boostmtree-package {boostmtree}

Boosted multivariate trees for longitudinal data.

Description

Multivariate extension of Friedman's (2001) gradient descent boosting method for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. Covariate-time interactions are modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter.

Package Overview

This package contains many useful functions and users should read the help file in its entirety for details. However, we briefly mention several key functions that may make it easier to navigate and understand the layout of the package.

1. boostmtree

   This is the main entry point to the package. It grows a multivariate tree using user supplied training data. Trees are grown using the randomForestSRC R-package.

2. predict.boostmtree (predict)

   Used for prediction. Predicted values are obtained by dropping the user supplied test data down the grow forest. The resulting object has class (rfsrc, predict).

Author(s)

Hemant Ishwaran, Amol Pande and Udaya B. Kogalur

References

Friedman J.H. (2001). Greedy function approximation: a gradient boosting machine, Ann. of Statist., 5:1189-1232.

Friedman J.H. (2002). Stochastic gradient boosting. Comp. Statist. Data Anal., 38(4):367–378.

Pande A., Li L., Rajeswaran J., Ehrlinger J., Kogalur U.B., Blackstone E.H., Ishwaran H. (2017). Boosted multivariate trees for longitudinal data, Machine Learning, 106(2): 277–305.

See Also

partialPlot, plot.boostmtree, predict.boostmtree, print.boostmtree, simLong