sdwd (version 1.0.5)

coef.cv.sdwd: compute coefficients from a "cv.sdwd" object

Description

Computes coefficients at chosen values of lambda from the cv.sdwd object.

Usage

# S3 method for cv.sdwd
coef(object, s=c("lambda.1se", "lambda.min"),...)

Arguments

object

A fitted cv.sdwd object, obtained by conducting the cross-validation to the sparse DWD model.

s

Value(s) of the L1 tuning parameter lambda for computing coefficients. Default value is "lambda.1se", which represents the largest lambda value achieving the cross-validation error within one standard error of the minimum. An alternative value is "lambda.min", which is the lambda incurring the least cross-validation error. s can also be numeric, being taken as the value(s) to be used.

…

Other arguments that can be passed to sdwd.

Value

The returned object depends on the choice of s and the … argument passed on to the sdwd method.

Details

This function computes the coefficients at the values of lambda suggested by the cross-validation. This function is modified based on the coef.cv function from the glmnet and the gcdnet packages.

References

Wang, B. and Zou, H. (2016) ``Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, 25(3), 826--838. https://www.tandfonline.com/doi/full/10.1080/10618600.2015.1049700

Yang, Y. and Zou, H. (2013) ``An Efficient Algorithm for Computing the HHSVM and Its Generalizations", Journal of Computational and Graphical Statistics, 22(2), 396--415. https://www.tandfonline.com/doi/full/10.1080/10618600.2012.680324

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33(1), 1--22. https://www.jstatsoft.org/v33/i01/paper

See Also

cv.sdwd and predict.cv.sdwd methods.

Examples

# NOT RUN {
data(colon)
colon$x = colon$x[ , 1:100] # this example only uses the first 100 columns 
set.seed(1)
cv = cv.sdwd(colon$x, colon$y, lambda2=1, nfolds=5)
c1 = coef(cv, s="lambda.1se")
# }