mboost (version 0.5-4)

boost_control: Control Hyper Parameters for Boosting Algorithms

Description

Definition of the initial number of boosting iterations, step size and other hyper parameters for boosting algorithms.

Usage

boost_control(mstop = 100, nu = 0.1, constraint = FALSE, 
              risk = c("inbag", "oobag", "none"), 
              savedata = TRUE, center = FALSE)

Arguments

mstop

an integer giving the number of initial boosting iterations.

nu

a double (between 0 and 1) defining the step size or shrinkage parameter.

constraint

a logical indicating whether the working responses should be restricted to $(-1, +1)$.

risk

a character indicating how the empirical risk should be computed for each boosting iteration. inbag leads risks computed for the learning sample (i.e., all non-zero weights), oobag to

savedata

a logical, should the data be saved in the returned object?

center

a logical indicating if the numerical covariates should be mean centered before fitting. Only implemented for glmboost and gamboost

Value

• An object of class boost_control, a list.

Details

Objects returned by this function specify hyper parameters of the boosting algorithms implemented in glmboost, gamboost and blackboost (via the control argument).