Plots reflectance spectra in the appropriate colorspace.

# S3 method for colspace
plot(x, ...)



(required) an object of class colspace.


additional graphical options, which vary by modeled space. Refer to their individual documentation:

Also see par.


A colorspace plot appropriate to the input data.


Smith T, Guild J. (1932) The CIE colorimetric standards and their use. Transactions of the Optical Society, 33(3), 73-134.

Westland S, Ripamonti C, Cheung V. (2012). Computational colour science using MATLAB. John Wiley & Sons.

Chittka L. (1992). The colour hexagon: a chromaticity diagram based on photoreceptor excitations as a generalized representation of colour opponency. Journal of Comparative Physiology A, 170(5), 533-543.

Chittka L, Shmida A, Troje N, Menzel R. (1994). Ultraviolet as a component of flower reflections, and the colour perception of Hymenoptera. Vision research, 34(11), 1489-1508.

Troje N. (1993). Spectral categories in the learning behaviour of blowflies. Zeitschrift fur Naturforschung C, 48, 96-96.

Stoddard, M. C., & Prum, R. O. (2008). Evolution of avian plumage color in a tetrahedral color space: A phylogenetic analysis of new world buntings. The American Naturalist, 171(6), 755-776.

Endler, J. A., & Mielke, P. (2005). Comparing entire colour patterns as birds see them. Biological Journal Of The Linnean Society, 86(4), 405-431.

Kelber A, Vorobyev M, Osorio D. (2003). Animal colour vision - behavioural tests and physiological concepts. Biological Reviews, 78, 81 - 118.

Backhaus W. (1991). Color opponent coding in the visual system of the honeybee. Vision Research, 31, 1381-1397.

Endler, J. A. (1990) On the measurement and classification of color in studies of animal color patterns. Biological Journal of the Linnean Society, 41, 315-352.

See also




# Dichromat <- vismodel(flowers, visual = 'canis') <- colspace(, space = 'di')

# Colour hexagon <- vismodel(flowers, visual = 'apis', qcatch = 'Ei', relative = FALSE,
                        vonkries = TRUE, achro = 'l', bkg = 'green') <- colspace(, space = 'hexagon')
plot(, sectors = 'coarse')

# Tetrahedron (static)
vis.sicalis <- vismodel(sicalis, visual = 'avg.uv')
tcs.sicalis <- colspace(vis.sicalis, space = 'tcs')

# Tetrahedron (interactive)
vis.sicalis <- vismodel(sicalis, visual = 'avg.uv')
tcs.sicalis <- colspace(vis.sicalis, space = 'tcs')
tcsplot(tcs.sicalis, size = 0.005)

## Add points to interactive tetrahedron
patch <- rep(c('C','T','B'), 7) <- subset(tcs.sicalis, 'C')
tcs.breast <- subset(tcs.sicalis, 'B')
tcsplot(, col ='blue')
tcspoints(tcs.breast, col ='red')

## Plot convex hull in interactive tetrahedron
tcsplot(tcs.sicalis, col = 'blue', size = 0.005)
# }