`mlr_learners_surv.parametric.Rd`

Calls survival::survreg from package survival.

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

The internal predict method is implemented in this package as our implementation 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`

) $$S(t) = \frac{S_0(t)}{exp(-lp) + (1-exp(-lp)) S_0(t)}$$Tobit (

`tobit`

) $$S(t) = 1 - F((t - lp)/s)$$

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

dist = "gaussian"; type = "tobit";

dist = "weibull"; type = "ph";

dist = "exponential"; type = "ph";

dist = "weibull"; type = "aft";

dist = "exponential"; type = "aft";

dist = "loglogistic"; type = "aft";

dist = "lognormal"; type = "aft";

dist = "loglogistic"; type = "po";

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")

Packages: survival, pracma

Predict Types: distr, lp, crank

Feature Types: logical, integer, numeric, factor

Properties: weights

Kalbfleisch, J. D., & Prentice, R. L. (2011). The statistical analysis of failure time data (Vol. 360). John Wiley & Sons.

RaphaelS1

`mlr3::Learner`

-> `mlr3proba::LearnerSurv`

-> `LearnerSurvParametric`

`new()`

Creates a new instance of this R6 class.

LearnerSurvParametric$new()

`clone()`

The objects of this class are cloneable with this method.

LearnerSurvParametric$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.parametric")) print(learner)#> <LearnerSurvParametric:surv.parametric> #> * Model: - #> * Parameters: list() #> * Packages: survival, pracma #> * Predict Type: distr #> * Feature types: logical, integer, numeric, factor #> * Properties: weights# available parameters: learner$param_set$ids()#> [1] "type" "na.action" "dist" "parms" #> [5] "init" "scale" "maxiter" "rel.tolerance" #> [9] "toler.chol" "debug" "outer.max" "robust" #> [13] "score" "cluster"