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.

## 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.

## Construction

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

Type: "surv"

Predict Types: `distr, lp, crank`

Feature Types: `logical, integer, numeric, factor`

Packages: survival distr6

## References

Kalbfleisch, J. D., Prentice, R. L. (2002).
The Statistical Analysis of Failure Time Data.
John Wiley & Sons.
doi: 10.1002/9781118032985
.

## See also

Other survival learners:
`LearnerSurvBlackboost`

,
`LearnerSurvCVGlmnet`

,
`LearnerSurvCoxPH`

,
`LearnerSurvFlexible`

,
`LearnerSurvGBM`

,
`LearnerSurvGamboost`

,
`LearnerSurvGlmboost`

,
`LearnerSurvGlmnet`

,
`LearnerSurvKaplan`

,
`LearnerSurvMboost`

,
`LearnerSurvNelson`

,
`LearnerSurvPenalized`

,
`LearnerSurvRandomForestSRC`

,
`LearnerSurvRanger`

,
`LearnerSurvRpart`

,
`LearnerSurvSVM`