netmatrix | R Documentation |

Auxiliary function to create a matrix with additional information for pairwise comparisons

netmatrix( x, var, levels, labels = levels, func = "mode", ties.method = "random" )

`x` |
A |

`var` |
Variable with additional information. |

`levels` |
An optional vector of the values that |

`labels` |
An optional vector with labels for |

`func` |
A character string with the function name to summarize values within pairwise comparisons; see Details. |

`ties.method` |
A character string describing how ties are
handled if |

For each pairwise comparison, unique values will be calculated for
the variable `var`

based on the argument `func`

: "mode"
(most common value), "min" (minimum value), "max", "mean",
"median", and "sum". In order to determine the most common value,
the argument `ties.method`

can be used in the case of ties
with "first" meaning that the first / smallest value will be
selected; similar for "last" (last / largest value) and "random"
(random selection).

A matrix with the same row and column names as the adjacency matrix
`x$A.matrix`

.

Guido Schwarzer sc@imbi.uni-freiburg.de

`netmeta`

, `netgraph.netmeta`

data(smokingcessation) # Add variable with (fictious) risk of bias values # with 1 = "low risk" and 2 = "high risk" # smokingcessation$rob <- rep(1:2, 12) p1 <- pairwise(list(treat1, treat2, treat3), event = list(event1, event2, event3), n = list(n1, n2, n3), data = smokingcessation, sm = "OR") net1 <- netmeta(p1, common = FALSE, ref = "A") # Generate network graph with information on risk of bias # col.rob <- netmatrix(net1, rob, ties.method = "last", levels = 1:2, labels = c("green", "yellow")) # netgraph(net1, plastic = FALSE, col = col.rob, cex.points = 5, bg.points = "gray", adj = 0.5) netgraph(net1, plastic = FALSE, col = col.rob, cex.points = n.trts, bg.points = "blue", labels = paste0(trts, " (n=", n.trts, ")"), offset = c(0.05, 0.035, 0.05, 0.025))

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