My question is fairly simple
Here I have table X with my explanatory variables, table y with my response values and, lastly, the coefficients given by multiple linear regression with lasso regularization from the package glmnet.
I assume variable A should have a coefficient of 1 as it fits perfectly with the response variable, and E should be the exact same value with a negative coefficient instead.
My code is the following:
library(glmnet) Y1 <- as.matrix(Y) is.matrix(Y1) X1 <- as.matrix(X) is.matrix(X1) X2 <- t(X1) CV = cv.glmnet(x=X1, y=Y1, family= "gaussian", type.measure = "mae", alpha =1 ) ## plot(CV) ## best_lambda = CV$lambda.1se lasso_coef = CV$glmnet.fit$beta[, CV$glmnet.fit$lambda == best_lambda] ## fit = (glmnet(x=X1, y=Y1, family= "gaussian", alpha=1, lambda=CV$lambda.1se)) ## fit$beta[,1] plot(lasso_coef, xvar = "lambda", label = TRUE) lasso_coef <- as.matrix(fit$beta) write.table(lasso_coef, "C:/Users/Diogo/Documents/masters/LIHC/teste de regressao/regressionlasso.txt", sep="\t")
What have I done wrong?