mlr_learners_surv.flexible.Rd
Calls flexsurv::flexsurvspline from package flexsurv.
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,
\(\beta = (\beta_0,...,\beta_P)\),
and covariates \(X^T = (X_0,...,X_P)^T\), where \(X_0\) is a column
of \(1\)s: \(lp = \beta X\).
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
mlr_learners$get("surv.flexible") lrn("surv.flexible")
Packages: flexsurv, pracma
Predict Types: distr, crank, lp
Feature Types: logical, integer, factor, numeric
Properties: weights
k
:
Actual default: 0
Adjusted default: 1
Reason for change: The default value of 0
is equivalent to, and a much less efficient
implementation of, LearnerSurvParametric.
Royston P, Parmar MKB (2002). “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in Medicine, 21(15), 2175–2197. doi: 10.1002/sim.1203.
RaphaelS1
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvFlexible
new()
Creates a new instance of this R6 class.
LearnerSurvFlexible$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvFlexible$clone(deep = FALSE)
deep
Whether to make a deep clone.
# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.flexible")) print(learner)#> <LearnerSurvFlexible:surv.flexible> #> * Model: - #> * Parameters: k=1 #> * Packages: flexsurv, pracma #> * Predict Type: distr #> * Feature types: logical, integer, factor, numeric #> * Properties: weights# available parameters: learner$param_set$ids()#> [1] "bhazard" "k" "knots" "bknots" #> [5] "scale" "timescale" "inits" "fixedpars" #> [9] "cl" "maxiter" "rel.tolerance" "toler.chol" #> [13] "debug" "outer.max"