595
#> [15] 0.7099277 0.9620080 0.9782936 0.8288005 0.7331343 0.6413326 0.7682817
#> [22] 0.2341361 0.7593966 0.6154288 0.7265027 0.5280993 0.6375879 0.8271322
#> [29] 0.6277107 0.6452097 0.7272867 0.8958168 0.7619129 1.0019446 0.6305388
#> [36] 0.8121142 0.9922989 0.3625633 0.5546433 0.3055567 0.6079265 0.5513252
#> [43] 0.6216015 0.6631565 0.7165684 0.9872939 0.1478672 0.6130328 0.6178067
#> [50] 0.7654677
#>
#> $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] -0.13401388 0.5014454 -1.134329 -0.4722309 -0.128715724 0.2189310
#> phi[2] -0.14985614 0.9352908 -2.010423 -0.7616085 -0.120979913 0.4824477
#> phi[3] -0.01772129 0.9600138 -1.897949 -0.6604549 -0.006073649 0.6199807
#> phi[4] -1.20428940 0.8572177 -2.694767 -1.8104522 -1.263304757 -0.6464176
#> phi[5] -0.42105020 0.9623343 -2.157990 -1.0886378 -0.456509078 0.2034791
#> phi[6] 0.68509097 0.8703122 -1.249711 0.1490288 0.738350112 1.2617287
#> phi[7] -0.12473766 0.6623573 -1.465792 -0.5483722 -0.134840617 0.3169678
#> phi[8] -0.19807987 0.9135630 -1.940269 -0.8117067 -0.214115612 0.3992876
#> 97.5% Rhat n.eff
#> phi[1] 0.8163179 1.065718 48
#> phi[2] 1.6466632 1.002681 920
#> phi[3] 1.8224277 1.034415 64
#> phi[4] 0.6885693 1.038244 89
#> phi[5] 1.6447577 1.088786 30
#> phi[6] 2.2838079 1.072620 35
#> phi[7] 1.1642828 1.050984 45
#> phi[8] 1.6641696 1.006505 1700
#>
#> $model_assessment
#> DIC pD dev n_data
#> 1 89.34295 34.29508 55.04787 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] -1.084 0.413 -1.855 -1.394 -1.083 -0.771 -0.292
#> EM[3,1] -0.768 0.395 -1.505 -1.043 -0.789 -0.490 -0.025
#> EM[4,1] -0.358 0.268 -0.827 -0.534 -0.373 -0.193 0.214
#> EM[5,1] -0.468 0.304 -1.049 -0.705 -0.462 -0.226 0.060
#> EM[6,1] -0.164 0.243 -0.678 -0.314 -0.158 0.008 0.281
#> EM[7,1] -0.448 0.163 -0.762 -0.563 -0.450 -0.329 -0.147
#> EM[8,1] -0.522 0.142 -0.796 -0.622 -0.526 -0.409 -0.252
#> EM[3,2] 0.316 0.443 -0.566 0.012 0.307 0.632 1.195
#> EM[4,2] 0.726 0.453 -0.060 0.397 0.686 1.030 1.670
#> EM[5,2] 0.615 0.517 -0.414 0.271 0.595 0.987 1.633
#> EM[6,2] 0.920 0.472 0.016 0.571 0.927 1.230 1.849
#> EM[7,2] 0.636 0.404 -0.209 0.375 0.632 0.928 1.371
#> EM[8,2] 0.562 0.407 -0.283 0.305 0.552 0.837 1.347
#> EM[4,3] 0.410 0.419 -0.366 0.087 0.440 0.703 1.178
#> EM[5,3] 0.300 0.486 -0.578 -0.064 0.267 0.625 1.315
#> EM[6,3] 0.604 0.463 -0.359 0.302 0.623 0.930 1.438
#> EM[7,3] 0.320 0.375 -0.418 0.057 0.333 0.579 1.024
#> EM[8,3] 0.246 0.381 -0.518 -0.022 0.255 0.509 0.990
#> EM[5,4] -0.110 0.410 -0.840 -0.423 -0.110 0.178 0.648
#> EM[6,4] 0.194 0.341 -0.473 -0.038 0.179 0.430 0.837
#> EM[7,4] -0.090 0.280 -0.707 -0.271 -0.057 0.113 0.378
#> EM[8,4] -0.164 0.285 -0.807 -0.360 -0.126 0.036 0.314
#> EM[6,5] 0.304 0.348 -0.337 0.051 0.311 0.554 0.959
#> EM[7,5] 0.020 0.316 -0.562 -0.223 0.036 0.241 0.612
#> EM[8,5] -0.054 0.309 -0.612 -0.294 -0.036 0.189 0.485
#> EM[7,6] -0.284 0.288 -0.852 -0.491 -0.285 -0.076 0.226
#> EM[8,6] -0.358 0.266 -0.906 -0.535 -0.360 -0.179 0.161
#> EM[8,7] -0.074 0.153 -0.379 -0.175 -0.076 0.019 0.227
#> EM.pred[2,1] -1.085 0.437 -1.926 -1.405 -1.082 -0.763 -0.252
#> EM.pred[3,1] -0.763 0.425 -1.578 -1.056 -0.776 -0.472 0.039
#> EM.pred[4,1] -0.358 0.302 -0.899 -0.560 -0.379 -0.181 0.303
#> EM.pred[5,1] -0.466 0.343 -1.143 -0.712 -0.459 -0.204 0.105
#> EM.pred[6,1] -0.165 0.290 -0.791 -0.337 -0.158 0.026 0.377
#> EM.pred[7,1] -0.454 0.222 -0.915 -0.595 -0.445 -0.307 -0.016
#> EM.pred[8,1] -0.520 0.206 -0.961 -0.643 -0.505 -0.380 -0.154
#> EM.pred[3,2] 0.318 0.467 -0.628 0.006 0.316 0.642 1.266
#> EM.pred[4,2] 0.721 0.479 -0.122 0.385 0.688 1.042 1.717
#> EM.pred[5,2] 0.616 0.541 -0.484 0.260 0.605 0.996 1.663
#> EM.pred[6,2] 0.917 0.493 -0.045 0.564 0.916 1.240 1.874
#> EM.pred[7,2] 0.635 0.433 -0.265 0.364 0.626 0.944 1.441
#> EM.pred[8,2] 0.563 0.428 -0.302 0.297 0.559 0.847 1.385
#> EM.pred[4,3] 0.410 0.445 -0.420 0.067 0.437 0.726 1.257
#> EM.pred[5,3] 0.302 0.508 -0.644 -0.065 0.269 0.644 1.336
#> EM.pred[6,3] 0.598 0.487 -0.392 0.289 0.622 0.937 1.493
#> EM.pred[7,3] 0.322 0.400 -0.449 0.038 0.340 0.590 1.100
#> EM.pred[8,3] 0.249 0.410 -0.588 -0.030 0.273 0.518 1.063
#> EM.pred[5,4] -0.114 0.439 -0.949 -0.419 -0.109 0.197 0.681
#> EM.pred[6,4] 0.193 0.369 -0.543 -0.055 0.182 0.451 0.892
#> EM.pred[7,4] -0.089 0.318 -0.793 -0.278 -0.049 0.130 0.439
#> EM.pred[8,4] -0.166 0.324 -0.894 -0.376 -0.121 0.053 0.385
#> EM.pred[6,5] 0.305 0.383 -0.380 0.019 0.307 0.569 1.025
#> EM.pred[7,5] 0.020 0.352 -0.632 -0.238 0.032 0.265 0.708
#> EM.pred[8,5] -0.051 0.344 -0.672 -0.314 -0.049 0.203 0.593
#> EM.pred[7,6] -0.282 0.328 -0.951 -0.513 -0.281 -0.050 0.324
#> EM.pred[8,6] -0.356 0.307 -0.975 -0.545 -0.360 -0.153 0.229
#> EM.pred[8,7] -0.071 0.214 -0.543 -0.189 -0.069 0.054 0.346
#> SUCRA[1] 0.063 0.089 0.000 0.000 0.000 0.143 0.286
#> SUCRA[2] 0.924 0.164 0.429 0.857 1.000 1.000 1.000
#> SUCRA[3] 0.733 0.266 0.143 0.571 0.857 0.857 1.000
#> SUCRA[4] 0.422 0.243 0.000 0.286 0.429 0.571 0.857
#> SUCRA[5] 0.536 0.275 0.000 0.286 0.571 0.714 1.000
#> SUCRA[6] 0.235 0.216 0.000 0.143 0.143 0.286 0.857
