Calls survival::survreg from package survival.

## Details

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";

## Dictionary

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

### Method clone()

The objects of this class are cloneable with this method.

LearnerSurvParametric$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples # stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("surv.parametric")) print(learner) #> <LearnerSurvParametric:surv.parametric> #> * Model: - #> * Parameters: list() #> * Packages: survival, pracma #> * Predict Type: distr #> * Feature types: logical, integer, numeric, factor #> * Properties: weights # available parameters: learner$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"