predict.enet

0th

Percentile

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.
...
Additonal arguments for generic print.
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.

See Also

print, plot, enet

Aliases
  • 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

Community examples

Looks like there are no examples yet.