mtc.nodesplit | R Documentation |
Generate and run an ensemble of node-splitting models, results of which can be jointly summarized and plotted.
mtc.nodesplit(network, comparisons=mtc.nodesplit.comparisons(network), ...)
mtc.nodesplit.comparisons(network)
network |
An object of S3 class |
comparisons |
Data frame specifying the comparisons to be split. The frame has two columns: 't1' and 't2'. |
... |
Arguments to be passed to |
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).
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 |
Gert van Valkenhoef, Joël Kuiper
mtc.model
mtc.run
# 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)
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