Starting from zero, the LARS-EN algorithm provides the entire sequence of coefficients and fits.

```
enet(x, y, lambda, max.steps, normalize=TRUE, intercept=TRUE,
trace = FALSE, eps = .Machine$double.eps)
```

x

matrix of predictors

y

response

lambda

Quadratic penalty parameter. lambda=0 performs the Lasso fit.

max.steps

Limit the number of steps taken; the default is ```
50 * min(m,
n-1)
```

, with m the number of variables, and n the number of samples.
One can use this option to perform early stopping.

trace

If TRUE, prints out its progress

normalize

Standardize the predictors?

intercept

Center the predictors?

eps

An effective zero

An "enet" object is returned, for which print, plot and predict methods exist.

The Elastic Net methodology is described in detail in Zou and Hastie (2004). The LARS-EN algorithm computes the complete elastic net solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit. The structure of enet() is based on lars() coded by Efron and Hastie. Some internel functions from the lars package are called. The user should install lars before using elasticnet functions.

Zou and Hastie (2005) "Regularization and
Variable Selection via the Elastic Net"
*Journal of the Royal Statistical Society, Series B, 67, 301-320*.

print, plot, and predict methods for enet

# NOT RUN { data(diabetes) attach(diabetes) ##fit the lasso model (treated as a special case of the elastic net) object1 <- enet(x,y,lambda=0) plot(object1) ##fit the elastic net model with lambda=1. object2 <- enet(x,y,lambda=1) plot(object2) ##early stopping after 50 LARS-EN steps object4 <- enet(x2,y,lambda=0.5,max.steps=50) plot(object4) detach(diabetes) # }