coef.gcdnet: Get coefficients or make coefficient predictions from a...

In gcdnet: The (Adaptive) LASSO and Elastic Net Penalized Least Squares, Logistic Regression, Hybrid Huberized Support Vector Machines, Squared Hinge Loss Support Vector Machines and Expectile Regression using a Fast Generalized Coordinate Descent Algorithm

Get coefficients or make coefficient predictions from a "gcdnet" object.

Description

Computes the coefficients or returns a list of the indices of the nonzero coefficients at the requested values for lambda from a fitted gcdnet object.

Usage

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

Arguments

object	fitted gcdnet model object.
s	value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type	type "coefficients" computes the coefficients at the requested values for s. Type "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.

Value

The object returned depends on type.

Author(s)

Yi Yang, Yuwen Gu and Hui Zou

Maintainer: Yi Yang <[e-mail]>

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/gcdnet

Gu, Y., and Zou, H. (2016). "High-dimensional generalizations of asymmetric least squares regression and their applications." The Annals of Statistics, 44(6), 2661–2694.

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

See Also

predict.gcdnet method

Examples

data(FHT)
fit1 <- gcdnet(x = FHT$x,y = FHT$y)
coef(fit1, type = "coef", s = c(0.1,0.005))
coef(fit1, type = "nonzero")

gcdnet documentation built on Aug. 14, 2022, 9:05 a.m.