Description Usage Arguments Value Author(s) References See Also Examples
Find optimal projection using PP index.
1 2 3 4 5 6 7 8 9 | PP.optimize.random(PPmethod, projdim, data, class, std=TRUE,
cooling=0.99, temp=1, r=NULL, lambda=NULL, weight=TRUE, ...)
PP.optimize.anneal(PPmethod, projdim, data, class, std=TRUE,
cooling=0.999, temp=1, energy=0.01,
r=NULL, lambda=NULL, weight=TRUE, ...)
PP.optimize.Huber(PPmethod, projdim, data, class, std=TRUE,
cooling=0.99, temp=1, r=NULL, lambda=NULL,
weight=TRUE, ...)
PP.optimize.plot(PP.opt, data, class, std=TRUE)
|
PPmethod |
Selected PP index “LDA" - LDA index “Lp" - Lp index; “PDA" - PDA index |
projdim |
dimension of projection that you want to find |
data |
data without class information |
class |
class information |
std |
decide whether data will be standardized or not before applying projection pursuit |
weight |
weight flag using in LDA index |
cooling |
parameter for optimization |
temp |
inital temperature for optimization |
energy |
parameter for simulated annealing optimization |
r |
a parameter for L_r index |
lambda |
a parameter for PDA index |
PP.opt |
the optimal projection |
... |
... |
index.best |
PP index for optimal projected data |
proj.best |
optimal projection |
Eun-kyung Lee
Lee E., Cook D., and Klinke, S. (2002) Projection Pursuit indices for supervised classification
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | data(iris)
PP.opt<-PP.optimize.random("LDA",1,iris[,1:4],iris[,5],cooling=0.999,temp=1)
PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])
PP.opt<-PP.optimize.anneal("LDA",1,iris[,1:4],iris[,5],cooling=0.999,temp=1,energy=0.01)
PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])
PP.opt<-PP.optimize.Huber("LDA",2,iris[,1:4],iris[,5],cooling=0.999,r=1)
PP.opt$index.best
PP.optimize.plot(PP.opt,iris[,1:4],iris[,5])
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