jndplot {pavo} | R Documentation |
Perceptually-corrected chromaticity diagrams
Description
Plot options for jnd2xyz
objects.
Usage
jndplot(
x,
arrow = c("relative", "absolute", "none"),
achro = FALSE,
arrow.labels = TRUE,
arrow.col = "darkgrey",
arrow.p = 1,
labels.cex = 1,
margin = "recommended",
square = TRUE,
...
)
Arguments
x |
(required) the output from a |
arrow |
If and how arrows indicating receptor vectors should be drawn.
Options are |
achro |
Logical. Should the achromatic variable be plotted as a
dimension? (only available for dichromats and trichromats, defaults to
|
arrow.labels |
Logical. Should labels be plotted for receptor arrows?
(defaults to |
arrow.col |
color of the arrows and labels. |
arrow.p |
scaling factor for arrows. |
labels.cex |
size of the arrow labels. |
margin |
accepts either |
square |
logical. Should the aspect ratio of the plot be held to 1:1?
(defaults to |
... |
additional parameters to be passed to |
Value
Creates a plot, details of the plot depend on the input data.
Note
the arrow
argument accepts three options:
-
"relative"
: With this option, arrows will be made relative to the data. Arrows will be centered on the data centroid, and will have an arbitrary length of half the average pairwise distance between points, which can be scaled with thearrow.p
argument. -
"absolute"
: With this option, arrows will be made to reflect the visual system underlying the data. Arrows will be centered on the achromatic point in colourspace, and will have length equal to the distance to a monochromatic point (i.e. a colour that stimulates approximately 99.9% of that receptor alone). Arrows can still be scaled using thearrow.p
argument, in which case they cannot be interpreted as described. -
"none"
: no arrows will be included.
Author(s)
Rafael Maia rm72@zips.uakron.edu
References
Pike, T.W. (2012). Preserving perceptual distances in chromaticity diagrams. Behavioral Ecology, 23, 723-728.
Examples
# Load floral reflectance spectra
data(flowers)
# Estimate quantum catches visual phenotype of a Blue Tit
vis.flowers <- vismodel(flowers, visual = 'bluetit')
# Estimate noise-weighted colour distances between all flowers
cd.flowers <- coldist(vis.flowers)
# Convert points to Cartesian coordinates in which Euclidean distances are
# noise-weighted.
propxyz <- jnd2xyz(cd.flowers)
# Plot the floral spectra in 'noise-corrected' space
plot(propxyz)