plot the cross-validation curve

Plot cross-validation error curves with the upper and lower standard deviations versus log lambda values.

Usage

# S3 method for class 'cv.lhsc'
plot(x, sign.lambda, ...)

Arguments

x

A fitted cv.lhsc object.

sign.lambda

Against log(lambda) (default) or its negative if sign.lambda=-1.

...

Other graphical parameters being passed to plot.

Details

This function plots the cross-validation error curves.

Author

Oh-Ran Kwon and Hui Zou
Maintainer: Oh-Ran Kwon kwon0085@umn.edu

See also

cv.lhsc.

Examples

set.seed(1)
data(BUPA)
BUPA$X = scale(BUPA$X, center=TRUE, scale=TRUE)
lambda = 10^(seq(-3, 3, length.out=10))
kern = rbfdot(sigma=sigest(BUPA$X))
m.cv = cv.lhsc(BUPA$X, BUPA$y, kern,
  lambda=lambda, eps=1e-5, maxit=1e5)
m.cv
#> $lambda
#>  [1] 1.000000e+03 2.154435e+02 4.641589e+01 1.000000e+01 2.154435e+00
#>  [6] 4.641589e-01 1.000000e-01 2.154435e-02 4.641589e-03 1.000000e-03
#> 
#> $cvm
#>  [1] 0.4202899 0.4202899 0.4202899 0.4202899 0.4202899 0.4202899 0.4202899
#>  [8] 0.4202899 0.3536232 0.3217391
#> 
#> $cvsd
#>  [1] 0.02787737 0.02787737 0.02787737 0.02787737 0.02787737 0.02787737
#>  [7] 0.02787737 0.02787737 0.02695808 0.01613845
#> 
#> $cvupper
#>  [1] 0.4481672 0.4481672 0.4481672 0.4481672 0.4481672 0.4481672 0.4481672
#>  [8] 0.4481672 0.3805813 0.3378776
#> 
#> $cvlower
#>  [1] 0.3924125 0.3924125 0.3924125 0.3924125 0.3924125 0.3924125 0.3924125
#>  [8] 0.3924125 0.3266651 0.3056007
#> 
#> $lambda.min
#> [1] 0.001
#> 
#> $lambda.1se
#> [1] 0.001
#> 
#> $cvm.min
#> [1] 0.3217391
#> 
#> $cvm.1se
#> [1] 0.3217391
#> 
#> attr(,"class")
#> [1] "cv.lhsc"