mlr_learners_surv.parametric: Survival Fully Parametric Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Fully Parametric Learner

Description

Parametric survival model. Calls survivalmodels::parametric() from 'survivalmodels'.

Details

This learner allows you to choose a distribution and a model form to compose a predicted survival probability distribution.

The predict method is implemented in survivalmodels::predict.parametric(). Our implementation is more efficient for composition to distributions than survival::predict.survreg().

Three types of prediction are returned for this learner:

1. lp: a vector of linear predictors (relative risk scores), one per test observation. 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.

2. crank: same as lp.

3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. The distribution distr is composed using the lp predictions and specifying a model form in the form 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 - \Phi((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, \Phi 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' (we note that ones mostly used in the literature):

• dist = "gaussian"; form = "tobit";

• dist = "weibull"; form = "ph"; (fairly used)

• dist = "exponential"; form = "ph";

• dist = "weibull"; form = "aft"; (fairly used, default option)

• dist = "exponential"; form = "aft";

• dist = "loglogistic"; form = "aft"; (fairly used)

• dist = "lognormal"; form = "aft";

• dist = "loglogistic"; form = "po";

Custom mlr3 parameters

• discrete determines the class of the returned survival probability distribution. If FALSE (default) continuous probability distributions are returned using distr6::VectorDistribution, otherwise distr6::Matdist (faster to calculate survival measures that require a distr prediction type).

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.parametric")
lrn("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
form	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	-		-
discrete	logical	-	TRUE, FALSE	-

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvParametric

Methods

Public methods

• LearnerSurvParametric$new()

• LearnerSurvParametric$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class. returned risk from survivalmodels is hp-style ie higher value => higher risk

Usage

LearnerSurvParametric$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvParametric$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

bblodfon

References

Kalbfleisch, D J, Prentice, L R (2011). The statistical analysis of failure time data. John Wiley & Sons.

See Also

• 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.

Examples

learner = mlr3::lrn("surv.parametric")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.