# Receptor noise limited model 2D and 1D plot Plots receptor noise limited model (RNL) for trichromatic and dichromatic animals. ```r 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(s) Felipe M. Gawryszewski f.gawry@gmail.com ## See Also `CTTKhexagon`, `CTTKhexagon3D`, `EMtriangle`, `EMtetrahedron`, `RNLplot3d`, `plot.colourvision`, `plot3d.colourvision` ## Examples ```r #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) ```

RNLplot function