Calls survival::survreg().

  • lp is predicted by using an internally defined predict method, see details

  • distr is predicted by using an internally defined predict method, see details

  • crank is identical to lp

This learner allows you to choose a distribution and a model form to compose a predicted survival probability distribution. Note: Just because any combination of distribution and model form is possible, this does not mean it will necessarily be sensible or interpretable.


The internal predict method is implemented in mlr3proba, which 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) $$S(t) = \frac{S_0(t)}{exp(-lp) + (1-exp(-lp)) S_0(t)}$$

where \(S_0\) is the estimated baseline survival distribution (in this case with a given parametric form), and \(lp\) is the predicted linear predictor.


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


Meta Information

  • Type: "surv"

  • Predict Types: distr, lp, crank

  • Feature Types: logical, integer, numeric, factor

  • Properties: weights

  • Packages: survival distr6 set6


Kalbfleisch JD, Prentice RL (2002). The Statistical Analysis of Failure Time Data. John Wiley & Sons, Inc. doi: 10.1002/9781118032985 .

See also

Super classes

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


Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.



Method clone()

The objects of this class are cloneable with this method.


LearnerSurvParametric$clone(deep = FALSE)



Whether to make a deep clone.