VDA (version 1.3)

plot.cv.vda.le: Plot a cv.vda.le object

Description

Plot a the cross validation error across lambda values

Usage

"plot"(x, ...)

Arguments

x

Object of class 'cv.vda.le', the result of a call to cv.vda.le.

...

Not used.

Details

3D plots the k-fold cross validation testing error for values across a different lambda1 and lambda2 values. Use cv.vda.le to produce the object of class "cv.vda.le".

When lam.vec.1 or lam.vec.2 is set to 0, the a 2D plot will be produced.

References

Lange, K. and Wu, T.T. (2008) An MM Algorithm for Multicategory Vertex Discriminant Analysis. Journal of Computational and Graphical Statistics, Volume 17, No 3, 527-544.

See Also

vda.le, cv.vda.le

Examples

### load zoo data
### column 1 is name, columns 2:17 are features, column 18 is class
data(zoo)

### feature matrix without intercept
x <- zoo[,2:17]

### class vector
y <- zoo[,18]

### lambda vector
lam1 <- (1:5)/100
lam2 <- (1:5)/100

### searching for the best pair, using both lasso and euclidean penalizations
cv <- cv.vda.le(x, y, kfold=3, lam.vec.1=lam1, lam.vec.2=lam2)
plot(cv)
outLE <- vda.le(x,y,cv$lam.opt[1],cv$lam.opt[2])

### searching for the best pair, using ONLY lasso penalization, set lambda 2=0 (remove comments)
#cvlasso <- cv.vda.le(x, y, kfold=3, lam.vec.1=exp(1:10)/1000, lam.vec.2=0)
#plot(cvlasso)
#cvlasso$lam.opt

### searching for the best pair, using ONLY euclidean penalization, set lambda1=0 (remove comments)
#cveuclidian <- cv.vda.le(x, y, kfold=3, lam.vec.1=0, lam.vec.2=exp(1:10)/1000)
#plot(cveuclidian)
#cveuclidian$lam.opt

# Predict five cases based on vda.le (Lasso and Euclidean penalties)
fivecases <- matrix(0,5,16)
fivecases[1,] <- c(1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0)
fivecases[2,] <- c(1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1)
fivecases[3,] <- c(0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0)
fivecases[4,] <- c(0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0)
fivecases[5,] <- c(0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0)
predict(outLE, fivecases)