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.


R6::R6Class() inheriting from LearnerSurv.


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.



Meta Information

  • Type: "surv"

  • Predict Types: distr, lp, crank

  • Feature Types: logical, integer, numeric, factor

  • 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