| gamboostMSM-package {gamboostMSM} | R Documentation |
Component-wise Functional Gradient Descent Boosting of Multi State Models
Description
Gradient boosting for Cox-type multi state models: minimization of negative partial log likelihood using component- and transition-wise base-learners.
Details
This package provides function objects to fit Cox-type multi state models
using the functional gradient descent boosting algorithm as implemented in the
splendid package mboost. Therefore, function Family() for fitting
multi state models is given, including negative log partial likelihood of a
Cox-type multi state model as risk function and its negative first partial
derivative with respect to the linear predictor as working response function.
Author(s)
Holger Reulen
References
Andersen, P. K., Borgan, O., Gill, R. D., Keiding, N. (1993) Statistical Models Based on Counting Processes. Springer Series in Statistics, New York: Springer-Verlag.
Buehlmann, P. Hothorn, T. (2007) Boosting Algorithms: Regularization, Prediction and Model Fitting (with Discussion). Statistical Science, 22(4), p. 477–505.
Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M., Hofner, B. (2012) mboost: Model-Based Boosting, R package version 2.2-0. http://CRAN.R-project.org/package=mboost.
Kneib, T., Hothorn, T., Tutz, G. (2009) Variable Selection and Model Choice in Geoadditive Regression Models. BIOMETRICS 65, p. 626–634.
Ridgeway, G. (1999) The state of boosting. Computing Science and Statistics 31, p. 172–181.