svmRegress: l1-SVR regression function
In jkcshea/l1svr: Inference for Support Vector Regression Under l1 Regularization

Description Usage Arguments Value

View source: R/svr-functions.R

This function carries out an SVR regression under l1 penalty.

1	svmRegress(Y, X, epsilon, lambda, intercept = TRUE, solver)

`Y`	Vector, dependent variable.
`X`	2d array, independent variables, excluding column of constants for the intercept.
`epsilon`	Parameter defining soft-thresholding rule.
`lambda`	Parameter to scale l1 penalty.
`intercept`	Boolean, set to `TRUE` if an intercept should be included in the regression.
`solver`	character, name of the linear programming package in R used to obtain the bounds on the treatment effect. The function supports `'gurobi'`, `'cplexapi'`, `'lpsolveapi'`. The name of the solver should be provided with quotation marks.

A list including the coefficient estimates, and solutions to the primal and dual problem that can be used to identify the support vectors and recover the coefficient estimates.

jkcshea/l1svr documentation built on March 4, 2021, 12:51 a.m.