# plot.cv.vda.r: Plot a cv.vda.r object In VDA: VDA

## Description

Plot a the cross validation error across lambda values

## Usage

 ```1 2``` ```## S3 method for class 'cv.vda.r' plot(x, ...) ```

## Arguments

 `x` Object of class 'cv.vda.r', the result of a call to `cv.vda.r`. `...` Not used.

## Details

Plots the k-fold cross validation testing error for values across a different lambda values. Use `cv.vda.r` to produce the object of class "cv.vda.r."

## Author(s)

Edward Grant, Xia Li, Kenneth Lange, Tong Tong Wu

Maintainer: Edward Grant [email protected]

## 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.

`vda.r`, `cv.vda.r`
 ``` 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``` ```# load data data(zoo) # feature matrix without intercept x <- zoo[,2:17] # class vector y <- zoo[,18] # lambda vector lam.vec <- (1:10)/10 # run 10 fold cross validation across lambdas cv <- cv.vda.r(x, y, 10, lam.vec) # plot CV results plot(cv) # Perform VDA with CV-selected optimal lambda out <- vda.r(x,y,cv\$lam.opt) # Predict five cases based on VDA 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(out, fivecases) ```