Description Usage Arguments Details Value Author(s) References See Also Examples
Does kfold crossvalidation for gcdnet, produces a plot,
and returns a value for lambda
. This function is modified based on the cv
function from the glmnet
package.
1 
x 

y 
response variable or class label 
lambda 
optional usersupplied lambda sequence; default is

nfolds 
number of folds  default is 5. Although 
foldid 
an optional vector of values between 1 and 
pred.loss 
loss function to use for crossvalidation error. Valid options are:
Default is 
delta 
parameter delta only used in HHSVM for computing margin based loss function, only available for 
omega 
parameter omega only used in expectile regression. Only available for 
... 
other arguments that can be passed to gcdnet. 
The function runs gcdnet
nfolds
+1 times; the
first to get the lambda
sequence, and then the remainder to
compute the fit with each of the folds omitted. The average error and standard deviation over the
folds are computed.
an object of class cv.gcdnet
is returned, which is a
list with the ingredients of the crossvalidation fit.
lambda 
the values of 
cvm 
the mean crossvalidated error  a vector of length

cvsd 
estimate of standard error of 
cvupper 
upper curve = 
cvlower 
lower curve = 
nzero 
number of nonzero coefficients at each 
name 
a text string indicating type of measure (for plotting purposes). 
gcdnet.fit 
a fitted 
lambda.min 
The optimal value of 
lambda.1se 
The largest value of 
Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <[email protected]>
Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," Journal of Computational and Graphical Statistics, 22, 396415.
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/
gcdnet
, plot.cv.gcdnet
, predict.cv.gcdnet
, and coef.cv.gcdnet
methods.
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  # fit an elastic net penalized HHSVM
# with lambda2 = 0.1 for the L2 penalty. Use the
# misclassification rate as the cross validation
# prediction loss. Use fivefold CV to choose
# the optimal lambda for the L1 penalty.
data(FHT)
set.seed(2011)
cv=cv.gcdnet(FHT$x, FHT$y, method ="hhsvm",
lambda2=0.1, pred.loss="misclass", nfolds=5, delta=1.5)
plot(cv)
# fit an elastic net penalized least squares
# with lambda2 = 0.1 for the L2 penalty. Use the
# least square loss as the cross validation
# prediction loss. Use fivefold CV to choose
# the optimal lambda for the L1 penalty.
set.seed(2011)
cv1=cv.gcdnet(FHT$x, FHT$y_reg, method ="ls",
lambda2=0.1,pred.loss="loss", nfolds=5)
plot(cv1)
# To fit a LASSO penalized logistic regression
# we set lambda2 = 0 to disable the L2 penalty. Use the
# logistic loss as the cross validation
# prediction loss. Use fivefold CV to choose
# the optimal lambda for the L1 penalty.
set.seed(2011)
cv2=cv.gcdnet(FHT$x, FHT$y, method ="logit",
lambda2 = 0, pred.loss="loss", nfolds=5)
plot(cv2)

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