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.

## Details

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.

## Dictionary

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

LearnerSurvParametric$new() mlr_learners$get("surv.parametric")
lrn("surv.parametric")


## Meta Information

• Type: "surv"

• Predict Types: distr, lp, crank

• Feature Types: logical, integer, numeric, factor

• Properties: weights

• Packages: survival distr6 set6

## References

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

Other survival learners: LearnerSurvBlackboost, LearnerSurvCVGlmnet, LearnerSurvCoxPH, LearnerSurvFlexible, LearnerSurvGBM, LearnerSurvGamboost, LearnerSurvGlmboost, LearnerSurvGlmnet, LearnerSurvKaplan, LearnerSurvMboost, LearnerSurvNelson, LearnerSurvObliqueRSF, LearnerSurvPenalized, LearnerSurvRandomForestSRC, LearnerSurvRanger, LearnerSurvRpart, LearnerSurvSVM

## Super classes

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

## Methods

### Public methods

Inherited methods

### Method new()

Creates a new instance of this R6 class.

### Arguments

deep

Whether to make a deep clone.