Boosted Generalized Linear Survival Learner
mlr_learners_surv.glmboost.Rd
Fits a generalized linear survival model using a boosting algorithm.
Calls mboost::glmboost()
from mboost.
Details
distr
prediction made by mboost::survFit()
.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("surv.glmboost")
mlr_learnerslrn("surv.glmboost")
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
Parameters
Id | Type | Default | Levels | Range |
offset | numeric | - | \((-\infty, \infty)\) | |
family | character | coxph | coxph, weibull, loglog, lognormal, gehan, cindex, custom | - |
custom.family | untyped | - | - | |
nuirange | untyped | c , 0 , 100 | - | |
center | logical | TRUE | TRUE, FALSE | - |
mstop | integer | 100 | \([0, \infty)\) | |
nu | numeric | 0.1 | \([0, 1]\) | |
risk | character | inbag | inbag, oobag, none | - |
oobweights | untyped | - | ||
stopintern | logical | FALSE | TRUE, FALSE | - |
trace | logical | FALSE | TRUE, FALSE | - |
sigma | numeric | 0.1 | \([0, 1]\) | |
ipcw | untyped | 1 | - | |
na.action | untyped | :: , stats , na.omit | - | |
contrasts.arg | untyped | - | - |
References
Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324--339.
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvGLMBoost
Methods
Method new()
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.
description
Selected features are extracted with the function
mboost::variable.names.mboost()
, with
used.only = TRUE
.
return character()
.
Usage
LearnerSurvGLMBoost$new()
Examples
learner = mlr3::lrn("surv.glmboost")
print(learner)
#> <LearnerSurvGLMBoost:surv.glmboost>: Boosted Generalized Linear Model
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
#> * Predict Types: crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: weights
# available parameters:
learner$param_set$ids()
#> [1] "offset" "family" "custom.family" "nuirange"
#> [5] "center" "mstop" "nu" "risk"
#> [9] "oobweights" "stopintern" "trace" "sigma"
#> [13] "ipcw" "na.action" "contrasts.arg"