Dear All

The problem is about regularization methods in multiple regression when the

independent variables are collinear. A modified regularization method with

two tuning parameters l1 and l2 and their product l1*l2 (Lambda 1 and

Lambda 2) such that l1 takes care of ridge property and l2 takes care of

LASSO property is proposed

The proposed method is given

<

https://i.stack.imgur.com/Ta8FR.jpg>

The problem is how to adapt "glmnet" to accomplish our task.

The extract of the code used is reproduced as follows;

cv.ridge<- glmnet(x, y, family="gaussian", alpha=0,

lambda=lambda1, standardize=TRUE)

cv.lasso<- glmnet(x, y, family="gaussian", alpha=1,

lambda=lambda2, standardize=TRUE)

##weight

a=1/abs(matrix(coef(cv.ridge, s=lambda1)[, 1][2:(ncol(x)+1)]

))^1

b=1/abs(matrix(coef(cv.lasso, s=lambda2)[, 1][2:(ncol(x)+1)]

))^1

c=a*b

w4 <-a+b+c

w4[w4[,1] == Inf] <- 9

# Fit modified procedure

fit<- glmnet(x, y, family="gaussian",

alpha=alpha,lambda=lambda1+lambda2, penalty.factor=w4)

The question is; Does the code address the modified procedure in as shown

in the equation? If not, suggestions are please welcome.

Thanks

--

OYEYEMI, Gafar Matanmi (Ph.D)

Reader

Department of Statistics

University of Ilorin.

Area of Specialization: Multivariate Analysis, Statistical Quality Control

& Total Quality Management.

Tel: +2348052278655, +2348068241885

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