bridge: Fit a Bridge Estimation

Description

Fit a bridge penalized maximum likelihood. It is computed the regularization path which is consisted of lasso or ridge penalty at the a grid values for lambda

Usage

bridge(X, y, q = 1, lambda.min = ifelse(n > p, 0.001, 0.05),
  nlambda = 100, lambda, eta = 1e-07, converge = 10^10)

Arguments

Design matrix.

Response vector.

is the degree of norm which includes ridge regression with q=2 and lasso estimates with q=1 as special cases

lambda.min

The smallest value for lambda if n>p is 0.001 and 0.05 otherwise.

nlambda

The number of lambda values - default is 100

lambda

A user supplied lambda sequence. By default, the program compute a squence of values the length of nlambda.

eta

is a preselected small positive threshold value. It is deleted jth variable to make the algorithm stable and also is excluded jth variable from the final model. Default is 1e-07.

converge

is the value of converge. Defaults is 10^10. In each iteration, it is calculated by sum of square the change in linear predictor for each coefficient. The algorithm iterates until converge > eta.

Value

An object of class rbridge, a list with entries

betas

Coefficients computed over the path of lambda

lambda

The lambda values which is given at the function

Details

Computes bridge estimation

Examples

Run this code

# NOT RUN {
set.seed(2019) 
beta <- c(3, 1.5, 0, 0, 2, 0, 0, 0)
p <- length(beta)
beta <- matrix(beta, nrow = p, ncol = 1)

n = 100
X = matrix(rnorm(n*p),n,p)
y = X%*%beta + rnorm(n) 

model1 <- bridge(X, y, q = 1)
print(model1)

model2 <- bridge(X, y, q = 2)
print(model2)

# }