#> SUCRA[7] 0.488 0.172 0.143 0.429 0.429 0.571 0.857
#> SUCRA[8] 0.599 0.169 0.286 0.429 0.571 0.714 0.857
#> abs_risk[1] 0.392 0.000 0.392 0.392 0.392 0.392 0.392
#> abs_risk[2] 0.187 0.062 0.091 0.138 0.179 0.229 0.325
#> abs_risk[3] 0.237 0.071 0.125 0.185 0.226 0.283 0.386
#> abs_risk[4] 0.313 0.058 0.220 0.274 0.307 0.347 0.444
#> abs_risk[5] 0.291 0.062 0.184 0.241 0.289 0.339 0.406
#> abs_risk[6] 0.355 0.054 0.246 0.320 0.355 0.394 0.460
#> abs_risk[7] 0.293 0.034 0.231 0.268 0.291 0.317 0.357
#> abs_risk[8] 0.277 0.028 0.225 0.257 0.276 0.300 0.333
#> 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] -0.388 0.290 -0.918 -0.576 -0.395 -0.206 0.228
#> delta[2,2] -0.389 0.256 -0.848 -0.566 -0.396 -0.221 0.154
#> delta[3,2] -0.508 0.195 -0.936 -0.631 -0.495 -0.365 -0.190
#> delta[4,2] -0.490 0.165 -0.818 -0.604 -0.484 -0.381 -0.177
#> delta[5,2] -0.461 0.204 -0.882 -0.589 -0.448 -0.321 -0.083
#> delta[6,2] -0.388 0.198 -0.754 -0.520 -0.390 -0.261 0.029
#> delta[7,2] -0.504 0.152 -0.808 -0.610 -0.501 -0.393 -0.224
#> delta[8,2] -0.427 0.174 -0.781 -0.538 -0.426 -0.306 -0.089
#> delta[9,2] -0.471 0.180 -0.848 -0.589 -0.463 -0.341 -0.168
#> delta[10,2] -0.318 0.308 -0.864 -0.534 -0.334 -0.133 0.348
#> delta[11,2] -0.206 0.248 -0.748 -0.364 -0.195 -0.030 0.242
#> delta[12,2] -0.328 0.285 -0.826 -0.527 -0.351 -0.157 0.290
#> delta[13,2] -1.124 0.392 -1.842 -1.416 -1.137 -0.819 -0.377
#> delta[14,2] -0.104 0.182 -0.491 -0.212 -0.103 0.012 0.244
#> delta[15,2] -0.107 0.246 -0.601 -0.263 -0.109 0.063 0.383
#> delta[16,2] -0.392 0.130 -0.647 -0.480 -0.391 -0.302 -0.136
#> delta[17,2] -0.419 0.287 -0.999 -0.607 -0.416 -0.226 0.141
#> delta[18,2] -0.501 0.317 -1.085 -0.737 -0.511 -0.247 0.065
#> delta[19,2] -0.495 0.333 -1.173 -0.734 -0.477 -0.231 0.064
#> delta[20,2] -0.427 0.204 -0.835 -0.562 -0.421 -0.287 -0.018
#> delta[21,2] -0.565 0.192 -0.975 -0.691 -0.553 -0.422 -0.235
#> delta[9,3] -0.564 0.171 -0.910 -0.679 -0.561 -0.435 -0.255
#> delta[10,3] -0.486 0.318 -1.113 -0.721 -0.479 -0.235 0.062
#> delta[12,3] -0.393 0.305 -0.964 -0.626 -0.377 -0.162 0.147
#> delta[13,3] -0.809 0.373 -1.510 -1.060 -0.834 -0.549 -0.099
#> delta[19,3] -0.484 0.222 -0.963 -0.612 -0.474 -0.329 -0.092
#> delta[10,4] -0.491 0.218 -0.971 -0.628 -0.478 -0.339 -0.112
#> delta[12,4] -0.388 0.203 -0.766 -0.528 -0.395 -0.254 0.046
#> delta[13,4] -0.245 0.278 -0.890 -0.405 -0.227 -0.060 0.253
#> dev.o[1,1] 2.360 2.488 0.006 0.535 1.660 3.440 8.529
#> dev.o[2,1] 0.787 1.105 0.001 0.073 0.357 1.056 3.980
#> dev.o[3,1] 1.205 1.497 0.001 0.140 0.640 1.744 5.163
#> dev.o[4,1] 0.694 1.069 0.001 0.065 0.292 0.879 3.630
#> dev.o[5,1] 0.729 1.023 0.001 0.073 0.339 0.978 3.725
#> dev.o[6,1] 0.913 1.211 0.001 0.098 0.447 1.241 4.328
#> dev.o[7,1] 0.723 1.047 0.001 0.066 0.343 0.924 3.779
#> dev.o[8,1] 0.695 0.977 0.001 0.073 0.310 0.887 3.507
#> dev.o[9,1] 0.746 0.987 0.001 0.088 0.359 1.014 3.568
#> dev.o[10,1] 0.586 0.883 0.001 0.052 0.236 0.757 3.196
#> dev.o[11,1] 0.925 1.296 0.001 0.096 0.419 1.203 4.907
#> dev.o[12,1] 1.352 1.476 0.002 0.218 0.841 2.017 5.155
#> dev.o[13,1] 1.221 1.524 0.001 0.151 0.619 1.697 5.520
#> dev.o[14,1] 0.897 1.190 0.001 0.104 0.467 1.233 4.227
#> dev.o[15,1] 1.107 1.505 0.001 0.131 0.534 1.508 5.278
#> dev.o[16,1] 1.524 1.951 0.002 0.191 0.780 2.098 7.315
#> dev.o[17,1] 2.285 2.482 0.005 0.398 1.425 3.345 8.983
#> dev.o[18,1] 1.142 1.571 0.001 0.122 0.531 1.539 5.557
#> dev.o[19,1] 1.685 1.742 0.005 0.353 1.136 2.459 6.372
#> dev.o[20,1] 0.725 1.028 0.000 0.070 0.328 0.943 3.626
#> dev.o[21,1] 1.090 1.509 0.001 0.116 0.513 1.454 5.669
#> dev.o[1,2] 3.189 1.933 0.631 1.731 2.844 4.195 7.972
#> dev.o[2,2] 0.798 1.118 0.001 0.082 0.359 1.066 4.145
#> dev.o[3,2] 1.189 1.505 0.001 0.148 0.637 1.669 5.359
#> dev.o[4,2] 0.759 1.049 0.001 0.076 0.343 1.048 3.816
#> dev.o[5,2] 0.650 0.932 0.001 0.066 0.292 0.887 3.172
#> dev.o[6,2] 0.966 1.314 0.001 0.108 0.463 1.291 4.879
#> dev.o[7,2] 0.839 1.198 0.001 0.086 0.377 1.103 4.039
#> dev.o[8,2] 0.630 0.882 0.001 0.066 0.299 0.825 3.239
#> dev.o[9,2] 0.645 0.914 0.001 0.059 0.285 0.834 3.296
#> dev.o[10,2] 1.622 1.920 0.002 0.205 0.912 2.369 6.822
#> dev.o[11,2] 0.963 1.388 0.001 0.100 0.427 1.270 5.044
#> dev.o[12,2] 0.796 1.146 0.001 0.080 0.349 0.996 4.187
#> dev.o[13,2] 1.007 1.425 0.001 0.100 0.452 1.325 4.989
#> dev.o[14,2] 0.800 1.110 0.001 0.081 0.360 1.064 3.960
#> dev.o[15,2] 1.198 1.590 0.002 0.153 0.616 1.624 5.441
#> dev.o[16,2] 1.532 1.968 0.002 0.189 0.791 2.109 7.143
#> dev.o[17,2] 2.600 2.246 0.039 0.890 2.030 3.758 8.394
#> dev.o[18,2] 1.031 1.345 0.001 0.112 0.547 1.449 4.739
#> dev.o[19,2] 0.428 0.615 0.000 0.040 0.191 0.546 2.259
#> dev.o[20,2] 0.693 0.942 0.001 0.073 0.335 0.964 3.229
#> dev.o[21,2] 1.068 1.392 0.001 0.130 0.544 1.496 5.020
#> dev.o[9,3] 0.803 1.111 0.001 0.080 0.365 1.044 3.896
#> dev.o[10,3] 0.686 0.970 0.001 0.067 0.302 0.923 3.291
#> dev.o[12,3] 1.111 1.434 0.002 0.133 0.560 1.523 4.992
#> dev.o[13,3] 0.988 1.397 0.001 0.086 0.435 1.303 5.104
#> dev.o[19,3] 1.336 1.205 0.021 0.464 1.015 1.907 4.471
