pavo (version 1.0.0)

coldist: Color distances

Description

Calculates color distances. When data are the result of vismodel, it applies the receptor-noise model of Vorobyev et al. (1998) to calculate color distances with noise based on relative photoreceptor densities. It also accepts colspace data from the hexagon, colour-opponent-coding, categorical, and cielab models, in which case euclidean distances (hexagon, cielab, categorical) or manhattan distances (coc) are returned.

Usage

coldist(modeldata, noise = c("neural", "quantum"), subset = NULL, achro = TRUE, qcatch = NULL, n = c(1, 2, 2, 4), weber = 0.1, weber.ref = "longest", weber.achro = 0.1, v, n1, n2, n3, n4)

Arguments

modeldata

(required) quantum catch color data. Can be the result from vismodel, or colspace. Data may also be independently calculated quantum catches, in the form of a data frame with columns representing photoreceptors.

noise

how the noise will be calculated. (Ignored for colspace objects if model is not a receptor noise model (i.e. hexagon, colour-opponent-coding, categorical, and cielab models)):

• neural: noise is proportional to the Weber fraction and is independent of the intensity of the signal received (i.e. assumes bright conditions).
• quantum: noise is the sum of the neural noise and receptor noise, and is thus proportional to the Weber fraction and inversely proportional to the intensity of the signal received (the quantum catches). Note that the quantum option will only work with objects of class vismodel.

subset

If only some of the comparisons should be returned, a character vector of length 1 or 2 can be provided, indicating which samples are desired. The subset vector must match the labels of the imput samples, but partial matching (and regular expressions) are supported.

achro

Logical. If TRUE, last column of the data frame is used to calculate the achromatic contrast, with noise based on the Weber fraction given by the argument weber.achro. If the data are from the hexagon model (i.e. colspace(space = 'hexagon')), it instead returns long (or 'green') receptor contrast.

qcatch

if the object is of class vismodel or colspace, this argument is ignored. If the object is a data frame of quantal catches from another source, this argument is used to specify what type of quantum catch is being used, so that the noise can be calculated accordingly:

• Qi: Quantum catch for each photoreceptor
• fi: Quantum catch according to Fechner law (the signal of the receptor channel is proportional to the logarithm of the quantum catch)

n

photoreceptor densities for the cones used in visual modeling. must have same length as number of columns (excluding achromatic receptor if used; defaults to the Pekin robin Leiothrix lutea densities: c(1,2,2,4)). Ignored for colspace objects if model is not a receptor noise model (i.e. hexagon, colour-opponent-coding, categorical, and cielab models).

weber

The Weber fraction to be used. The noise-to-signal ratio v is unknown, and therefore must be calculated based on the epirically estimated Weber fraction of one of the cone classes. v is then applied to estimate the Weber fraction of the other cones. by default, the value of 0.1 is used (the empirically estimated value for the LWS cone from Leiothrix lutea). Ignored for colspace objects if model is not a receptor noise model (i.e. hexagon, colour-opponent-coding, categorical, and cielab models).

weber.ref

the cone class used to obtain the empirical estimate of the Weber fraction used for the weber argument. By default, n4 is used, representing the LWS cone for Leiothrix lutea. Ignored for colspace objects if model is not a receptor noise model (i.e. hexagon, colour-opponent-coding, categorical, and cielab models).

weber.achro

the Weber fraction to be used to calculate achromatic contrast, when achro = TRUE. Defaults to 0.1. Ignored for colspace objects if model is not a receptor noise model (i.e. hexagon, colour-opponent-coding, categorical, and cielab models).

n1, n2, n3, n4, v

deprecated arguments. see below.

Value

A data frame containing up to 4 columns. The first two (patch1, patch2) refer to the two colors being contrasted; dS is the chromatic contrast (delta S) and dL is the achromatic contrast (delta L). Units are JND's in the receptor-noise model, euclidean distances in the hexagon, cielab, and categorical colorspaces, and manhattan distances in the color-opponent-coding space.

Note on previous versions

previous versions of coldist calculated receptor noise using the arguments v for the individual cone noise-to-signal ratio and n1,n2,n3,n4 for the relative cone densities. These arguments have been replaced by weber and n, which takes a vector of relative cone densities. weber.ref allows the user to specify which receptor to use as the reference to obtain the desired Weber fraction, and coldist calculates internally the value of v to be used when calculating the Weber fraction for the remaining cones. This allows a more explicit choice of Weber fraction, without the need to find the right value of v to use in order to obtain the desired signal-to-noise ratio. Additionally, by allowing n to be entered as a vector, coldist can now handle visual systems with more than four photoreceptors. In addition, the achromatic noise is calculated based on the weber.achro argument directly, and not based on v and n4 as before.

References

Vorobyev, M., Osorio, D., Bennett, A., Marshall, N., & Cuthill, I. (1998). Tetrachromacy, oil droplets and bird plumage colours. Journal Of Comparative Physiology A-Neuroethology Sensory Neural And Behavioral Physiology, 183(5), 621-633.

Hart, N. S. (2001). The visual ecology of avian photoreceptors. Progress In Retinal And Eye Research, 20(5), 675-703.

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

Examples

## Not run: 
# # Dichromat
# data(flowers)
# vis.flowers <- vismodel(flowers, visual = 'canis', relative = FALSE)
# didist.flowers <- coldist(vis.flowers)
# 
# # Trichromat 
# vis.flowers <- vismodel(flowers, visual = 'apis', relative = FALSE)
# tridist.flowers <- coldist(vis.flowers)
# 
# # Trichromat, color-hexagon model (euclidean distances)
# vis.flowers <- vismodel(flowers, visual = 'apis', qcatch = 'Ei', 
#                         relative = FALSE, vonkries = TRUE, achro = 'l', bkg = 'green')
# hex.flowers <- colspace(vis.flowers, space = 'hexagon')
# hexdist.flowers <- coldist(hex.flowers)
# 
# # Trichromat, color-opponent-coding model (manhattan distances)
# vis.flowers <- vismodel(flowers, visual = 'apis', qcatch = 'Ei', relative = FALSE, vonkries = TRUE)
# coc.flowers <- colspace(vis.flowers, space = 'coc')
# hexdist.flowers <- coldist(coc.flowers)
# 
# # Tetrachromat
# data(sicalis)
# vis.sicalis <- vismodel(sicalis, visual = 'avg.uv', relative = FALSE)
# tetradist.sicalis.n <- coldist(vis.sicalis)
# 
# # This will also work, but give you several warnings you shouldn't ignore!!
# col.sicalis <- colspace(vis.sicalis)
# tetradist.sicalis.n <- coldist(col.sicalis)
# 
# tetradist.sicalis.q <- coldist(vis.sicalis, noise = 'quantum')
# ## End(Not run)