mlr_learners_surv.flexible | R Documentation |
Flexible parametric spline learner.
Calls flexsurv::flexsurvspline()
from flexsurv.
This learner returns two prediction types:
lp
: a vector of linear predictors (relative risk scores), for each test
observation.
Calculated using flexsurv::flexsurvspline()
and the estimated coefficients.
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, the linear predictor (lp
) is lp = \beta X
.
distr
: a survival matrix in two dimensions, where observations are
represented in rows and time points in columns.
Calculated using predict.flexsurvreg()
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.flexible") lrn("surv.flexible")
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma
Id | Type | Default | Levels | Range |
bhazard | untyped | - | - | |
k | integer | 0 | [0, \infty) |
|
knots | untyped | - | - | |
bknots | untyped | - | - | |
scale | character | hazard | hazard, odds, normal | - |
timescale | character | log | log, identity | - |
inits | untyped | - | - | |
rtrunc | untyped | - | - | |
fixedpars | untyped | - | - | |
cl | numeric | 0.95 | [0, 1] |
|
maxiter | integer | 30 | (-\infty, \infty) |
|
rel.tolerance | numeric | 1e-09 | (-\infty, \infty) |
|
toler.chol | numeric | 1e-10 | (-\infty, \infty) |
|
debug | integer | 0 | [0, 1] |
|
outer.max | integer | 10 | (-\infty, \infty) |
|
k
:
Actual default: 0
Initial value: 1
Reason for change: The default value of 0
is equivalent to, and a much less efficient
implementation of, LearnerSurvParametric.
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvFlexible
new()
Creates a new instance of this R6 class.
LearnerSurvFlexible$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvFlexible$clone(deep = FALSE)
deep
Whether to make a deep clone.
RaphaelS1
Royston, Patrick, Parmar, KB M (2002). “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in medicine, 21(15), 2175–2197.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("surv.flexible")
print(learner)
# available parameters:
learner$param_set$ids()
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