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R/loss.R
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
Defines functions
ercls
hubercls
hubercls <- function(r, delta) {
(1 - r - 0.5 * delta) * (r <= 1 - delta)
+ 0.5 * (1 - r)^2 / delta * (r <= 1) * (r > 1 - delta)
}
ercls <- function(r, omega) {
abs(omega - (r < 0)) * r^2
}
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gcdnet documentation built on Aug. 14, 2022, 9:05 a.m.
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Defines functions ercls hubercls
hubercls <- function(r, delta) {
(1 - r - 0.5 * delta) * (r <= 1 - delta)
+ 0.5 * (1 - r)^2 / delta * (r <= 1) * (r > 1 - delta)
}
ercls <- function(r, omega) {
abs(omega - (r < 0)) * r^2
}
Try the gcdnet package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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