plotObjectivePairs {TunePareto} | R Documentation |
Plots a matrix of Pareto front panels for each pair of objectives. The plot for n
objectives consists of n x n
panels, where the panel in row i
and column j
depicts the Pareto fronts of the i
-th and the j
-th objective. Each of the panels is drawn in the same way as plotParetoFronts2D
.
plotObjectivePairs(tuneParetoResult, drawLabels = TRUE, drawBoundaries = TRUE, labelPos = 4, fitLabels=TRUE, cex.conf=0.5, lty.fronts=1, pch.fronts=8, col.fronts, ...)
tuneParetoResult |
An object of class |
drawLabels |
If set to true, the descriptions of the configurations are printed next to the points in the plot. |
drawBoundaries |
If set to true, the upper or lower objective limits supplied in the |
labelPos |
The position of the configuration labels in the plot (if |
fitLabels |
If this parameter is true (and |
cex.conf |
The size of the configuration labels in the plots (if |
lty.fronts |
A vector of line types to use for the Pareto fronts. By default, straight lines are drawn for all fronts. |
pch.fronts |
A vector of symbols to use for points on the Pareto fronts. All points on the same front will have the same symbol. By default, an asterisk is used. |
col.fronts |
A vector of colors to use for the Pareto fronts. By default, a predefined set of colors is used. |
... |
Further graphical parameters to be passed to the |
This function does not have a return value.
tunePareto
, plotParetoFronts2D
, plotDominationGraph
# optimize the 'cost' parameter of an SVM according # to CV error, CV error variance, and CV Specificity # on two classes of the 'iris' data set r <- tunePareto(data = iris[, -ncol(iris)], labels = iris[, ncol(iris)], classifier = tunePareto.svm(), cost=c(0.001,0.005,0.01,0.05,0.1,0.5,1,5,10,50), objectiveFunctions=list(cvError(10, 10), cvErrorVariance(10, 10), cvSpecificity(10, 10, caseClass="virginica"))) # plot the matrix of Pareto fronts plotObjectivePairs(r)