Description Details Custom mlr3 defaults Dictionary Super classes Methods References See Also Examples
A mlr3proba::LearnerSurv implementing flexible from package
flexsurv.
Calls flexsurv::flexsurvspline()
.
The distr
prediction is estimated using the fitted custom distributions
from flexsurv::flexsurvspline()
and the estimated coefficients however the prediction takes
place in this package and not in flexsurv for a much faster and more efficient
implementation.
As flexible spline models estimate the baseline hazard as the intercept, the linear predictor,
lp
, can be calculated as in the classical setting. i.e. For fitted coefficients,
β = (β0,...,βP),
and covariates X^T = (X0,...,XP)^T, where X0 is a column
of 1s: lp = βX.
k
:
Actual default: 0
Adjusted default: 1
Reason for change: The default value of 0
is equivalent to, and a much less efficient
implementation of, LearnerSurvParametric.
This Learner can be instantiated via the dictionary
mlr_learners or with the associated sugar function lrn()
:
1 2 | mlr_learners$get("surv.flexible")
lrn("surv.flexible")
|
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvFlexible
new()
Creates a new instance of this R6 class.
LearnerSurvFlexible$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvFlexible$clone(deep = FALSE)
deep
Whether to make a deep clone.
Royston P, Parmar MKB (2002). “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in Medicine, 21(15), 2175–2197. doi: 10.1002/sim.1203.
Dictionary of Learners: mlr3::mlr_learners
1 2 3 4 5 6 7 | if (requireNamespace("flexsurv")) {
learner = mlr3::lrn("surv.flexible")
print(learner)
# available parameters:
learner$param_set$ids()
}
|
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