plot.netposet: Scatter plot of partially order of treatment ranks

Description

This function generates a scatter plot of a partial order of treatment ranks.

Usage

# S3 method for netposet
plot(x,
     pooled=ifelse(x$comb.random, "random", "fixed"),
     sel.x = 1, sel.y = 2, sel.z = 3,
     dim = "2d",
     cex = 1, col = "black",
     adj.x = 0, adj.y = 1,
     offset.x = 0.005, offset.y = -0.005,
     arrows = FALSE,
     col.lines = "black", lty.lines = 1, lwd.lines = 1,
     length = 0.05,
     grid = TRUE,
     col.grid = "gray", lty.grid = 2, lwd.grid = 1,
     ...)

Arguments

An object of class netmeta (mandatory).

pooled

A character string indicating whether scatter plot should be drawn for fixed effect ("fixed") or random effects model ("random"). Can be abbreviated.

sel.x

sel.y

sel.z

dim

A character string indicating whether a 2- or 3-dimensional plot should be produced, either "2d" or "3d".

cex

The magnification to be used for treatment labels.

col

A vector with with colour of treatment labels.

adj.x

Value(s) in [0, 1] to specify adjustment of treatment labels on x-axis; see text.

adj.y

Value(s) in [0, 1] to specify adjustment of treatment labels on y-axis; see text.

offset.x

Offset of treatment labels on x-axis.

offset.y

Offset of treatment labels on x-axis.

arrows

A logical indicating whether arrows should be printed.

col.lines

Line colour.

lty.lines

Line type.

lwd.lines

Line width.

length

Length of arrows; see arrows.

grid

A logical indicating whether a grid lines should be added to plot.

col.grid

Colour of grid lines.

lty.grid

Line type of grid lines.

lwd.grid

Line width of grid lines.

…

Additional graphical arguments.

Details

Scatter plot ...

In order to generate 3-D plots (argument dim = "3d"), R package rgl is necessary. Note, under macOS the X.Org X Window System must be available (see https://www.xquartz.org).

References

Carlsen L, Bruggemann R (2014), Partial order methodology: a valuable tool in chemometrics. Journal of Chemometrics, 28 226--34, DOI:10.1002/cem.2569

Examples

Run this code

# NOT RUN {
# Use depression dataset
#
data(Linde2015)
#
# Define order of treatments
#
trts <- c("TCA", "SSRI", "SNRI", "NRI",
          "Low-dose SARI", "NaSSa", "rMAO-A", "Hypericum",
          "Placebo")
#
# Outcome labels
#
outcomes <- c("Early response", "Early remission")
#
# (1) Early response
#
p1 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(resp1, resp2, resp3),
               n = list(n1, n2, n3),
               studlab = id, data = Linde2015, sm = "OR")
#
net1 <- netmeta(p1,
                comb.fixed = FALSE, comb.random = TRUE,
                seq = trts, ref = "Placebo")
#
# (2) Early remission
#
p2 <- pairwise(treat = list(treatment1, treatment2, treatment3),
               event = list(remi1, remi2, remi3),
               n = list(n1, n2, n3),
               studlab = id, data = Linde2015, sm = "OR")
#
net2 <- netmeta(p2,
                comb.fixed = FALSE, comb.random = TRUE,
                seq = trts, ref = "Placebo")
#
# Partial order of treatment rankings (all five outcomes)
#
po <- netposet(netrank(net1, small.values = "bad"),
               netrank(net2, small.values = "bad"),
               outcomes = outcomes)
#
# Scatter plot
#
plot(po)
# }

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