# plotParetoEmp: Pareto front visualization In GPareto: Gaussian Processes for Pareto Front Estimation and Optimization

## Description

Plot the Pareto front with step functions.

## Usage

 ```1 2 3 4 5 6 7 8``` ```plotParetoEmp( nondominatedPoints, add = TRUE, max = FALSE, bounds = NULL, alpha = 0.5, ... ) ```

## Arguments

 `nondominatedPoints` points considered to plot the Pareto front with segments, matrix with one point per row, `add` optional boolean indicating whether a new graphic should be drawn, `max` optional boolean indicating whether to display a Pareto front in a maximization context, `bounds` for 3D, optional 2*nobj matrix of boundaries `alpha` for 3D, optional value in [0,1] for transparency `...` additional values to be passed to the `lines` function.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```#------------------------------------------------------------ # Simple example #------------------------------------------------------------ x <- c(0.2, 0.4, 0.6, 0.8) y <- c(0.8, 0.7, 0.5, 0.1) plot(x, y, col = "green", pch = 20) plotParetoEmp(cbind(x, y), col = "green") ## Alternative plotParetoEmp(cbind(x, y), col = "red", add = FALSE) ## With maximization plotParetoEmp(cbind(x, y), col = "blue", max = TRUE) ## 3D plots library(rgl) set.seed(5) X <- matrix(runif(60), ncol=3) Xnd <- t(nondominated_points(t(X))) plot3d(X) plot3d(Xnd, col="red", size=8, add=TRUE) plot3d(x=min(Xnd[,1]), y=min(Xnd[,2]), z=min(Xnd[,3]), col="green", size=8, add=TRUE) X.range <- diff(apply(X,2,range)) bounds <- rbind(apply(X,2,min)-0.1*X.range,apply(X,2,max)+0.1*X.range) plotParetoEmp(nondominatedPoints = Xnd, add=TRUE, bounds=bounds, alpha=0.5) ```

GPareto documentation built on May 31, 2021, 5:09 p.m.