#> dev.o[10,4] 1.234 1.503 0.002 0.175 0.708 1.755 5.313
#> dev.o[12,4] 0.804 1.084 0.001 0.095 0.389 1.064 3.940
#> dev.o[13,4] 1.289 1.684 0.001 0.150 0.659 1.758 6.028
#> effectiveness[1,1] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,1] 0.716 0.451 0.000 0.000 1.000 1.000 1.000
#> effectiveness[3,1] 0.206 0.404 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,1] 0.004 0.060 0.000 0.000 0.000 0.000 0.000
#> effectiveness[5,1] 0.046 0.210 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,1] 0.002 0.045 0.000 0.000 0.000 0.000 0.000
#> effectiveness[7,1] 0.005 0.073 0.000 0.000 0.000 0.000 0.000
#> effectiveness[8,1] 0.020 0.141 0.000 0.000 0.000 0.000 0.000
#> effectiveness[1,2] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,2] 0.181 0.385 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,2] 0.422 0.494 0.000 0.000 0.000 1.000 1.000
#> effectiveness[4,2] 0.059 0.236 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,2] 0.166 0.372 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,2] 0.034 0.181 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,2] 0.043 0.204 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,2] 0.095 0.293 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,3] 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,3] 0.041 0.198 0.000 0.000 0.000 0.000 1.000
#> effectiveness[3,3] 0.096 0.294 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,3] 0.176 0.381 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,3] 0.228 0.420 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,3] 0.030 0.170 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,3] 0.125 0.331 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,3] 0.305 0.460 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,4] 0.000 0.018 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,4] 0.017 0.128 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,4] 0.069 0.253 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,4] 0.146 0.353 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,4] 0.124 0.330 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,4] 0.054 0.226 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,4] 0.281 0.450 0.000 0.000 0.000 1.000 1.000
#> effectiveness[8,4] 0.309 0.462 0.000 0.000 0.000 1.000 1.000
#> effectiveness[1,5] 0.003 0.055 0.000 0.000 0.000 0.000 0.000
#> effectiveness[2,5] 0.022 0.147 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,5] 0.079 0.269 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,5] 0.176 0.381 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,5] 0.120 0.325 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,5] 0.087 0.282 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,5] 0.330 0.470 0.000 0.000 0.000 1.000 1.000
#> effectiveness[8,5] 0.184 0.387 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,6] 0.062 0.241 0.000 0.000 0.000 0.000 1.000
#> effectiveness[2,6] 0.013 0.115 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,6] 0.050 0.218 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,6] 0.215 0.411 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,6] 0.175 0.380 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,6] 0.240 0.427 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,6] 0.166 0.372 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,6] 0.079 0.270 0.000 0.000 0.000 0.000 1.000
#> effectiveness[1,7] 0.305 0.460 0.000 0.000 0.000 1.000 1.000
#> effectiveness[2,7] 0.006 0.077 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,7] 0.062 0.241 0.000 0.000 0.000 0.000 1.000
#> effectiveness[4,7] 0.158 0.365 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,7] 0.089 0.285 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,7] 0.323 0.468 0.000 0.000 0.000 1.000 1.000
#> effectiveness[7,7] 0.049 0.216 0.000 0.000 0.000 0.000 1.000
#> effectiveness[8,7] 0.008 0.087 0.000 0.000 0.000 0.000 0.000
#> effectiveness[1,8] 0.630 0.483 0.000 0.000 1.000 1.000 1.000
#> effectiveness[2,8] 0.004 0.063 0.000 0.000 0.000 0.000 0.000
#> effectiveness[3,8] 0.016 0.127 0.000 0.000 0.000 0.000 0.000
#> effectiveness[4,8] 0.067 0.251 0.000 0.000 0.000 0.000 1.000
#> effectiveness[5,8] 0.052 0.223 0.000 0.000 0.000 0.000 1.000
#> effectiveness[6,8] 0.230 0.421 0.000 0.000 0.000 0.000 1.000
#> effectiveness[7,8] 0.000 0.000 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] 1.588 0.784 0.369 0.981 1.481 2.096 3.298
#> hat.par[2,1] 49.915 4.506 41.208 46.905 49.985 52.947 58.545
#> hat.par[3,1] 43.288 4.346 35.353 40.217 43.155 46.270 51.948
#> hat.par[4,1] 42.265 4.581 33.754 39.044 42.022 45.244 51.797
#> hat.par[5,1] 17.053 2.497 12.168 15.305 17.014 18.710 22.058
#> hat.par[6,1] 43.655 4.055 35.530 40.960 43.668 46.333 51.500
#> hat.par[7,1] 157.126 7.170 142.618 152.379 156.990 162.142 171.237
#> hat.par[8,1] 68.317 5.402 57.707 64.565 68.319 71.867 79.053
#> hat.par[9,1] 89.507 4.906 80.194 86.255 89.336 92.739 99.543
#> hat.par[10,1] 78.518 3.683 71.118 76.241 78.616 80.962 85.604
#> hat.par[11,1] 72.753 5.607 61.401 69.035 72.706 76.585 83.799
