# rbstpath: Robust Boosting Path for Nonconvex Loss Functions In bst: Gradient Boosting

## Description

Gradient boosting path for optimizing robust loss functions with componentwise linear, smoothing splines, tree models as base learners. See details below before use.

## Usage

 `1` ```rbstpath(x, y, rmstop=seq(40, 400, by=20), ctrl=bst_control(), del=1e-16, ...) ```

## Arguments

 `x` a data frame containing the variables in the model. `y` vector of responses. `y` must be in {1, -1}. `rmstop` vector of boosting iterations `ctrl` an object of class `bst_control`. `del` convergency criteria `...` arguments passed to `rbst`

## Details

This function invokes `rbst` with `mstop` being each element of vector `rmstop`. It can provide different paths. Thus `rmstop` serves as another hyper-parameter. However, the most important hyper-parameter is the loss truncation point or the point determines the level of nonconvexity. This is an experimental function and may not be needed in practice.

## Value

A length `rmstop` vector of lists with each element being an object of class `rbst`.

## Author(s)

Zhu Wang

`rbst`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```x <- matrix(rnorm(100*5),ncol=5) c <- 2*x[,1] p <- exp(c)/(exp(c)+exp(-c)) y <- rbinom(100,1,p) y[y != 1] <- -1 y[1:10] <- -y[1:10] x <- as.data.frame(x) dat.m <- bst(x, y, ctrl = bst_control(mstop=50), family = "hinge", learner = "ls") predict(dat.m) dat.m1 <- bst(x, y, ctrl = bst_control(twinboost=TRUE, coefir=coef(dat.m), xselect.init = dat.m\$xselect, mstop=50)) dat.m2 <- rbst(x, y, ctrl = bst_control(mstop=50, s=0, trace=TRUE), rfamily = "thinge", learner = "ls") predict(dat.m2) rmstop <- seq(10, 40, by=10) dat.m3 <- rbstpath(x, y, rmstop, ctrl=bst_control(s=0), rfamily = "thinge", learner = "ls") ```