colourvision (version 2.1.0)

RNLplot: Receptor noise limited model 2D and 1D plot

Description

Plots receptor noise limited model (RNL) for trichromatic and dichromatic animals.

Usage

RNLplot(model, photo, item="R1",
        vectors=TRUE, vnames=TRUE, vsize="auto",
        xlab="x", ylab="y", xlim="auto", ylim="auto", asp=1, ...)

Arguments

model

Output of a colour vision model.

photo

Number of photoreceptor types.

item

Model output item to be plotted. Default plots stimulus data. See RNLmodel.

vectors

Whether vectors representing direction of photoreceptor outputs should be plotted.

vnames

Whether vector names should be plotted.

vsize

Length of vectors to be plotted. Default calculates length automatically.

xlab

x-axis range. Default calculates range automatically. See par function.

ylab

y-axis range. Default calculates range automatically. See par function.

xlim

see par function. Default calculates xlim automatically.

ylim

see par function. Default calculates ylim automatically.

asp

see plot function.

...

Other arguments passed to plot function.

Author

Felipe M. Gawryszewski

See Also

CTTKhexagon, CTTKhexagon3D, EMtriangle, EMtetrahedron, RNLplot3d, plot.colourvision, plot3d.colourvision

Examples

#dichromat
C<-photor(lambda.max=c(450,550))
Rb <- data.frame(300:700, rep(7, length(300:700)))
data("D65")
R1.1<-logistic(x=seq(300,700,1), x0=500, L=50, k=0.04)
R1.2<-logistic(x=seq(300,700,1), x0=400, L=50, k=0.04)
w<-R1.1[,1]
R1.1<-R1.1[,2]+10
R1.2<-R1.2[,2]+10
R1<-data.frame(w=w, R1.1=R1.1, R1.2=R1.2)
model<-RNLmodel(model="log",
       R1=R1, Rb=Rb, I=D65, C=C,
       noise=TRUE, e = c(0.13, 0.06))
plot(model)

#trichromat
C<-photor(lambda.max=c(350,450,550))
Rb <- data.frame(300:700, rep(7, length(300:700)))
data("D65")
R1.1<-logistic(x=seq(300,700,1), x0=500, L=50, k=0.04)
R1.2<-logistic(x=seq(300,700,1), x0=400, L=50, k=0.04)
w<-R1.1[,1]
R1.1<-R1.1[,2]+10
R1.2<-R1.2[,2]+10
R1<-data.frame(w=w, R1.1=R1.1, R1.2=R1.2)
model<-RNLmodel(model="log",
       R1=R1, Rb=Rb, I=D65, C=C,
       noise=TRUE, e = c(0.13, 0.06, 0.12))
plot(model)