#> hat.par[12,1] 77.816 4.079 69.677 75.079 77.955 80.709 85.186
#> hat.par[13,1] 49.261 4.466 40.732 46.271 49.307 52.230 57.950
#> hat.par[14,1] 33.684 4.508 25.717 30.606 33.369 36.475 43.718
#> hat.par[15,1] 37.386 4.725 28.446 34.143 37.236 40.442 47.055
#> hat.par[16,1] 306.296 13.584 280.288 297.020 306.291 315.392 333.549
#> hat.par[17,1] 10.457 2.572 5.939 8.604 10.329 12.136 15.867
#> hat.par[18,1] 21.818 3.390 15.401 19.460 21.778 24.086 28.825
#> hat.par[19,1] 3.988 1.358 1.798 3.012 3.832 4.781 7.208
#> hat.par[20,1] 25.270 3.583 18.541 22.859 25.142 27.581 32.621
#> hat.par[21,1] 32.092 4.282 23.753 29.136 31.912 34.899 40.562
#> hat.par[1,2] 1.400 0.747 0.309 0.820 1.303 1.845 3.139
#> hat.par[2,2] 46.055 4.675 37.016 42.857 46.009 49.215 55.638
#> hat.par[3,2] 31.638 3.954 24.044 28.918 31.582 34.242 39.691
#> hat.par[4,2] 43.908 5.111 34.484 40.415 43.673 47.162 54.540
#> hat.par[5,2] 12.004 2.140 8.043 10.493 11.892 13.386 16.260
#> hat.par[6,2] 35.177 3.806 27.595 32.666 35.155 37.646 42.854
#> hat.par[7,2] 196.819 9.809 177.877 190.166 196.788 203.344 217.109
#> hat.par[8,2] 51.684 4.791 42.561 48.358 51.563 55.075 61.403
#> hat.par[9,2] 81.953 5.503 71.408 78.179 82.003 85.577 93.063
#> hat.par[10,2] 72.865 3.960 64.957 70.198 73.010 75.648 80.213
#> hat.par[11,2] 119.271 8.306 103.634 113.631 119.042 124.692 136.108
#> hat.par[12,2] 80.048 4.919 70.159 76.779 80.115 83.248 89.791
#> hat.par[13,2] 25.674 4.515 17.340 22.604 25.460 28.558 35.054
#> hat.par[14,2] 32.184 4.370 23.700 29.171 32.090 35.022 41.320
#> hat.par[15,2] 31.771 4.439 23.811 28.717 31.537 34.543 40.953
#> hat.par[16,2] 245.281 12.970 220.394 236.249 245.034 254.063 271.506
#> hat.par[17,2] 7.567 2.028 4.118 6.090 7.368 8.874 12.068
#> hat.par[18,2] 13.162 2.515 8.634 11.344 13.052 14.811 18.386
#> hat.par[19,2] 2.525 0.956 1.064 1.826 2.372 3.056 4.746
#> hat.par[20,2] 18.827 3.026 13.562 16.746 18.670 20.758 25.290
#> hat.par[21,2] 22.101 3.480 15.733 19.712 21.951 24.355 29.452
#> hat.par[9,3] 79.940 5.472 69.391 76.333 79.801 83.476 90.462
#> hat.par[10,3] 68.783 4.195 60.302 66.062 68.871 71.655 76.750
#> hat.par[12,3] 67.507 4.717 58.552 64.336 67.409 70.722 76.892
#> hat.par[13,3] 35.106 5.129 25.221 31.662 34.871 38.472 45.759
#> hat.par[19,3] 2.484 0.916 1.130 1.830 2.343 3.023 4.651
#> hat.par[10,4] 67.182 4.073 58.968 64.513 67.238 69.931 74.983
#> hat.par[12,4] 62.642 4.292 54.307 59.777 62.582 65.461 70.975
#> hat.par[13,4] 41.866 4.820 32.494 38.552 41.766 44.992 51.623
#> phi[1] -0.134 0.501 -1.134 -0.472 -0.129 0.219 0.816
#> phi[2] -0.150 0.935 -2.010 -0.762 -0.121 0.482 1.647
#> phi[3] -0.018 0.960 -1.898 -0.660 -0.006 0.620 1.822
#> phi[4] -1.204 0.857 -2.695 -1.810 -1.263 -0.646 0.689
#> phi[5] -0.421 0.962 -2.158 -1.089 -0.457 0.203 1.645
#> phi[6] 0.685 0.870 -1.250 0.149 0.738 1.262 2.284
#> phi[7] -0.125 0.662 -1.466 -0.548 -0.135 0.317 1.164
#> phi[8] -0.198 0.914 -1.940 -0.812 -0.214 0.399 1.664
#> tau 0.127 0.080 0.019 0.064 0.110 0.181 0.310
#> totresdev.o 55.048 9.088 38.838 48.819 54.705 60.754 74.948
#> deviance 582.331 13.389 558.342 572.891 581.712 590.860 610.992
#> Rhat n.eff
#> EM[2,1] 1.011 200
#> EM[3,1] 1.084 31
#> EM[4,1] 1.035 710
#> EM[5,1] 1.112 23
#> EM[6,1] 1.060 48
#> EM[7,1] 1.215 14
#> EM[8,1] 1.085 30
#> EM[3,2] 1.032 68
#> EM[4,2] 1.027 130
#> EM[5,2] 1.038 110
#> EM[6,2] 1.047 62
#> EM[7,2] 1.014 320
#> EM[8,2] 1.007 1700
#> EM[4,3] 1.089 28
#> EM[5,3] 1.127 25
#> EM[6,3] 1.081 32
#> EM[7,3] 1.016 140
#> EM[8,3] 1.036 62
#> EM[5,4] 1.080 36
#> EM[6,4] 1.043 120
#> EM[7,4] 1.108 26
#> EM[8,4] 1.061 62
#> EM[6,5] 1.219 13
#> EM[7,5] 1.124 22
#> EM[8,5] 1.094 27
#> EM[7,6] 1.142 19
#> EM[8,6] 1.099 26
#> EM[8,7] 1.044 62
#> EM.pred[2,1] 1.010 210
#> EM.pred[3,1] 1.068 37
#> EM.pred[4,1] 1.035 600
#> EM.pred[5,1] 1.093 28
#> EM.pred[6,1] 1.059 58
#> EM.pred[7,1] 1.108 24
#> EM.pred[8,1] 1.047 57
#> EM.pred[3,2] 1.031 71
#> EM.pred[4,2] 1.027 150
#> EM.pred[5,2] 1.038 110
#> EM.pred[6,2] 1.044 69
#> EM.pred[7,2] 1.015 270
#> EM.pred[8,2] 1.007 2400
#> EM.pred[4,3] 1.076 31
#> EM.pred[5,3] 1.121 27
#> EM.pred[6,3] 1.073 35
#> EM.pred[7,3] 1.019 120
#> EM.pred[8,3] 1.030 75
#> EM.pred[5,4] 1.072 43
#> EM.pred[6,4] 1.036 170
#> EM.pred[7,4] 1.091 32
#> EM.pred[8,4] 1.048 90
#> EM.pred[6,5] 1.171 16
#> EM.pred[7,5] 1.108 26
#> EM.pred[8,5] 1.077 34
#> EM.pred[7,6] 1.115 23
#> EM.pred[8,6] 1.078 35
#> EM.pred[8,7] 1.031 140
#> SUCRA[1] 1.019 150
#> SUCRA[2] 1.043 110
#> SUCRA[3] 1.066 44
#> SUCRA[4] 1.026 82
#> SUCRA[5] 1.104 25
#> SUCRA[6] 1.107 28
#> SUCRA[7] 1.035 82
#> SUCRA[8] 1.020 120
#> abs_risk[1] 1.000 1
#> abs_risk[2] 1.011 190
#> abs_risk[3] 1.082 32
#> abs_risk[4] 1.032 1400
#> abs_risk[5] 1.104 25
#> abs_risk[6] 1.066 48
#> abs_risk[7] 1.209 14
#> abs_risk[8] 1.083 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] 1.030 3000
#> delta[2,2] 1.021 720
#> delta[3,2] 1.181 16
#> delta[4,2] 1.034 68
#> delta[5,2] 1.146 19
#> delta[6,2] 1.068 36
#> delta[7,2] 1.061 39
#> delta[8,2] 1.125 21
#> delta[9,2] 1.168 16
#> delta[10,2] 1.041 420
#> delta[11,2] 1.049 70
