CV_linearc: CV Linearc (c++)

In MGallow/logitr: Penalized Logistic Regression

Description

Computes the coefficient estimates for linear regression. ridge regularization and bridge regularization optional. This function is to be used with the "linearc" function

Usage

CV_linearc(X, y, lam = 0L, alpha = 0L, penalty = "none", weights = 0L,
  intercept = TRUE, kernel = FALSE, method = "SVD", tol = 1e-05,
  maxit = 10000, vec = 0L, init = 0L, K = 5L)

Arguments

X	matrix
y	matrix or vector of response values 0,1
lam	vector of tuning parameters for ridge regularization term. Defaults to 'lam = 0'
alpha	vector of tuning parameters for bridge regularization term. Defaults to 'alpha = 1.5'
penalty	choose from c('none', 'ridge', 'bridge'). Defaults to 'none'
intercept	Defaults to TRUE
method	optimization algorithm. Choose from 'IRLS' or 'MM'. Defaults to 'IRLS'
tol	tolerance - used to determine algorithm convergence. Defaults to 1e-5
maxit	maximum iterations. Defaults to 1e5
vec	optional vector to specify which coefficients will be penalized
init	optional initialization for MM algorithm
K	specify number of folds in cross validation, if necessary

Value

returns best lambda, best alpha, cv.errors

MGallow/logitr documentation built on May 6, 2019, 12:06 a.m.