EMlasso: EM algorithm for lasso penalty

In HDPenReg: High-Dimensional Penalized Regression

Description

EM algorithm for lasso penalty

Usage

EMlasso(X, y, lambda, maxSteps = 1000, intercept = TRUE,
  model = c("linear", "logistic"), burn = 50, threshold = 1e-08,
  eps = 1e-05, epsCG = 1e-08)

Arguments

X	the matrix (of size n*p) of the covariates.
y	a vector of length n with the response.
lambda	a sequence of l1 penalty regularization term. If no sequence is provided, the function computes his own sequence.
maxSteps	Maximal number of steps for EM algorithm.
intercept	If TRUE, there is an intercept in the model.
model	"linear" or "logistic"
burn	Number of steps before thresholding some variables to zero.
threshold	Zero tolerance. Coefficients under this value are set to zero.
eps	Epsilon for the convergence of the EM algorithm.
epsCG	Epsilon for the convergence of the conjugate gradient.

Value

A list containing :

step

Vector containing the number of steps of the algorithm for every lambda.

variable

List of vector of the same length as lambda. The i-th item contains the index of non-zero coefficients for the i-th lambda value.

coefficient

List of vector of the same length as lambda. The i-th item contains the non-zero coefficients for the i-th lambda value.

lambda

Vector containing the lambda values.

mu

Intercept.

Author(s)

Quentin Grimonprez, Serge Iovleff

See Also

EMcvlasso

Examples

dataset=simul(50,100,0.4,1,10,matrix(c(0.1,0.9,0.02,0.02),nrow=2))
result=EMlasso(dataset$data,dataset$response)
# Obtain estimated coefficient in matrix format
coefficient = listToMatrix(result)

HDPenReg documentation built on May 2, 2019, 6:09 p.m.