#> delta[12,2] 1.037 360
#> delta[13,2] 1.023 100
#> delta[14,2] 1.024 110
#> delta[15,2] 1.056 46
#> delta[16,2] 1.017 200
#> delta[17,2] 1.130 20
#> delta[18,2] 1.109 24
#> delta[19,2] 1.076 33
#> delta[20,2] 1.109 23
#> delta[21,2] 1.056 41
#> delta[9,3] 1.104 25
#> delta[10,3] 1.100 26
#> delta[12,3] 1.147 18
#> delta[13,3] 1.134 21
#> delta[19,3] 1.161 17
#> delta[10,4] 1.178 16
#> delta[12,4] 1.067 36
#> delta[13,4] 1.077 51
#> dev.o[1,1] 1.001 3000
#> dev.o[2,1] 1.002 1400
#> dev.o[3,1] 1.015 230
#> dev.o[4,1] 1.001 2100
#> dev.o[5,1] 1.001 3000
#> dev.o[6,1] 1.004 530
#> dev.o[7,1] 1.001 3000
#> dev.o[8,1] 1.001 3000
#> dev.o[9,1] 1.007 360
#> dev.o[10,1] 1.003 910
#> dev.o[11,1] 1.003 890
#> dev.o[12,1] 1.030 93
#> dev.o[13,1] 1.004 870
#> dev.o[14,1] 1.001 3000
#> dev.o[15,1] 1.002 1700
#> dev.o[16,1] 1.004 1100
#> dev.o[17,1] 1.023 110
#> dev.o[18,1] 1.004 520
#> dev.o[19,1] 1.002 1700
#> dev.o[20,1] 1.001 2400
#> dev.o[21,1] 1.002 1700
#> dev.o[1,2] 1.007 1400
#> dev.o[2,2] 1.004 660
#> dev.o[3,2] 1.009 240
#> dev.o[4,2] 1.003 1100
#> dev.o[5,2] 1.001 3000
#> dev.o[6,2] 1.003 780
#> dev.o[7,2] 1.002 1400
#> dev.o[8,2] 1.003 860
#> dev.o[9,2] 1.003 900
#> dev.o[10,2] 1.002 2100
#> dev.o[11,2] 1.001 3000
#> dev.o[12,2] 1.003 1200
#> dev.o[13,2] 1.001 3000
#> dev.o[14,2] 1.001 3000
#> dev.o[15,2] 1.003 970
#> dev.o[16,2] 1.003 740
#> dev.o[17,2] 1.011 230
#> dev.o[18,2] 1.017 130
#> dev.o[19,2] 1.005 450
#> dev.o[20,2] 1.002 1900
#> dev.o[21,2] 1.005 450
#> dev.o[9,3] 1.002 1200
#> dev.o[10,3] 1.001 3000
#> dev.o[12,3] 1.007 330
#> dev.o[13,3] 1.001 2800
#> dev.o[19,3] 1.012 170
#> dev.o[10,4] 1.009 370
#> dev.o[12,4] 1.001 3000
#> dev.o[13,4] 1.002 2000
#> effectiveness[1,1] 1.000 1
#> effectiveness[2,1] 1.048 56
#> effectiveness[3,1] 1.046 70
#> effectiveness[4,1] 1.070 1700
#> effectiveness[5,1] 1.021 510
#> effectiveness[6,1] 1.294 500
#> effectiveness[7,1] 1.143 530
#> effectiveness[8,1] 1.036 650
#> effectiveness[1,2] 1.000 1
#> effectiveness[2,2] 1.064 55
#> effectiveness[3,2] 1.012 180
#> effectiveness[4,2] 1.069 120
#> effectiveness[5,2] 1.029 120
#> effectiveness[6,2] 1.141 91
#> effectiveness[7,2] 1.005 2400
#> effectiveness[8,2] 1.037 160
#> effectiveness[1,3] 1.000 1
#> effectiveness[2,3] 1.003 3000
#> effectiveness[3,3] 1.008 710
#> effectiveness[4,3] 1.009 360
#> effectiveness[5,3] 1.052 57
#> effectiveness[6,3] 1.105 140
#> effectiveness[7,3] 1.046 100
#> effectiveness[8,3] 1.001 2700
#> effectiveness[1,4] 1.291 3000
#> effectiveness[2,4] 1.018 1600
#> effectiveness[3,4] 1.009 840
#> effectiveness[4,4] 1.012 320
#> effectiveness[5,4] 1.038 120
#> effectiveness[6,4] 1.064 140
#> effectiveness[7,4] 1.011 220
#> effectiveness[8,4] 1.004 570
#> effectiveness[1,5] 1.109 1300
#> effectiveness[2,5] 1.009 2300
#> effectiveness[3,5] 1.001 3000
#> effectiveness[4,5] 1.007 480
#> effectiveness[5,5] 1.013 360
#> effectiveness[6,5] 1.017 360
#> effectiveness[7,5] 1.012 180
#> effectiveness[8,5] 1.016 200
#> effectiveness[1,6] 1.028 300
#> effectiveness[2,6] 1.034 1100
#> effectiveness[3,6] 1.018 550
#> effectiveness[4,6] 1.023 130
#> effectiveness[5,6] 1.154 25
#> effectiveness[6,6] 1.006 400
#> effectiveness[7,6] 1.077 48
#> effectiveness[8,6] 1.091 72
#> effectiveness[1,7] 1.004 590
#> effectiveness[2,7] 1.136 500
#> effectiveness[3,7] 1.140 56
#> effectiveness[4,7] 1.003 1100
#> effectiveness[5,7] 1.067 89
#> effectiveness[6,7] 1.018 130
#> effectiveness[7,7] 1.051 200
#> effectiveness[8,7] 1.063 940
#> effectiveness[1,8] 1.010 210
#> effectiveness[2,8] 1.135 750
#> effectiveness[3,8] 1.042 700
#> effectiveness[4,8] 1.118 62
#> effectiveness[5,8] 1.105 86
#> effectiveness[6,8] 1.030 96
#> effectiveness[7,8] 1.000 1
#> effectiveness[8,8] 1.000 1
#> hat.par[1,1] 1.010 640
#> hat.par[2,1] 1.005 590
#> hat.par[3,1] 1.020 100
#> hat.par[4,1] 1.007 330
#> hat.par[5,1] 1.006 370
#> hat.par[6,1] 1.004 1900
#> hat.par[7,1] 1.014 150
#> hat.par[8,1] 1.006 360
#> hat.par[9,1] 1.040 56
#> hat.par[10,1] 1.009 490
#> hat.par[11,1] 1.003 990
#> hat.par[12,1] 1.039 60
#> hat.par[13,1] 1.006 450
#> hat.par[14,1] 1.001 2400
#> hat.par[15,1] 1.011 200
#> hat.par[16,1] 1.007 420
#> hat.par[17,1] 1.034 65
#> hat.par[18,1] 1.017 120
#> hat.par[19,1] 1.004 530
#> hat.par[20,1] 1.011 200
#> hat.par[21,1] 1.005 450
#> hat.par[1,2] 1.008 1200
#> hat.par[2,2] 1.005 1100
#> hat.par[3,2] 1.026 84
#> hat.par[4,2] 1.007 320
#> hat.par[5,2] 1.003 680
#> hat.par[6,2] 1.005 550
#> hat.par[7,2] 1.001 3000
#> hat.par[8,2] 1.028 78
#> hat.par[9,2] 1.011 200
#> hat.par[10,2] 1.004 640
#> hat.par[11,2] 1.006 390
#> hat.par[12,2] 1.004 570
#> hat.par[13,2] 1.008 280
#> hat.par[14,2] 1.002 1900
#> hat.par[15,2] 1.007 330
#> hat.par[16,2] 1.004 620
#> hat.par[17,2] 1.015 150
#> hat.par[18,2] 1.020 110
#> hat.par[19,2] 1.016 130
#> hat.par[20,2] 1.002 1400
#> hat.par[21,2] 1.013 160
#> hat.par[9,3] 1.006 430
#> hat.par[10,3] 1.001 2500
#> hat.par[12,3] 1.023 90
#> hat.par[13,3] 1.018 120
#> hat.par[19,3] 1.016 130
#> hat.par[10,4] 1.009 290
#> hat.par[12,4] 1.003 720
#> hat.par[13,4] 1.005 560
#> phi[1] 1.066 48
