mboost (version 2.1-0)

mboost-package: mboost: Model-Based Boosting

Description

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

NEWS in 2.0-series

Version 2.0 comes with new features, is faster and more accurate in some aspects. In addition, some changes to the user interface were necessary: Subsetting mboost objects changes the object. At each time, a model is associated with a number of boosting iterations which can be changed (increased or decreased) using the subset operator.

The center argument in bols was renamed to intercept. Argument z renamed to by.

The base-learners bns and bss are deprecated and replaced by bbs (which results in qualitatively the same models but is computationally much more attractive).

New features include new families (for example for ordinal regression) and the which argument to the coef and predict methods for selecting interesting base-learners. Predict methods are much faster now.

The memory consumption could be reduced considerably, thanks to sparse matrix technology in package Matrix. Resampling procedures run automatically in parallel if package multicore is available.

The most important advancement is a generic implementation of the optimizer in function mboost_fit.

Details

ll{ Package: mboost Type: Package Version: 2.1-0 Date: 2011-11-15 License: GPL-2 LazyLoad: yes LazyData: yes }

This package is intended for modern regression modelling and stands in-between classical generalized linear and additive models, as for example implemented by lm, glm, or gam, and machine-learning approaches for complex interactions models, most prominently represented by gbm and randomForest.

All functionality in this package is based on the generic implementation of the optimization algorithm (function mboost_fit) that allows for fitting linear, additive, and interaction models (and mixtures of those) in low and high dimensions. The response may be numeric, binary, ordered, censored or count data.

Both theory and applications are discussed by Buehlmann and Hothorn (2007). UseRs without a basic knowledge of boosting methods are asked to read this introduction before analyzing data using this package. The examples presented in this paper are available as package vignette mboost_illustrations.

Note that the model fitting procedures in this package DO NOT automatically determine an appropriate model complexity. This task is the responsibility of the data analyst.

References

Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477--505.

Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Mattthias Schmid and Benjamin Hofner (2010), Model-based Boosting 2.0. Journal of Machine Learning Research, 11, 2109 -- 2113.

See Also

The main fitting functions include: gamboost for boosted (generalized) additive models, glmboost for boosted linear models and blackboost for boosted trees. See there for more details and further links.

Examples

data("bodyfat")
  set.seed(290875)

  ### model conditional expectation of DEXfat given
  model <- mboost(DEXfat ~ 
      bols(age) +                 ### a linear function of age
      btree(hipcirc, waistcirc) + ### a non-linear interaction of
                                  ### hip and waist circumference
      bbs(kneebreadth),           ### a smooth function of kneebreadth
      data = bodyfat, control = boost_control(mstop = 100))

  ### bootstrap for assessing `optimal' number of boosting iterations
  cvm <- cvrisk(model, papply = lapply)

  ### restrict model to mstop(cvm)
  model[mstop(cvm), return = FALSE]
  mstop(model)

  ### plot age and kneebreadth
  layout(matrix(1:2, nc = 2))
  plot(model, which = c("age", "kneebreadth"))

  ### plot interaction of hip and waist circumference
  attach(bodyfat) 
  nd <- expand.grid(hipcirc = h <- seq(from = min(hipcirc),
                                  to = max(hipcirc),
                                  length = 100),
                    waistcirc = w <- seq(from = min(waistcirc),
                                  to = max(waistcirc),
                                  length = 100))
  plot(model, which = 2, newdata = nd)
  detach(bodyfat)

  ### customized plot
  layout(1)
  pr <- predict(model, which = "hip", newdata = nd)
  persp(x = h, y = w, z = matrix(pr, nrow = 100, ncol = 100))