Boosting methods for fitting generalized additive models for location, scale and shape (GAMLSS).
This package uses boosting algorithms for fitting GAMLSS (generalized
additive models for location, scale and shape). For information on
GAMLSS theory see Rigby and Stasinopoulos (2005), or the information
provided at http://gamlss.org. For a tutorial on
gamboostLSS see Hofner et al. (2015). Thomas et al. (2018)
developed a novel non-cyclic approach to fit
This approach is suitable for the combination with
speeds up model tuning via
The fitting methods
gamboostLSS, are alternatives for the algorithms
gamlss in the
package. They offer shrinkage of effect estimates, intrinsic variable
selecion and model choice for potentially high-dimensional data
glmboostLSS (for linear effects) and
gamboostLSS (for smooth effects) depend on their
gamboost for generalized additive models
(contained in package
mboost, see Hothorn et al. 2010,
2015) and are similar in their usage.
The package includes some pre-defined GAMLSS distributions, but the
user can also specify new distributions with
A wide range of different base-learners is available for covariate
baselearners) including linear
bols), non-linear (
bbs), random (
spatial effects (
bspatial or Markov random fields
Each bease-learner can be included seperately for each predictor. The
selection of base-learnes is crucial as it implies the kind of effect
the covariate has on each distribution parameter in the final GAMLSS.
Benjamin Hofner, Andreas Mayr, Nora Fenske, Janek Thomas, Matthias Schmid
Maintainer: Benjamin Hofner <[email protected]>
B. Hofner, A. Mayr, M. Schmid (2016). gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework. Journal of Statistical Software, 74(1), 1-31.
Mayr, A., Fenske, N., Hofner, B., Kneib, T. and Schmid, M. (2012): Generalized additive models for location, scale and shape for high-dimensional data - a flexible approach based on boosting. Journal of the Royal Statistical Society, Series C (Applied Statistics) 61(3): 403-427.
M. Schmid, S. Potapov, A. Pfahlberg, and T. Hothorn. Estimation and regularization techniques for regression models with multidimensional prediction functions. Statistics and Computing, 20(2):139-150, 2010.
Rigby, R. A. and D. M. Stasinopoulos (2005). Generalized additive models for location, scale and shape (with discussion). Journal of the Royal Statistical Society, Series C (Applied Statistics), 54, 507-554.
Stasinopoulos, D. M. and R. A. Rigby (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software 23(7).
Buehlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.
Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M. and Hofner, B. (2010). Model-based boosting 2.0. Journal of Machine Learning Research 11(Aug), 2109-2113.
Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M. and Hofner, B. (2015). mboost: Model-based boosting. R package version 2.4-2. https://CRAN.R-project.org/package=mboost
Thomas, J., Mayr, A., Bischl, B., Schmid, M., Smith, A., and Hofner, B. (2018),
Gradient boosting for distributional regression - faster tuning and improved
variable selection via noncyclical updates.
Statistics and Computing. 28: 673-687.
(Preliminary version: http://arxiv.org/abs/1611.10171).
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# Generate covariates x1 <- runif(100) x2 <- runif(100) eta_mu <- 2 - 2*x1 eta_sigma <- -1 + 2*x2 # Generate response: Negative Binomial Distribution y <- numeric(100) for( i in 1:100) y[i] <- rnbinom(1, size=exp(eta_sigma[i]), mu=exp(eta_mu[i])) # Model fitting, 300 boosting steps, same formula for both distribution parameters mod1 <- glmboostLSS( y ~ x1 + x2, families=NBinomialLSS(), control=boost_control(mstop=300), center = TRUE) # Shrinked effect estimates coef(mod1, off2int=TRUE) # Empirical risk with respect to mu plot(risk(mod1)$mu) # Empirical risk with respect to sigma plot(risk(mod1)$sigma)
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