ume.network.data {bnma}  R Documentation 
Make a network object for the unrelated mean effects model (inconsistency model) containing data, priors, and a JAGS model file
Description
This is similar to the function network.data
, except this is used for the unrelated mean effects model.
Usage
ume.network.data(
Outcomes,
Study,
Treat,
N = NULL,
SE = NULL,
response = NULL,
Treat.order = NULL,
type = "random",
mean.mu = NULL,
prec.mu = NULL,
mean.d = NULL,
prec.d = NULL,
hy.prior = list("dunif", 0, 5),
dic = TRUE
)
Arguments
Outcomes 
Armlevel outcomes. If it is a multinomial response, the matrix would be arms (row) by multinomial categories (column). If it is binomial or normal, it would be a vector. 
Study 
A vector of study indicator for each arm 
Treat 
A vector of treatment indicator for each arm. Treatments should have positive integer values starting from 1 to total number of treatments. In a study, lowest number is taken as the baseline treatment. 
N 
A vector of total number of observations in each arm. Used for binomial and multinomial responses. 
SE 
A vector of standard error for each arm. Used only for normal response. 
response 
Specification of the outcomes type. Must specify one of the following: "normal", "binomial", or "multinomial". 
Treat.order 
Treatment order which determines how treatments are compared. The first treatment that is specified is considered to be the baseline treatment. Default order is alphabetical. If the treatments are coded 1, 2, etc, then the treatment with a value of 1 would be assigned as a baseline treatment. 
type 
Type of model fitted: either "random" for random effects model or "fixed" for fixed effects model. Default is "random". 
mean.mu 
Prior mean for the study effect (baseline risk) 
prec.mu 
Prior precision for the study effect (baseline risk) 
mean.d 
Prior mean for the relative effect 
prec.d 
Prior precision for the relative effect 
hy.prior 
Prior for the heterogeneity parameter. Supports uniform, gamma, and half normal for normal. It should be a list of length 3, where first element should be the distribution (one of dunif, dgamma, dhnorm, dwish) and the next two are the parameters associated with the distribution. For example, list("dunif", 0, 5) give uniform prior with lower bound 0 and upper bound 5 for the heterogeneity parameter. 
dic 
This is an indicator for whether user wants to calculate DIC. Model stores less information if you set it to FALSE. 
Value
Creates list of variables that are used to run the model using ume.network.run
r 

t 

nstudy 
Number of study 
na 
Number of arms for each study 
ntreat 
Number of treatment 
b.id 
Indicator in sequence of all treatments for which treatment is base treatment in Study 
code 
Rjags model file code that is generated using information provided by the user. To view model file inside R in a nice format, use 
References
S. Dias, N.J. Welton, A.J. Sutton, D.M. Caldwell, G. Lu, and A.E. Ades (2013), Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials, Medical Decision Making 33(5):641656. doi:10.1177/0272989X12455847
Examples
network < with(thrombolytic, {
ume.network.data(Outcomes, Study, Treat, N = N, response = "binomial")
})
network