lp is predicted by
distr is predicted by
mboost::survFit() which assumes a PH fit with a Breslow estimator
crank is identical to
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
If the value given to the
Family parameter is "custom.family" then an object of class
mboost::Family() needs to be passed to the
distr, crank, lp, response
integer, numeric, factor, logical
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 .
Other survival learners:
Creates a new instance of this R6 class. Importance is supported but fails tests as internally data is coerced to model matrix and original names can't be recovered.
Importance is supported but fails tests as internally data is coerced to model matrix and original names can't be recovered.
The objects of this class are cloneable with this method.
LearnerSurvGlmboost$clone(deep = FALSE)
Whether to make a deep clone.