#> phi[2] 1.003 920
#> phi[3] 1.034 64
#> phi[4] 1.038 89
#> phi[5] 1.089 30
#> phi[6] 1.073 35
#> phi[7] 1.051 45
#> phi[8] 1.007 1700
#> tau 1.180 16
#> totresdev.o 1.010 220
#> deviance 1.006 380
#>
#> 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 = 89.2 and DIC = 671.5
#> 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] -1.08467173 0.4366460 -1.92567716 -1.405290358 -1.08221752
#> EM.pred[3,1] -0.76278577 0.4249281 -1.57842817 -1.056405141 -0.77602661
#> EM.pred[4,1] -0.35826332 0.3019428 -0.89884304 -0.560428866 -0.37909568
#> EM.pred[5,1] -0.46641888 0.3426449 -1.14321954 -0.712028142 -0.45863031
#> EM.pred[6,1] -0.16537910 0.2902573 -0.79103642 -0.336880557 -0.15794130
#> EM.pred[7,1] -0.45376710 0.2218684 -0.91463228 -0.594623706 -0.44531576
#> EM.pred[8,1] -0.52021399 0.2058627 -0.96130865 -0.643472122 -0.50472958
#> EM.pred[3,2] 0.31810794 0.4671492 -0.62832166 0.006128761 0.31612475
#> EM.pred[4,2] 0.72112138 0.4790516 -0.12195748 0.385315184 0.68813385
#> EM.pred[5,2] 0.61555866 0.5412715 -0.48389661 0.259852549 0.60510467
#> EM.pred[6,2] 0.91737637 0.4932709 -0.04509473 0.564048326 0.91560703
#> EM.pred[7,2] 0.63519538 0.4329326 -0.26466195 0.363978589 0.62617047
#> EM.pred[8,2] 0.56330776 0.4280803 -0.30179360 0.296987244 0.55948814
#> EM.pred[4,3] 0.41029245 0.4452615 -0.41956300 0.066621969 0.43698346
#> EM.pred[5,3] 0.30168024 0.5083531 -0.64354181 -0.065460513 0.26919065
#> EM.pred[6,3] 0.59835994 0.4865012 -0.39238862 0.288616334 0.62161357
#> EM.pred[7,3] 0.32196913 0.4003096 -0.44864106 0.037704480 0.34031578
#> EM.pred[8,3] 0.24868898 0.4095823 -0.58848844 -0.030403113 0.27269293
#> EM.pred[5,4] -0.11363478 0.4393768 -0.94902286 -0.418703833 -0.10864098
#> EM.pred[6,4] 0.19349742 0.3692715 -0.54268308 -0.054882707 0.18228677
#> EM.pred[7,4] -0.08935595 0.3182274 -0.79336467 -0.278362863 -0.04909552
#> EM.pred[8,4] -0.16639389 0.3235570 -0.89445146 -0.376124766 -0.12083654
#> EM.pred[6,5] 0.30508463 0.3827299 -0.38028753 0.019129320 0.30676358
#> EM.pred[7,5] 0.02036938 0.3520192 -0.63172581 -0.237951815 0.03209049
#> EM.pred[8,5] -0.05149945 0.3435155 -0.67230549 -0.313587677 -0.04903220
#> EM.pred[7,6] -0.28229929 0.3283443 -0.95137103 -0.513045364 -0.28108661
#> EM.pred[8,6] -0.35550837 0.3066574 -0.97507459 -0.545037994 -0.35989144
#> EM.pred[8,7] -0.07121551 0.2144172 -0.54317925 -0.189018213 -0.06945708
#> 75% 97.5% Rhat n.eff
#> EM.pred[2,1] -0.76264963 -0.25200316 1.009974 210
#> EM.pred[3,1] -0.47222992 0.03859115 1.068102 37
#> EM.pred[4,1] -0.18099786 0.30265944 1.034531 600
#> EM.pred[5,1] -0.20385173 0.10493648 1.092979 28
#> EM.pred[6,1] 0.02600996 0.37698612 1.058613 58
#> EM.pred[7,1] -0.30699800 -0.01640321 1.107870 24
#> EM.pred[8,1] -0.37966309 -0.15445076 1.046884 57
#> EM.pred[3,2] 0.64167750 1.26642116 1.031439 71
#> EM.pred[4,2] 1.04201849 1.71745765 1.026601 150
#> EM.pred[5,2] 0.99596997 1.66283983 1.037571 110
#> EM.pred[6,2] 1.24029732 1.87386915 1.044227 69
#> EM.pred[7,2] 0.94386127 1.44075421 1.014778 270
#> EM.pred[8,2] 0.84650087 1.38491449 1.007386 2400
#> EM.pred[4,3] 0.72619881 1.25663849 1.075774 31
#> EM.pred[5,3] 0.64446784 1.33632323 1.121491 27
#> EM.pred[6,3] 0.93650911 1.49309381 1.073461 35
#> EM.pred[7,3] 0.58975913 1.10034479 1.019383 120
#> EM.pred[8,3] 0.51814764 1.06348730 1.029922 75
#> EM.pred[5,4] 0.19687621 0.68112413 1.072329 43
#> EM.pred[6,4] 0.45124821 0.89192630 1.035902 170
#> EM.pred[7,4] 0.13005653 0.43897014 1.091393 32
#> EM.pred[8,4] 0.05297870 0.38466718 1.047550 90
#> EM.pred[6,5] 0.56940195 1.02459451 1.171388 16
#> EM.pred[7,5] 0.26526662 0.70770698 1.108290 26
#> EM.pred[8,5] 0.20292219 0.59273309 1.077345 34
#> EM.pred[7,6] -0.05042549 0.32394290 1.114790 23
#> EM.pred[8,6] -0.15261386 0.22850555 1.078127 35
#> EM.pred[8,7] 0.05412859 0.34588736 1.030687 140
#>
#> $tau
#> mean sd 2.5% 25% 50% 75%
#> 0.12695209 0.07984776 0.01913408 0.06389442 0.11046393 0.18133506
#> 97.5% Rhat n.eff
#> 0.31041361 1.17967052 16.00000000
#>
#> $delta
#> mean sd 2.5% 25% 50% 75%
#> delta[1,2] -0.3877233 0.2903228 -0.9181648 -0.5763986 -0.3947342 -0.20605910
#> delta[2,2] -0.3892433 0.2559630 -0.8477256 -0.5657029 -0.3964507 -0.22058204
#> delta[3,2] -0.5081054 0.1946517 -0.9360039 -0.6310886 -0.4953308 -0.36545817
#> delta[4,2] -0.4898420 0.1646070 -0.8179129 -0.6036128 -0.4840714 -0.38078336
#> delta[5,2] -0.4606613 0.2041363 -0.8819322 -0.5891083 -0.4480068 -0.32080119
#> delta[6,2] -0.3877150 0.1979125 -0.7537991 -0.5199508 -0.3897765 -0.26083552
#> delta[7,2] -0.5038366 0.1522428 -0.8075554 -0.6097587 -0.5006116 -0.39262137
#> delta[8,2] -0.4265632 0.1739327 -0.7810351 -0.5380534 -0.4260225 -0.30573578
#> delta[9,2] -0.4713464 0.1797038 -0.8484650 -0.5887129 -0.4634319 -0.34070994
#> delta[10,2] -0.3183508 0.3077716 -0.8639165 -0.5344079 -0.3337421 -0.13291966
#> delta[11,2] -0.2064307 0.2476471 -0.7475996 -0.3641260 -0.1945881 -0.03001666
#> delta[12,2] -0.3284730 0.2847451 -0.8260801 -0.5267727 -0.3513912 -0.15743797
#> delta[13,2] -1.1237872 0.3921329 -1.8423701 -1.4155614 -1.1371032 -0.81949432
