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A mlr3::TaskGenerator calling simsurv::simsurv() from package simsurv.

This generator currently only exposes a small subset of the flexibility of simsurv, and just creates a small dataset with the following numerical covariates:

  • treatment: Bernoulli distributed with hazard ratio 0.5.

  • height: Normally distributed with hazard ratio 1.

  • weight: normally distributed with hazard ratio 1.

See simsurv::simsurv() for an explanation of the hyperparameters. Initial values for hyperparameters are lambdas = 0.1, gammas = 1.5 and maxt = 5. The last one, by default generates samples which are administratively censored at \(\tau = 5\), so increase this value if you want to change this.

Dictionary

This TaskGenerator can be instantiated via the dictionary mlr_task_generators or with the associated sugar function tgen():

mlr_task_generators$get("simsurv")
tgen("simsurv")

Parameters

IdTypeDefaultLevelsRange
distcharacterweibullweibull, exponential, gompertz-
lambdasnumeric-\([0, \infty)\)
gammasnumeric-\([0, \infty)\)
maxtnumeric-\([0, \infty)\)

References

Brilleman, L. S, Wolfe, Rory, Moreno-Betancur, Margarita, Crowther, J. M (2021). “Simulating Survival Data Using the simsurv R Package.” Journal of Statistical Software, 97(3), 1–27. doi:10.18637/JSS.V097.I03 .

See also

Other TaskGenerator: mlr_task_generators_coxed, mlr_task_generators_simdens

Super class

mlr3::TaskGenerator -> TaskGeneratorSimsurv

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method help()

Opens the corresponding help page referenced by field $man.

Usage

TaskGeneratorSimsurv$help()


Method clone()

The objects of this class are cloneable with this method.

Usage

TaskGeneratorSimsurv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

  # generate 20 samples with Weibull survival distribution
  gen = tgen("simsurv")
  task = gen$generate(20)
  head(task)
#>    eventtime status   height treatment   weight
#>        <num>  <int>    <num>     <int>    <num>
#> 1:  4.722029      1 160.4491         0 91.39158
#> 2:  2.748901      1 159.5854         0 81.70784
#> 3:  1.880703      1 175.1759         0 79.92841
#> 4:  5.000000      0 173.7549         1 70.10283
#> 5:  2.758909      1 173.5845         0 77.85363
#> 6:  5.000000      0 176.7559         1 73.66173

  # generate 100 samples with exponential survival distribution and tau = 40
  gen = tgen("simsurv", dist = "exponential", gammas = NULL, maxt = 40)
  task = gen$generate(100)
  head(task)
#>    eventtime status   height treatment   weight
#>        <num>  <int>    <num>     <int>    <num>
#> 1: 28.910379      1 162.6955         1 76.32827
#> 2:  6.810159      1 168.9355         0 71.42271
#> 3: 20.444209      1 168.6686         1 70.20593
#> 4:  4.324903      1 171.0205         0 74.50941
#> 5:  7.866362      1 204.4362         0 83.79403
#> 6:  4.059363      1 165.1062         0 78.03904