bst (version 0.3-14)

rbstpath: Robust Boosting Path for Truncated Loss Functions

Description

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

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 critera

...

arguments passed to rbst

Value

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

See Also

rbst

Examples

# NOT RUN {
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, rfamily = "thinge", learner = "ls")
# }