jndrot: Rotate Cartesian coordinates obtained from 'jnd2xyz()'

View source: R/jndrot.R

jndrotR Documentation

Rotate Cartesian coordinates obtained from jnd2xyz()

Description

Rotate Cartesian coordinates obtained from jnd2xyz()

Usage

jndrot(
  jnd2xyzres,
  center = c("mean", "achro"),
  ref1 = "l",
  ref2 = "u",
  axis1 = c(1, 1, 0),
  axis2 = c(0, 0, 1)
)

Arguments

jnd2xyzres

(required) the output from a jnd2xyz() call.

center

should the vectors for rotation be centered in the achromatic center ("achro") or the data centroid ("mean", the default)?

ref1

the cone to be used as a the first reference. May be NULL (for no first rotation in the 3-dimensional case) or must match name in the original data that was used for coldist(). Defaults to 'l'.

ref2

the cone to be used as a the second reference. May be NULL (for no first rotation in the 3-dimensional case) or must match name in the original data that was used for coldist(). Defaults to 'u'. (only used if data has 3 dimensions).

axis1

A vector of length 3 composed of 0's and 1's, with 1's representing the axes (x, y, z) to rotate around. Defaults to c(1, 1, 0), such that the rotation aligns with the xy plane (only used if data has 2 or 3 dimensions). Ignored if ref1 is NULL (in 3-dimensional case only)

axis2

A vector of length 3 composed of 0's and 1's, with 1's representing the axes (x, y, z) to rotate around. Defaults to c(0, 0, 1), such that the rotation aligns with the z axis (only used if data has 3 dimensions). Ignored if ref2 is NULL (in 3-dimensional case only)

Author(s)

Rafael Maia rm72@zips.uakron.edu

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, before rotating them about the data centroid
jndrot(jnd2xyz(cd.flowers))


rmaia/pavo documentation built on Jan. 19, 2024, 6:24 p.m.