FHT: FHT data introduced in Friedman et al. (2010).

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

FHT data introduced in Friedman et al. (2010).

Description

The FHT data set has n = 50 observations and p = 100 predictors. The covariance between predictors Xj and Xj' has the same correlation 0.5. See details in Friedman et al. (2010).

Format

This data frame contains the following columns:

x

a matrix with 100 rows and 5000 columns

y

class labels

y_reg

response variable for regression

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/

Examples

data(FHT)

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