Get information on network connectivity (number of subnetworks, distance matrix)
To determine the network structure and to test whether a given
network is fully connected. Network information is provided as a
triple of vectors treat1, treat2, and studlab
where each row corresponds to an existing pairwise treatment
comparison (treat1, treat2) in a study
(studlab). The function calculates the number of subnetworks
(connectivity components; value of 1 corresponds to a fully
connected network) and the distance matrix (in block-diagonal form
in the case of subnetworks). If some treatments are combinations of
other treatments or have common components, an analysis based on
the additive network meta-analysis model might be possible, see
discomb function.
netconnection( treat1, treat2, studlab, data = NULL, subset = NULL, sep.trts = ":", nchar.trts = 666, title = "", warn = FALSE ) ## S3 method for class 'netconnection' print( x, digits = max(4, .Options$digits - 3), nchar.trts = x$nchar.trts, details = FALSE, ... )
treat1 |
Label / number for first treatment or an object of
class |
treat2 |
Label / number for second treatment. |
studlab |
An optional - but important! - vector with study labels (see Details). |
data |
An optional data frame containing the study information. |
subset |
An optional vector specifying a subset of studies to be used. |
sep.trts |
A character used in comparison names as separator between treatment labels. |
nchar.trts |
A numeric defining the minimum number of characters used to create unique treatment names. |
title |
Title of meta-analysis / systematic review. |
warn |
A logical indicating whether warnings should be printed. |
x |
An object of class |
digits |
Minimal number of significant digits, see
|
details |
A logical indicating whether to print the distance matrix. |
... |
Additional arguments (ignored at the moment) |
An object of class netconnection with corresponding
print function. The object is a list containing the
following components:
treat1, treat2, studlab, title, warn, nchar.trts |
As defined above. |
k |
Total number of studies. |
m |
Total number of pairwise comparisons. |
n |
Total number of treatments. |
n.subnets |
Number of subnetworks; equal to 1 for a fully connected network. |
D.matrix |
Distance matrix. |
A.matrix |
Adjacency matrix. |
L.matrix |
Laplace matrix. |
call |
Function call. |
version |
Version of R package netmeta used to create object. |
Gerta Rücker ruecker@imbi.uni-freiburg.de, Guido Schwarzer sc@imbi.uni-freiburg.de
data(Senn2013)
nc1 <- netconnection(treat1, treat2, studlab, data = Senn2013)
nc1
# Extract number of (sub)networks
#
nc1$n.subnets
# Extract distance matrix
#
nc1$D.matrix
# Conduct network meta-analysis (results not shown)
#
net1 <- netmeta(TE, seTE, treat1, treat2, studlab, data = Senn2013)
# Artificial example with two subnetworks
#
t1 <- c("G", "B", "B", "D", "A", "F")
t2 <- c("B", "C", "E", "E", "H", "A")
#
nc2 <- netconnection(t1, t2)
print(nc2, details = TRUE)
# Number of subnetworks
#
nc2$n.subnets
# Extract distance matrix
#
nc2$D.matrix
# Conduct network meta-analysis (results in an error message due to
# unconnected network)
try(net2 <- netmeta(1:6, 1:6, t1, t2, 1:6))
# Conduct network meta-analysis on first subnetwork
#
net2.1 <- netmeta(1:6, 1:6, t1, t2, 1:6,
subset = (t1 %in% c("A", "F", "H") & t2 %in% c("A", "F", "H")))
# Conduct network meta-analysis on second subnetwork
#
net2.2 <- netmeta(1:6, 1:6, t1, t2, 1:6,
subset = !(t1 %in% c("A", "F", "H") & t2 %in% c("A", "F", "H")))
summary(net2.1)
summary(net2.2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.