#> delta[14,2] -0.1041357 0.1816044 -0.4911982 -0.2115361 -0.1025628 0.01237067
#> delta[15,2] -0.1072727 0.2458582 -0.6010313 -0.2626005 -0.1093913 0.06349389
#> delta[16,2] -0.3917362 0.1303968 -0.6465950 -0.4799467 -0.3910737 -0.30158047
#> delta[17,2] -0.4186279 0.2869045 -0.9992253 -0.6074110 -0.4164897 -0.22551540
#> delta[18,2] -0.5007266 0.3168685 -1.0845513 -0.7368813 -0.5111483 -0.24694264
#> delta[19,2] -0.4951453 0.3328552 -1.1734328 -0.7342970 -0.4768681 -0.23089139
#> delta[20,2] -0.4273595 0.2038348 -0.8348447 -0.5621759 -0.4211233 -0.28666202
#> delta[21,2] -0.5653500 0.1922115 -0.9754544 -0.6914354 -0.5531994 -0.42221487
#> delta[9,3] -0.5635279 0.1708487 -0.9103961 -0.6794193 -0.5607780 -0.43481908
#> delta[10,3] -0.4864293 0.3183214 -1.1127655 -0.7205162 -0.4786876 -0.23522605
#> delta[12,3] -0.3933413 0.3046332 -0.9635469 -0.6264132 -0.3772520 -0.16237328
#> delta[13,3] -0.8092069 0.3729214 -1.5099691 -1.0601985 -0.8339221 -0.54894197
#> delta[19,3] -0.4840963 0.2216821 -0.9630369 -0.6115137 -0.4736482 -0.32896763
#> delta[10,4] -0.4908771 0.2182532 -0.9706643 -0.6276205 -0.4776945 -0.33902357
#> delta[12,4] -0.3878595 0.2034742 -0.7660479 -0.5280040 -0.3949774 -0.25398051
#> delta[13,4] -0.2454372 0.2776055 -0.8898658 -0.4051891 -0.2268704 -0.05974512
#> 97.5% Rhat n.eff
#> delta[1,2] 0.22763262 1.030054 3000
#> delta[2,2] 0.15388589 1.020500 720
#> delta[3,2] -0.19037443 1.180641 16
#> delta[4,2] -0.17737594 1.033684 68
#> delta[5,2] -0.08315796 1.145707 19
#> delta[6,2] 0.02907250 1.067833 36
#> delta[7,2] -0.22391250 1.061376 39
#> delta[8,2] -0.08906307 1.125327 21
#> delta[9,2] -0.16798835 1.167531 16
#> delta[10,2] 0.34777418 1.041403 420
#> delta[11,2] 0.24234019 1.048723 70
#> delta[12,2] 0.28950900 1.037389 360
#> delta[13,2] -0.37713035 1.022734 100
#> delta[14,2] 0.24447726 1.023948 110
#> delta[15,2] 0.38319219 1.056110 46
#> delta[16,2] -0.13555833 1.016550 200
#> delta[17,2] 0.14094266 1.130036 20
#> delta[18,2] 0.06509985 1.108979 24
#> delta[19,2] 0.06357894 1.076074 33
#> delta[20,2] -0.01780548 1.108869 23
#> delta[21,2] -0.23503792 1.055865 41
#> delta[9,3] -0.25475241 1.103667 25
#> delta[10,3] 0.06233988 1.099587 26
#> delta[12,3] 0.14739978 1.147086 18
#> delta[13,3] -0.09850386 1.134353 21
#> delta[19,3] -0.09167523 1.161473 17
#> delta[10,4] -0.11196418 1.178308 16
#> delta[12,4] 0.04643525 1.066615 36
#> delta[13,4] 0.25281161 1.076931 51
#>
#> $heter_prior
#> [1] 0 1 1
#>
#> $SUCRA
#> mean sd 2.5% 25% 50% 75%
#> SUCRA[1] 0.06261905 0.08940405 0.0000000 0.0000000 0.0000000 0.1428571
#> SUCRA[2] 0.92409524 0.16351506 0.4285714 0.8571429 1.0000000 1.0000000
#> SUCRA[3] 0.73261905 0.26592230 0.1428571 0.5714286 0.8571429 0.8571429
#> SUCRA[4] 0.42219048 0.24289783 0.0000000 0.2857143 0.4285714 0.5714286
#> SUCRA[5] 0.53604762 0.27499770 0.0000000 0.2857143 0.5714286 0.7142857
#> SUCRA[6] 0.23533333 0.21582244 0.0000000 0.1428571 0.1428571 0.2857143
#> SUCRA[7] 0.48847619 0.17166497 0.1428571 0.4285714 0.4285714 0.5714286
#> SUCRA[8] 0.59861905 0.16914595 0.2857143 0.4285714 0.5714286 0.7142857
#> 97.5% Rhat n.eff
#> SUCRA[1] 0.2857143 1.019417 150
#> SUCRA[2] 1.0000000 1.043263 110
#> SUCRA[3] 1.0000000 1.066187 44
#> SUCRA[4] 0.8571429 1.025810 82
#> SUCRA[5] 1.0000000 1.103648 25
#> SUCRA[6] 0.8571429 1.106835 28
#> SUCRA[7] 0.8571429 1.035246 82
#> SUCRA[8] 0.8571429 1.020052 120
#>
#> $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] 0.7163333333 0.45085213 0 0 1 1 1 1.047623
#> effectiveness[3,1] 0.2060000000 0.40449789 0 0 0 0 1 1.045550
#> effectiveness[4,1] 0.0036666667 0.06045197 0 0 0 0 0 1.070399
#> effectiveness[5,1] 0.0463333333 0.21024103 0 0 0 0 1 1.021224
#> effectiveness[6,1] 0.0020000000 0.04468406 0 0 0 0 0 1.293699
#> effectiveness[7,1] 0.0053333333 0.07284681 0 0 0 0 0 1.142756
#> effectiveness[8,1] 0.0203333333 0.14116137 0 0 0 0 0 1.036332
#> effectiveness[1,2] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> effectiveness[2,2] 0.1806666667 0.38480590 0 0 0 0 1 1.064111
#> effectiveness[3,2] 0.4223333333 0.49401340 0 0 0 1 1 1.011777
#> effectiveness[4,2] 0.0590000000 0.23566398 0 0 0 0 1 1.069140
#> effectiveness[5,2] 0.1656666667 0.37184313 0 0 0 0 1 1.028889
#> effectiveness[6,2] 0.0340000000 0.18125935 0 0 0 0 1 1.140966
#> effectiveness[7,2] 0.0433333333 0.20364032 0 0 0 0 1 1.004532
#> effectiveness[8,2] 0.0950000000 0.29326382 0 0 0 0 1 1.036533
#> effectiveness[1,3] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> effectiveness[2,3] 0.0410000000 0.19832325 0 0 0 0 1 1.003477
#> effectiveness[3,3] 0.0956666667 0.29418260 0 0 0 0 1 1.007530
#> effectiveness[4,3] 0.1756666667 0.38059976 0 0 0 0 1 1.008984
#> effectiveness[5,3] 0.2280000000 0.41961255 0 0 0 0 1 1.052437
#> effectiveness[6,3] 0.0296666667 0.16969430 0 0 0 0 1 1.105048
#> effectiveness[7,3] 0.1253333333 0.33115169 0 0 0 0 1 1.045651
#> effectiveness[8,3] 0.3046666667 0.46034284 0 0 0 1 1 1.001233
#> effectiveness[1,4] 0.0003333333 0.01825742 0 0 0 0 0 1.290904
#> effectiveness[2,4] 0.0166666667 0.12804044 0 0 0 0 0 1.017840
