# rTerm: Wrapper Functions for terms in gpe In pre: Prediction Rule Ensembles

## Description

Wrapper functions for terms in gpe.

## Usage

 ```1 2 3 4 5``` ```rTerm(x) lTerm(x, lb = -Inf, ub = Inf, scale = 1/0.4) eTerm(x, scale = 1/0.4) ```

## Arguments

 `x` Input symbol. `lb` Lower quantile when winsorizing. `-Inf` yields no winsorizing in the lower tail. `ub` Lower quantile when winsorizing. `Inf` yields no winsorizing in the upper tail. `scale` Inverse value to time `x` by. Usually the standard deviation is used. `0.4 / scale` is used as the multiplier as suggested in Friedman & Popescu (2008) and gives each linear term the same a-priori influence as a typical rule.

## Details

The motivation to use wrappers is to ease getting the different terms as shown in the examples and to simplify the formula passed to `cv.glmnet` in `gpe`. `lTerm` potentially rescales and/or winsorizes `x` depending on the input. `eTerm` potentially rescale `x` depending on the input.

## Value

`x` potentially transformed with additional information provided in the attributes.

## References

Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916-954.

`gpe`, `gpe_trees` `gpe_linear` `gpe_earth`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```mt <- terms( ~ rTerm(x1 < 0) + rTerm(x2 > 0) + lTerm(x3) + eTerm(x4), specials = c("rTerm", "lTerm", "eTerm")) attr(mt, "specials") # \$rTerm # [1] 1 2 # # \$lTerm # [1] 3 # # \$eTerm # [1] 4 ```