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 <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 [[alternative HTML version deleted]]