Calls mboost::glmboost().

The dist parameter is specified slightly differently than in mboost. Whereas the latter takes in objects, in this learner instead a string is specified in order to identify which distribution to use. As the default in mboost is the Gaussian family, which is not compatible with survival models, instead we have by default "coxph".

If the value given to the Family parameter is "custom.family" then an object of class mboost::Family() needs to be passed to the custom.family parameter.

Format

R6::R6Class() inheriting from LearnerSurv.

Construction

LearnerSurvGlmboost$new()
mlr_learners$get("surv.glmboost")
lrn("surv.glmboost")

Meta Information

  • Type: "surv"

  • Predict Types: distr, crank, lp

  • Feature Types: integer, numeric, factor, logical

  • Packages: mboost distr6 survival

References

Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324--339. doi: 10.1198/016214503000125 .

Bühlmann P (2006). “Boosting for High-Dimensional Linear Models.” The Annals of Statistics, 34(2), 559--583. http://www.jstor.org/stable/25463430.

Bühlmann P, Hothorn T (2007). “Boosting Algorithms: Regularization, Prediction and Model Fitting.” Statistical Science, 22(4), 477--505. doi: 10.1214/07-sts242 .

Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B (2010). “Model-based boosting 2.0.” Journal of Machine Learning Research, 11(Aug), 2109--2113. http://www.jmlr.org/papers/v11/hothorn10a.html.

Hofner B, Mayr A, Robinzonov N, Schmid M (2012). “Model-based boosting in R: a hands-on tutorial using the R package mboost.” Computational Statistics, 29(1-2), 3--35. doi: 10.1007/s00180-012-0382-5 .

See also

Examples

library(mlr3) task = tgen("simsurv")$generate(20) learner = lrn("surv.glmboost") resampling = rsmp("cv", folds = 3) resample(task, learner, resampling)
#> <ResampleResult> of 3 iterations #> * Task: simsurv #> * Learner: surv.glmboost #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations