# NOT RUN {
## use iris data
## it is known that feature 3 and 4 are more important.
data(iris)
set.seed(100)
subid = sample(1:150,50)
iris.dat = as.matrix(iris[subid,1:4])
iris.lab = as.factor(iris[subid,5])
## try different ranking methods
mysig = 6
out1 = do.specu(iris.dat, sigma=mysig, ranking="method1")
out2 = do.specu(iris.dat, sigma=mysig, ranking="method2")
out3 = do.specu(iris.dat, sigma=mysig, ranking="method3")
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=iris.lab, main="SPECU::method1")
plot(out2$Y, pch=19, col=iris.lab, main="SPECU::method2")
plot(out3$Y, pch=19, col=iris.lab, main="SPECU::method3")
par(opar)
# }
# NOT RUN {
# }
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