mtc.nodesplit function

Node-splitting analysis of inconsistency

Generate and run an ensemble of node-splitting models, results of which can be jointly summarized and plotted. utf8

mtc.nodesplit(network, comparisons=mtc.nodesplit.comparisons(network), ...)
mtc.nodesplit.comparisons(network)

Arguments

• network: An object of S3 class mtc.network.
• comparisons: Data frame specifying the comparisons to be split. The frame has two columns: 't1' and 't2'.
• ...: Arguments to be passed to mtc.run or mtc.model. This can be used to set the likelihood/link or the number of iterations, for example.

Details

mtc.nodesplit returns the MCMC results for all relevant node-splitting models [van Valkenhoef et al. 2015] . To get appropriate summary statistics, call summary() on the results object. The summary can be plotted. See mtc.model for details on how the node-splitting models are generated.

To control parameters of the MCMC estimation, see mtc.run. To specify the likelihood/link or to control other model parameters, see mtc.model. The ... arguments are first matched against mtc.run, and those that do not match are passed to mtc.model.

mtc.nodesplit.comparisons returns a data frame enumerating all comparisons that can reasonably be split (i.e. have independent indirect evidence).

Returns

For mtc.nodesplit: an object of class mtc.nodesplit. This is a list with the following elements: - d.X.Y: For each comparison (t1=X, t2=Y), the MCMC results

• consistency: The consistency model results

For summary: an object of class mtc.nodesplit.summary. This is a list with the following elements: - dir.effect: Summary of direct effects for each split comparison

• ind.effect: Summary of indirect effects for each split comparison

• cons.effect: Summary of consistency model effects for each split comparison

• p.value: Inconsistency p-values for each split comparison

• cons.model: The generated consistency model

Author(s)

Gert van Valkenhoef, Joël Kuiper

See Also

mtc.model

mtc.run

Examples

# Run all relevant node-splitting models
## Not run:  result.ns <- mtc.nodesplit(parkinson, thin=50) 
# (read results from file instead of running:)
result.ns <- readRDS(system.file('extdata/parkinson.ns.rds', package='gemtc'))

# List the individual models 
names(result.ns)

# Time series plots and convergence diagnostics for d.A.C model
plot(result.ns$d.A.C)
gelman.diag(result.ns$d.A.C, multivariate=FALSE)

# Overall summary and plot
summary.ns <- summary(result.ns)
print(summary.ns)
plot(summary.ns)