mtc.nodesplit: Node-splitting analysis of inconsistency

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/nodesplit.R

Description

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

Usage

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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).

Value

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

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# 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)

Example output

Loading required package: coda
[1] "d.A.C"       "d.A.D"       "d.B.D"       "d.C.D"       "consistency"
Potential scale reduction factors:

           Point est. Upper C.I.
d.A.C           0.999       1.00
d.D.A           1.002       1.00
d.D.B           1.003       1.01
d.D.C           1.009       1.02
d.D.E           1.004       1.01
d.direct        0.999       1.00
d.indirect      1.001       1.00
deviance        1.000       1.00
sd.d            1.005       1.01

Node-splitting analysis of inconsistency
========================================

   comparison  p.value CrI                 
1  d.A.C       0.74625                     
2  -> direct           -0.31 (-2.0, 1.6)   
3  -> indirect         -0.76 (-2.8, 1.2)   
4  -> network          -0.53 (-1.8, 0.66)  
5  d.A.D       0.62375                     
6  -> direct           -0.90 (-3.0, 1.2)   
7  -> indirect         -0.23 (-2.8, 2.4)   
8  -> network          -0.56 (-1.8, 0.68)  
9  d.B.D       0.94875                     
10 -> direct           1.4 (-1.0, 3.7)     
11 -> indirect         1.5 (-1.7, 4.8)     
12 -> network          1.3 (-0.064, 2.8)   
13 d.C.D       0.70875                     
14 -> direct           0.025 (-1.0, 1.3)   
15 -> indirect         -0.45 (-2.9, 2.0)   
16 -> network          -0.029 (-0.90, 0.90)

gemtc documentation built on May 15, 2021, 1:07 a.m.