Elastic Net Regresion in R

# Elastic Net

# load the package
library(glmnet)

# load data
data(longley)
x <- as.matrix(longley[,1:6])
y <- as.matrix(longley[,7])

# fit model
fit <- glmnet(x, y, family="gaussian", alpha=0.5, lambda=0.001)

# summarize the fit
print(fit)

# make predictions
predictions <- predict(fit, x, type="link")

# summarize accuracy
mse <- mean((y - predictions)^2)
print(mse)

Call:  glmnet(x = x, y = y, family = "gaussian", alpha = 0.5, lambda = 0.001) 

     Df   %Dev Lambda
[1,]  6 0.9949  0.001
[1] 0.0590839