gemtc (version 0.1-2)

mtc.model: Generate network meta-analysis models

Description

The mtc.model function generates network meta-analysis models from an mtc.network object.

Usage

mtc.model(network, type = "Consistency", factor = 2.5, n.chain = 4)

Arguments

network

An object of S3 class mtc.network

type

A string literal indicating whether the model should be "consistency" or "inconsistency"

factor

Variance scaling factor for the starting values

n.chain

Number of chains in the model

Value

• An object of class mtc.model. This is a list with the following elements:
• type
  The type of model
• description
  A short description of the model
• n.chain
  Number of chains
• var.scale
  The previously supplied scaling factor
• j.network
  The Java network object (internal)
• j.model
  The Java model object (internal)
• j.generator
  The Java starting value generator object (internal)

encoding

utf8

Visualization

Calling the generic plot method on an S3 mtc.model object will show a graph with the treatments as vertices and the comparisons as edges. The solid arrows represent basic parameters, the dotted arrows represent inconsistency factors and solid lines represent comparisons that are not associated with any parameter but do have direct evidence from trials.

html{ The example code will generate the following graph:

parkinson-inconsistency.png

{Graph showing structure of inconsistency model generated from parkison example data} } latex{ The example code will generate the following graph:

parkinson-inconsistency.pdf

{options: width=3in} }

Note that the layout algorithm used in visualization is non-deterministic and might produce different results on each run.

Details

The mtc.model function generates an object of S3 class mtc.model, which can be visualized by the generic plot function or summarized by the generic summary function.

See Also

mtc.network, mtc.run

Examples

file <- system.file("extdata/parkinson.gemtc", package="gemtc")
network <- read.mtc.network(file)
model <- mtc.model(network, "incons")
plot(model)
summary(model)