coef.sdwd {sdwd}

compute coefficients for the sparse DWD

Description

Computes the coefficients or returns the indices of nonzero coefficients at chosen values of lambda from a fitted sdwd object.

Usage

## S3 method for class 'sdwd'
coef(object, s=NULL, type=c("coefficients","nonzero"), ...)

Arguments

object

A fitted sdwd object.

s

Value(s) of the L1 tuning parameter lambda for computing coefficients. Default is the entire lambda sequence obtained by sdwd.

type

"coefficients" or "nonzero"? "coefficients" computes the coefficients at given values for s; "nonzero" returns a list of the indices of the nonzero coefficients for each value of s. Default is "coefficients".

...

Not used. Other arguments to predict.

Details

s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the coef function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right lambda indices. This function is modified based on the coef function from the gcdnet and the glmnet packages.

Value

Either the coefficients at the requested values of lambda, or a list of the indices of the nonzero coefficients for each lambda.

Author(s)

Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang [e-mail]

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

predict.sdwd

Examples

data(colon)
fit = sdwd(colon$x, colon$y, lambda2=1)
c1 = coef(fit, type="coef",s=c(0.1, 0.005))
c2 = coef(fit, type="nonzero")