This is similar to the function network.run, except it uses contrast-level data instead of arms-level data.
contrast.network.run(
network,
inits = NULL,
n.chains = 3,
max.run = 1e+05,
setsize = 10000,
n.run = 50000,
conv.limit = 1.05,
extra.pars.save = NULL
)Data that is put into rjags function jags.model
Initial values that are either specified by the user or generated as a default
Parameters that are saved. Add more parameters in extra.pars.save if other variables are desired
Half of the converged sequence is thrown out as a burnin
If the number of iterations user wants (n.run) is less than the number of converged sequence after burnin, we thin the sequence and store the thinning interval
MCMC samples stored using jags. The returned samples have the form of mcmc.list and can be directly applied to coda functions
Maximum Gelman and Rubin's convergence diagnostic calculated for the final sample
Contains deviance statistics such as pD (effective number of parameters) and DIC (Deviance Information Criterion)
Rank probability calculated for each treatments. rank.preference parameter in contrast.network.data is used to define whether higher or lower value is preferred. The numbers are probabilities that a given treatment has been in certain rank in the sequence.
contrast level network object created from contrast.network.data function
Initial values for the parameters being sampled. If left unspecified, program will generate reasonable initial values.
Number of chains to run
Maximum number of iterations that user is willing to run. If the algorithm is not converging, it will run up to max.run iterations before printing a message that it did not converge
Number of iterations that are run between convergence checks. If the algorithm converges fast, user wouldn't need a big setsize. The number that is printed between each convergence checks is the gelman-rubin diagnostics and we would want that to be below the conv.limit the user specifies.
Final number of iterations that the user wants to store. If after the algorithm converges, user wants less number of iterations, we thin the sequence. If the user wants more iterations, we run extra iterations to reach the specified number of runs
Convergence limit for Gelman and Rubin's convergence diagnostic. Point estimate is used to test convergence of parameters for study effect (eta), relative effect (d), and heterogeneity (log variance (logvar)).
Parameters that user wants to save besides the default parameters saved. See code using cat(network$code) to see which parameters can be saved.
network <- with(parkinsons_contrast, {
contrast.network.data(Outcomes, Treat, SE, na, V)
})
# \donttest{
result <- contrast.network.run(network)
# }
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