plot.dispRity {dispRity} | R Documentation |
dispRity object plotting
Description
Plots a dispRity
object.
Usage
## S3 method for class 'dispRity'
plot(
x,
...,
type,
quantiles = c(50, 95),
cent.tend = median,
rarefaction = NULL,
elements = FALSE,
observed = FALSE,
add = FALSE,
density = NULL,
specific.args
)
Arguments
x |
A |
... |
Any optional arguments to be passed to |
type |
Either |
quantiles |
The quantiles to display (default is |
cent.tend |
A function for summarising the bootstrapped disparity values (default is |
rarefaction |
Either |
elements |
|
observed |
|
add |
|
density |
the density of shading lines to be passed to |
specific.args |
optional, a named list of arguments to be passed for some specific plot types. See details. |
Details
When specifying optional arguments with ...
in a graph with multiple elements (e.g. points
, lines
, etc...) you can specify which specific element to affect using the syntax <element>.<argument>
. For example if you want everything in the plot to be in blue at the exception of the points to be red, you can use plot(..., col = "blue", points.col = "red")
.
The different type
arguments are:
-
"continuous"
: plots the results as a continuous line. -
"box"
: plots the results as discrete box plots (note that this option ignores the user set quantiles and central tendency). -
"line"
: plots the results as discrete vertical lines with the user's set quantiles and central tendency. -
"polygon"
: identical as"line"
but using polygons rather than vertical lines. -
"preview"
: plots two dimensional preview of the space (default isc(1,2)
). WARNING: the plotted dimensions might not be representative of the full multi-dimensional space!
The different specific.args
arguments for the following options are:
if
type = "preview"
, the specific arguments can be:-
dimensions
: two specific dimensions to plot (default isspecific.args = list(dimensions = c(1,2)
); -
matrix
: which specific matrix to plot the data from (by default, all the matrices are used). -
tree
: whether to plot the underlying tree(s) or not. Can be either logical, whether to plot no tree (default isspecific.args = list(tree = FALSE)
), all trees (specific.args = list(tree = TRUE)
) or a specific set of trees (e.g.specific.args = list(tree = c(1,2))
)
-
if data is a
"test.metric"
result that was obtained with the optionsave.steps = TRUE
(seetest.metric
), it is possible to specify which steps to by specifying the following specific argument:specific.args = list(visualise.steps = c(1,4,5))
for visualising steps 1, 4 and 5 of the different shifts. By default, if the"test.metric"
was obtained with the optionsave.steps = TRUE
, four steps are displayed.if data is a
"dispRity"
and"projection"
object (fromdispRity.covar.projections
), it is possible to plot either the boxplot of disparity values for each projection (usingcorrelation.plot = NULL
; default) or to plot the correlation between two calculated elements (e.g.correlation.plot = c("position", "distance")
).
When plotting "randtest"
or "test.metric"
data or when using type = "preview"
a legend is plotted by default. To remove the legend you can use the argument legend = FALSE
. You can control specific arguments for the legend using the ...
optional arguments preceded by legend.
. For example, to change the legend position you can use legend.x = "topleft"
or legend.x = 4.2
and legend.y = 1.23
. General legend
arguments such as col
, legend
, pch
, etc... are recycled by the function but can always be specified using this syntax.
Author(s)
Thomas Guillerme
See Also
dispRity
, summary.dispRity
, null.test
, dtt.dispRity
, model.test
, model.test.sim
, test.metric
Examples
## Load the disparity data based on Beck & Lee 2014
data(disparity)
## Discrete plotting
plot(disparity, type = "box")
## Using polygons rather than boxes (quantiles and central tendency can be
## set by the user)
plot(disparity, type = "polygon", quantiles = c(10, 50, 95),
cent.tend = mean)
## Using different options
plot(disparity, type = "line", elements = TRUE, ylim = c(0, 3),
xlab = ("Time (Ma)"), ylab = "disparity")
## Continuous plotting (all default options)
plot(disparity, type = "continuous")
## Rarefactions plots
plot(disparity, rarefaction = TRUE)
## Observed data
plot(disparity, observed = TRUE)
## Observed data with graphical details
plot(disparity, observed = list("pch" = 19, col = "blue", cex = 4))
## For plotting dispRity objects with the dual classes "randtest", "dtt",
## "model.test", "model.sim" and "test.metric" see the examples
## in the specific function manuals from the "See also" section above