are my results what they are supposed to look like?

Question

[demoraesdiogo2017]

Hello

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.

https://imgbbb.com/image/zgLnt

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?

[russhh]

you're using lasso. It will strive to both fit the data and keep the coefficients small. The balance of these two forces is influencing the coefficients that are fitted.