Survival Flexible Parametric Spline Learner
mlr_learners_surv.flexible.Rd
Flexible parametric spline learner.
Calls flexsurv::flexsurvspline()
from flexsurv.
Details
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: \(lp = \beta X\).
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("surv.flexible")
mlr_learnerslrn("surv.flexible")
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma
Parameters
Id | Type | Default | Levels | Range |
bhazard | untyped | - | - | |
k | integer | 0 | \([0, \infty)\) | |
knots | untyped | - | - | |
bknots | untyped | - | - | |
scale | character | hazard | hazard, odds, normal | - |
timescale | character | log | log, identity | - |
inits | untyped | - | - | |
rtrunc | untyped | - | - | |
fixedpars | untyped | - | - | |
cl | numeric | 0.95 | \([0, 1]\) | |
maxiter | integer | 30 | \((-\infty, \infty)\) | |
rel.tolerance | numeric | 1e-09 | \((-\infty, \infty)\) | |
toler.chol | numeric | 1e-10 | \((-\infty, \infty)\) | |
debug | integer | 0 | \([0, 1]\) | |
outer.max | integer | 10 | \((-\infty, \infty)\) |
Custom mlr3 defaults
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.
References
Royston, Patrick, Parmar, KB M (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.
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
-> LearnerSurvFlexible
Examples
learner = mlr3::lrn("surv.flexible")
print(learner)
#> <LearnerSurvFlexible:surv.flexible>: Flexible Parametric Splines
#> * Model: -
#> * Parameters: k=1
#> * Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma
#> * Predict Types: crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: weights
# available parameters:
learner$param_set$ids()
#> [1] "bhazard" "k" "knots" "bknots"
#> [5] "scale" "timescale" "inits" "rtrunc"
#> [9] "fixedpars" "cl" "maxiter" "rel.tolerance"
#> [13] "toler.chol" "debug" "outer.max"