plot.cv.lrome: plot the cross-validation curve produced by cv.lrome

In emeryyi/rome: Robust Mean Estimation

Description

Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the lambda values used. This function is modified based on the plot.cv function from the glmnet package.

Usage

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

Arguments

x	fitted cv.lrome object
sign.lambda	either plot against log(lambda) (default) or its negative if sign.lambda=-1.
...	other graphical parameters to plot

Details

A plot is produced.

References

Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," Journal of Computational and Graphical Statistics, 22, 396-415.
BugReport: https://github.com/emeryyi/fastcox.git

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33, 1.
http://www.jstatsoft.org/v33/i01/

See Also

cv.lrome.

Examples

# fit an elastic net penalized logistic regression 
# with lambda2 = 1 for the L2 penalty. Use the 
# logistic loss as the cross validation 
# prediction loss. Use five-fold CV to choose 
# the optimal lambda for the L1 penalty.
data(FHT)
set.seed(2011)
cv=cv.lrome(FHT$x, FHT$y, lambda2 = 1, nfolds=5)
plot(cv)

emeryyi/rome documentation built on May 6, 2019, 9:53 a.m.