plot.colourvision {colourvision}

Plot colour vision models into chromaticity diagrams

Description

Plotting method for objects of class colourvision. Plotting method for animals with two or three photoreceptor types.

Usage

## S3 method for class 'colourvision'
plot(x, ...)

Arguments

x

Object of class "colourvision".

...

Additional arguments passed to the plot function. See CTTKhexagon for Chittka (1992) model (CTTKmodel) plotting arguments; EMtriangle and EMline for Endler and Mielke (2005) model (EMmodel) plotting arguments; RNLplot for RNL plotting arguments; and GENplot for generic model (GENmodel) plotting arguments.

Author(s)

Felipe M. Gawryszewski [e-mail]

References

Chittka, L. 1992. The colour hexagon: a chromaticity diagram based on photoreceptor excitations as a generalized representation of colour opponency. J Comp Physiol A 170:533-543.

Endler, J. A., and P. Mielke. 2005. Comparing entire colour patterns as birds see them. Biol J Linn Soc 86:405-431.

See Also

plot3d.colourvision, EMtriangle, CTTKhexagon, EMmodel, CTTKmodel, RNLmodel, RNLthres

Examples

#trichromatic
##Photoreceptor sensitivity curves
C<-photor(lambda.max=c(350,450,550))

##Gray background
Rb <- data.frame(300:700, rep(7, length(300:700)))

## Read CIE D65 standard illuminant
data("D65")

##Reflectance data
R1<-logistic(x=seq(300,700,1), x0=500, L=50, k=0.04)
R1[,2]<-R1[,2]+10

##Run models
model<-EMmodel(photo=3,
       R=R1, I=D65, Rb=Rb, C=C)
plot(model)

model<-CTTKmodel(photo=3,
       R=R1, I=D65, Rb=Rb, C=C)
plot(model)

model<-RNLmodel(model="log", photo=3,
       R1=R1, I=D65, Rb=Rb, C=C, noise=TRUE, e=c(0.13, 0.06, 0.12))
plot(model)

#colour threshold
model<-RNLthres(photo=3, I=D65, Rb=Rb, C=C,
         noise=TRUE, e=c(0.13, 0.06, 0.12))
plot(model)

#dichromatic
##Photoreceptor sensitivity curves
C<-photor(lambda.max=c(400,550))

##Run models
model<-EMmodel(photo=2,
       R=R1, I=D65, Rb=Rb, C=C)
plot(model)

model<-EMmodel(photo=2, type="edge",
       R=R1, I=D65, Rb=Rb, C=C)
plot(model)

model<-CTTKmodel(photo=2,
       R=R1, I=D65, Rb=Rb, C=C)
plot(model)

model<-RNLmodel(model="log", photo=2,
       R1=R1, I=D65, Rb=Rb, C=C, noise=TRUE, e=c(0.13, 0.06))
plot(model)

#colour threshold
model<-RNLthres(photo=2, I=D65, Rb=Rb, C=C,
         noise=TRUE, e=c(0.13, 0.06))
plot(model)