#> effectiveness[3,4] 0.0690000000 0.25349639 0 0 0 0 1 1.008636
#> effectiveness[4,4] 0.1456666667 0.35283053 0 0 0 0 1 1.012346
#> effectiveness[5,4] 0.1240000000 0.32963650 0 0 0 0 1 1.038237
#> effectiveness[6,4] 0.0540000000 0.22605538 0 0 0 0 1 1.063766
#> effectiveness[7,4] 0.2810000000 0.44956242 0 0 0 1 1 1.010854
#> effectiveness[8,4] 0.3093333333 0.46229586 0 0 0 1 1 1.004052
#> effectiveness[1,5] 0.0030000000 0.05469915 0 0 0 0 0 1.109096
#> effectiveness[2,5] 0.0220000000 0.14670779 0 0 0 0 0 1.009428
#> effectiveness[3,5] 0.0786666667 0.26926268 0 0 0 0 1 1.000814
#> effectiveness[4,5] 0.1756666667 0.38059976 0 0 0 0 1 1.006823
#> effectiveness[5,5] 0.1196666667 0.32462545 0 0 0 0 1 1.012857
#> effectiveness[6,5] 0.0870000000 0.28188204 0 0 0 0 1 1.016640
#> effectiveness[7,5] 0.3303333333 0.47041151 0 0 0 1 1 1.012379
#> effectiveness[8,5] 0.1836666667 0.38727667 0 0 0 0 1 1.016475
#> effectiveness[1,6] 0.0616666667 0.24058924 0 0 0 0 1 1.027579
#> effectiveness[2,6] 0.0133333333 0.11471679 0 0 0 0 0 1.033780
#> effectiveness[3,6] 0.0500000000 0.21798128 0 0 0 0 1 1.018216
#> effectiveness[4,6] 0.2146666667 0.41065935 0 0 0 0 1 1.022580
#> effectiveness[5,6] 0.1750000000 0.38003045 0 0 0 0 1 1.153686
#> effectiveness[6,6] 0.2403333333 0.42735711 0 0 0 0 1 1.006371
#> effectiveness[7,6] 0.1656666667 0.37184313 0 0 0 0 1 1.077274
#> effectiveness[8,6] 0.0793333333 0.27030337 0 0 0 0 1 1.090666
#> effectiveness[1,7] 0.3046666667 0.46034284 0 0 0 1 1 1.003880
#> effectiveness[2,7] 0.0060000000 0.07723981 0 0 0 0 0 1.135746
#> effectiveness[3,7] 0.0620000000 0.24119575 0 0 0 0 1 1.139778
#> effectiveness[4,7] 0.1583333333 0.36511413 0 0 0 0 1 1.002903
#> effectiveness[5,7] 0.0890000000 0.28479121 0 0 0 0 1 1.066507
#> effectiveness[6,7] 0.3233333333 0.46782672 0 0 0 1 1 1.017587
#> effectiveness[7,7] 0.0490000000 0.21590400 0 0 0 0 1 1.050630
#> effectiveness[8,7] 0.0076666667 0.08723775 0 0 0 0 0 1.063109
#> effectiveness[1,8] 0.6303333333 0.48279490 0 0 1 1 1 1.010085
#> effectiveness[2,8] 0.0040000000 0.06312946 0 0 0 0 0 1.134929
#> effectiveness[3,8] 0.0163333333 0.12677505 0 0 0 0 0 1.041609
#> effectiveness[4,8] 0.0673333333 0.25064017 0 0 0 0 1 1.118431
#> effectiveness[5,8] 0.0523333333 0.22273548 0 0 0 0 1 1.105253
#> effectiveness[6,8] 0.2296666667 0.42068858 0 0 0 0 1 1.030221
#> effectiveness[7,8] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> effectiveness[8,8] 0.0000000000 0.00000000 0 0 0 0 0 1.000000
#> n.eff
#> effectiveness[1,1] 1
#> effectiveness[2,1] 56
#> effectiveness[3,1] 70
#> effectiveness[4,1] 1700
#> effectiveness[5,1] 510
#> effectiveness[6,1] 500
#> effectiveness[7,1] 530
#> effectiveness[8,1] 650
#> effectiveness[1,2] 1
#> effectiveness[2,2] 55
#> effectiveness[3,2] 180
#> effectiveness[4,2] 120
#> effectiveness[5,2] 120
#> effectiveness[6,2] 91
#> effectiveness[7,2] 2400
#> effectiveness[8,2] 160
#> effectiveness[1,3] 1
#> effectiveness[2,3] 3000
#> effectiveness[3,3] 710
#> effectiveness[4,3] 360
#> effectiveness[5,3] 57
#> effectiveness[6,3] 140
#> effectiveness[7,3] 100
#> effectiveness[8,3] 2700
#> effectiveness[1,4] 3000
#> effectiveness[2,4] 1600
#> effectiveness[3,4] 840
#> effectiveness[4,4] 320
#> effectiveness[5,4] 120
#> effectiveness[6,4] 140
#> effectiveness[7,4] 220
#> effectiveness[8,4] 570
#> effectiveness[1,5] 1300
#> effectiveness[2,5] 2300
#> effectiveness[3,5] 3000
#> effectiveness[4,5] 480
#> effectiveness[5,5] 360
#> effectiveness[6,5] 360
#> effectiveness[7,5] 180
#> effectiveness[8,5] 200
#> effectiveness[1,6] 300
#> effectiveness[2,6] 1100
#> effectiveness[3,6] 550
#> effectiveness[4,6] 130
#> effectiveness[5,6] 25
#> effectiveness[6,6] 400
#> effectiveness[7,6] 48
#> effectiveness[8,6] 72
#> effectiveness[1,7] 590
#> effectiveness[2,7] 500
#> effectiveness[3,7] 56
#> effectiveness[4,7] 1100
#> effectiveness[5,7] 89
#> effectiveness[6,7] 130
#> effectiveness[7,7] 200
#> effectiveness[8,7] 940
#> effectiveness[1,8] 210
#> effectiveness[2,8] 750
#> effectiveness[3,8] 700
#> effectiveness[4,8] 62
#> effectiveness[5,8] 86
#> effectiveness[6,8] 96
#> effectiveness[7,8] 1
#> 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] 0.1867084 0.06238818 0.09149723 0.1377293 0.1789715 0.2294464
#> abs_risk[3] 0.2372068 0.07061976 0.12511121 0.1849982 0.2262691 0.2829065
#> abs_risk[4] 0.3132276 0.05757604 0.21976102 0.2739773 0.3071813 0.3466949
#> abs_risk[5] 0.2912603 0.06150182 0.18400188 0.2414348 0.2886604 0.3393202
#> abs_risk[6] 0.3553794 0.05445212 0.24625669 0.3199438 0.3547925 0.3936380
#> abs_risk[7] 0.2926059 0.03356584 0.23107423 0.2682706 0.2911388 0.3166287
#> abs_risk[8] 0.2773508 0.02843270 0.22503851 0.2569495 0.2756408 0.2995886
#> 97.5% Rhat n.eff
#> abs_risk[1] 0.3916667 1.000000 1
#> abs_risk[2] 0.3247202 1.011044 190
#> abs_risk[3] 0.3858265 1.081666 32
#> abs_risk[4] 0.4435922 1.032370 1400
#> abs_risk[5] 0.4059571 1.103975 25
#> abs_risk[6] 0.4601576 1.066158 48
#> abs_risk[7] 0.3571566 1.209327 14
#> abs_risk[8] 0.3334597 1.082997 30
#>
#> $base_risk
#> [1] 0.3916667
#>
#> attr(,"class")
#> [1] "run_model"
# }