mlr_learners_surv.cv_coxboost | R Documentation |
Fits a survival Cox model using likelihood based boosting and interal cross-validation for the
number of steps.
Calls CoxBoost::CoxBoost()
or CoxBoost::cv.CoxBoost()
from package 'CoxBoost'.
Use LearnerSurvCoxboost and LearnerSurvCVCoxboost for Cox boosting without and with internal
cross-validation of boosting step number, respectively. Tuning using the internal optimizer in
LearnerSurvCVCoxboost may be more efficient when tuning stepno
only. However, for tuning
multiple hyperparameters, mlr3tuning and LearnerSurvCoxboost will likely give better
results.
If penalty == "optimCoxBoostPenalty"
then CoxBoost::optimCoxBoostPenalty is used to determine
the penalty value to be used in CoxBoost::cv.CoxBoost.
Three prediction types are returned for this learner, using the internal
predict.CoxBoost()
function:
lp
: a vector of linear predictors (relative risk scores), one per
observation.
crank
: same as lp
.
distr
: a 2d survival matrix, with observations as rows and time points
as columns. The internal transformation uses the Breslow estimator to compose
the survival distributions from the lp
predictions.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.cv_coxboost") lrn("surv.cv_coxboost")
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, CoxBoost, pracma
Id | Type | Default | Levels | Range |
maxstepno | integer | 100 | [0, \infty) |
|
K | integer | 10 | [2, \infty) |
|
type | character | verweij | verweij, naive | - |
folds | untyped | NULL | - | |
minstepno | integer | 50 | [0, \infty) |
|
start.penalty | numeric | - | (-\infty, \infty) |
|
iter.max | integer | 10 | [1, \infty) |
|
upper.margin | numeric | 0.05 | [0, 1] |
|
unpen.index | untyped | - | - | |
standardize | logical | TRUE | TRUE, FALSE | - |
penalty | numeric | - | (-\infty, \infty) |
|
criterion | character | pscore | pscore, score, hpscore, hscore | - |
stepsize.factor | numeric | 1 | (-\infty, \infty) |
|
sf.scheme | character | sigmoid | sigmoid, linear | - |
pendistmat | untyped | - | - | |
connected.index | untyped | - | - | |
x.is.01 | logical | FALSE | TRUE, FALSE | - |
return.score | logical | TRUE | TRUE, FALSE | - |
trace | logical | FALSE | TRUE, FALSE | - |
at.step | untyped | - | - | |
The package 'CoxBoost' is not on CRAN and has to be installed from GitHub using
remotes::install_github("binderh/CoxBoost")
.
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvCVCoxboost
new()
Creates a new instance of this R6 class.
LearnerSurvCVCoxboost$new()
selected_features()
Returns the set of selected features which have non-zero coefficients.
Calls the internal coef.CoxBoost()
function.
LearnerSurvCVCoxboost$selected_features(at_step = NULL)
at_step
(integer(1)
)
Which boosting step to get the coefficients for. If no step is given
(default), the final boosting step is used.
(character()
) vector of feature names.
clone()
The objects of this class are cloneable with this method.
LearnerSurvCVCoxboost$clone(deep = FALSE)
deep
Whether to make a deep clone.
RaphaelS1
Binder, Harald, Allignol, Arthur, Schumacher, Martin, Beyersmann, Jan (2009). “Boosting for high-dimensional time-to-event data with competing risks.” Bioinformatics, 25(7), 890–896.
Dictionary of Learners: mlr3::mlr_learners.
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.
learner = mlr3::lrn("surv.cv_coxboost")
print(learner)
# available parameters:
learner$param_set$ids()
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