Description Usage Arguments Details Value Methods (by generic) Examples
View source: R/inconsistency.functions.R
Splits contributions for a given set of treatment comparisons into direct and indirect evidence. A discrepancy between the two suggests that the consistency assumption required for NMA and MBNMA may violated.
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network 
An object of class 
likelihood 
A string indicating the likelihood to use in the model. Can take either 
link 
A string indicating the link function to use in the model. Can take any link function
defined within JAGS (e.g. 
method 
Can take either 
comparisons 
A matrix specifying the comparisons to be split (one row per comparison).
The matrix must have two columns indicating each treatment for each comparison. Values can
either be character (corresponding to the treatment names given in 
drop.discon 
A boolean object that indicates whether to drop treatments
that are disconnected at the treatment level. Default is 
... 
Arguments to be sent to 
x 
An object of 
plot.type 
A character string that can take the value of 
The S3 method plot()
on an nma.nodesplit
object generates either
forest plots of posterior medians and 95\% credible intervals, or density plots
of posterior densities for direct and indirect evidence.
Plots the desired graph(s) and returns an object (or list of object if
plot.type=NULL
) of class(c("gg", "ggplot"))
plot
: Plot outputs from treatmentlevel nodesplit models
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  # Using the triptans data
network < mbnma.network(HF2PPITT)
split < nma.nodesplit(network, likelihood = "binomial", link="logit",
method="common")
#### To perform nodesplit on selected comparisons ####
# Check for closed loops of treatments with independent evidence sources
loops < inconsistency.loops(network$data.ab)
# This...
single.split < nma.nodesplit(network, likelihood = "binomial", link="logit",
method="random", comparisons=rbind(c("sumatriptan_1", "almotriptan_1")))
#...is the same as...
single.split < nma.nodesplit(network, likelihood = "binomial", link="logit",
method="random", comparisons=rbind(c(6, 12)))
# Plot results
plot(split, plot.type="density") # Plot density plots of posterior densities
plot(split, plot.type="forest") # Plot forest plots of direct and indirect evidence
# Print and summarise results
print(split)
summary(split) # Generate a data frame of summary results

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