erboost (version 1.0)

shrink.erboost: L1 shrinkage of the predictor variables in a erboost

Description

Performs recursive shrinkage in each of the trees in a erboost fit using different shrinkage parameters for each variable.

Usage

shrink.erboost(object, 
           n.trees, 
           lambda = rep(10, length(object$var.names)), 
           ...)

Arguments

object

A erboost.object

n.trees

the number of trees to use

lambda

a vector with length equal to the number of variables containing the shrinkage parameter for each variable

...

other parameters (ignored)

Value

• predF
  Predicted values from the shrunken tree
• objective
  The value of the loss function associated with the predicted values
• gradient
  A vector with length equal to the number of variables containing the derivative of the objective function with respect to beta, the logit transform of the shrinkage parameter for each variable

Warning

This function is experimental.

Details

This function is currently experimental. Used in conjunction with a gradient ascent search for inclusion of variables.

References

Y. Yang and H. Zou (2012) Nonparametric Multivariate Expectile Regression via ER-Boost, submitted to Journal of Business & Economic Statistics.

Hastie, T. J., and Pregibon, D. "Shrinking Trees." AT&T Bell Laboratories Technical Report (March 1990). http://www-stat.stanford.edu/~hastie/Papers/shrinktree.ps

See Also

shrink.erboost.pred, erboost