rbstpath {bst} R Documentation

## Robust Boosting Path for Nonconvex Loss Functions

### 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

```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`.

Zhu Wang

`rbst`

### Examples

```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")
```

[Package bst version 0.3-23 Index]