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]