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