Description Usage Arguments Details Author(s) References See Also Examples
View source: R/plot.cv.vda.le.R
Plot a the cross validation error across lambda values
1 2 |
x |
Object of class 'cv.vda.le', the result of a call to |
... |
Not used. |
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.
Edward Grant, Xia Li, Kenneth Lange, Tong Tong Wu
Maintainer: Edward Grant edward.m.grant@gmail.com
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ### 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)
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