# predict.enet

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##### Make predictions or extract coefficients from a fitted elastic net model

While enet() produces the entire path of solutions, predict.enet allows one to extract a prediction at a particular point along the path.

Keywords
methods, regression
##### Usage
predict.enet(object, newx, s, type = c("fit", "coefficients"), mode =
c("step","fraction", "norm", "penalty"),naive=FALSE, ...)
##### Arguments
object
A fitted enet object
newx
If type="fit", then newx should be the x values at which the fit is required. If type="coefficients", then newx can be omitted.
s
a value, or vector of values, indexing the path. Its values depends on the mode= argument. By default (mode="step").
type
If type="fit", predict returns the fitted values. If type="coefficients", predict returns the coefficients. Abbreviations allowed.
mode
Mode="step" means the s= argument indexes the LARS-EN step number, and the coefficients will be returned corresponding to the values corresponding to step s. If mode="fraction", then s should be a number between 0 and 1, and it refers to the ratio of the
naive
IF naive is True, then the naive elastic net fit is returned.
...
##### Details

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

##### Value

• Either a vector/matrix of fitted values, or a vector/matrix of coefficients.

##### References

Zou and Hastie (2004) "Regularization and Variable Selection via the Elastic Net" In press, Journal of the Royal Statistical Society, Series B.

print, plot, enet

• predict.enet
##### Examples
data(diabetes)
attach(diabetes)
object <- enet(x,y,lambda=0.1)
### make predictions at the values in x, at each of the
### steps produced in object
fits <- predict.enet(object, x, type="fit")
### extract the coefficient vector with L1 norm=2000
coef2000 <- predict(object, s=2000, type="coef", mode="norm")
### extract the coefficient vector with L1 norm fraction=0.45
coef.45 <- predict(object, s=0.45, type="coef", mode="fraction")
detach(diabetes)
Documentation reproduced from package elasticnet, version 1.02, License: GPL version 2 or newer

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