Survival Fully Parametric Learner
mlr_learners_surv.parametric.Rd
Parametric survival model.
Calls survival::survreg()
from survival.
Details
This learner allows you to choose a distribution and a model form to compose a predicted survival probability distribution.
The internal predict method is implemented in this package as our implementation is more
efficient for composition to distributions than survival::predict.survreg()
.
lp
is predicted using the formula \(lp = X\beta\) where \(X\) are the variables in the test
data set and \(\beta\) are the fitted coefficients.
The distribution distr
is composed using the lp
and specifying a model form in the
type
hyper-parameter. These are as follows, with respective survival functions,
Accelerated Failure Time (
aft
) $$S(t) = S_0(\frac{t}{exp(lp)})$$Proportional Hazards (
ph
) $$S(t) = S_0(t)^{exp(lp)}$$Proportional Odds (
po
) $$S(t) = \frac{S_0(t)}{exp(-lp) + (1-exp(-lp)) S_0(t)}$$Tobit (
tobit
) $$S(t) = 1 - F((t - lp)/s)$$
where \(S_0\) is the estimated baseline survival distribution (in this case with a given parametric form), \(lp\) is the predicted linear predictor, \(F\) is the cdf of a N(0, 1) distribution, and \(s\) is the fitted scale parameter.
Whilst any combination of distribution and model form is possible, this does not mean it will necessarily create a sensible or interpretable prediction. The following combinations are 'sensible':
dist = "gaussian"; type = "tobit";
dist = "weibull"; type = "ph";
dist = "exponential"; type = "ph";
dist = "weibull"; type = "aft";
dist = "exponential"; type = "aft";
dist = "loglogistic"; type = "aft";
dist = "lognormal"; type = "aft";
dist = "loglogistic"; type = "po";
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("surv.parametric")
mlr_learnerslrn("surv.parametric")
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survival, pracma
Parameters
Id | Type | Default | Levels | Range |
type | character | aft | aft, ph, po, tobit | - |
na.action | untyped | - | - | |
dist | character | weibull | weibull, exponential, gaussian, lognormal, loglogistic | - |
parms | untyped | - | - | |
init | untyped | - | - | |
scale | numeric | 0 | \([0, \infty)\) | |
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 | \((-\infty, \infty)\) | |
robust | logical | FALSE | TRUE, FALSE | - |
score | logical | FALSE | TRUE, FALSE | - |
cluster | untyped | - | - |
References
Kalbfleisch, D J, Prentice, L R (2011). The statistical analysis of failure time data. John Wiley & Sons.
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
-> LearnerSurvParametric
Examples
learner = mlr3::lrn("surv.parametric")
print(learner)
#> <LearnerSurvParametric:surv.parametric>: Fully Parametric Learner
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, survival, pracma
#> * Predict Types: crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: weights
# available parameters:
learner$param_set$ids()
#> [1] "type" "na.action" "dist" "parms"
#> [5] "init" "scale" "maxiter" "rel.tolerance"
#> [9] "toler.chol" "debug" "outer.max" "robust"
#> [13] "score" "cluster"