plotScatter3d: Visualize three-objective Pareto-front approximations.

In jakobbossek/ecr2: Evolutionary Computation in R

Visualize three-objective Pareto-front approximations.

Description

Given a data frame with the results of (multiple) runs of (multiple) different three-objective optimization algorithms on (multiple) problem instances the function generates 3D scatterplots of the obtained Pareto-front approximations.

Usage

plotScatter3d(
  df,
  obj.cols = c("f1", "f2", "f3"),
  max.in.row = 4L,
  package = "scatterplot3d",
  ...
)

Arguments

df	[data.frame] Data.frame with columns at least obj.cols, “prob” and “algorithm”.
obj.cols	[character(>= 3)] Column names of the objective functions. Default is c("f1", "f2", "f3").
max.in.row	[integer(1)] Maximum number of plots to be displayed side by side in a row. Default is 4.
package	[character(1L)] Which package to use for 3d scatterplot generation? Possible choices are “scatterplot3d”, “plot3D”, “plot3Drgl” or “plotly”. Default is “scatterplot3d”.
...	[any] Further arguments passed down to scatterplot function.

Value

Nothing

See Also

Other EMOA performance assessment tools: approximateNadirPoint(), approximateRefPoints(), approximateRefSets(), computeDominanceRanking(), emoaIndEps(), makeEMOAIndicator(), niceCellFormater(), normalize(), plotDistribution(), plotFront(), plotScatter2d(), toLatex()

jakobbossek/ecr2 documentation built on Sept. 23, 2023, 12:33 p.m.