# plot.netposet

##### Scatter plot of partially order of treatment ranks

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

- Keywords
- hplot, poset, Scatterplot of partially ordered rankings

##### 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

- x
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

##### See Also

##### Examples

```
# 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)
# }
```

*Documentation reproduced from package netmeta, version 0.9-5, License: GPL (>= 2)*