plotScatter2d {ecr} | R Documentation |
Visualize bi-objective Pareto-front approximations.
Description
Given a data frame with the results of (multiple) runs of (multiple)
different multi-objective optimization algorithms on (multiple) problem instances
the function generates ggplot
plots of the obtained
Pareto-front approximations.
Usage
plotScatter2d(
df,
obj.cols = c("f1", "f2"),
shape = "algorithm",
colour = NULL,
highlight.algos = NULL,
offset.highlighted = 0,
title = NULL,
subtitle = NULL,
facet.type = "wrap",
facet.args = list()
)
Arguments
df |
[ |
obj.cols |
[ |
shape |
[ |
colour |
[ |
highlight.algos |
[ |
offset.highlighted |
[ |
title |
[ |
subtitle |
[ |
facet.type |
[ |
facet.args |
[ |
Value
[ggplot
] A ggplot object.
Note
At the moment only approximations of bi-objective functions are supported.
See Also
Other EMOA performance assessment tools:
approximateNadirPoint()
,
approximateRefPoints()
,
approximateRefSets()
,
computeDominanceRanking()
,
emoaIndEps()
,
makeEMOAIndicator()
,
niceCellFormater()
,
normalize()
,
plotDistribution()
,
plotFront()
,
plotScatter3d()
,
toLatex()
Examples
## Not run:
# load examplary data
data(mcMST)
print(head(mcMST))
# no customization; use the defaults
pl = plotFronts(mcMST)
# algo PRIM is obtained by weighted sum scalarization
# Since the front is (mainly) convex we highlight these solutions
pl = plotFronts(mcMST, highlight.algos = "PRIM")
# customize layout
pl = plotFronts(mcMST, title = "Pareto-approximations",
subtitle = "based on different mcMST algorithms.", facet.args = list(nrow = 2))
## End(Not run)