dot-check_survival_time_plausibility: Internal function to test plausibility of provided survival...

In familiar: End-to-End Automated Machine Learning and Model Evaluation

Internal function to test plausibility of provided survival times.

Description

This function checks whether non-positive outcome time is present in the data. This may produce unexpected results for some packages. For example, glmnet will not train if an instance has a survival time of 0 or lower.

Usage

.check_survival_time_plausibility(
  data,
  outcome_type,
  outcome_column,
  check_stringency = "strict"
)

Arguments

data	Data set as loaded using the .load_data function.
outcome_type	(recommended) Type of outcome found in the outcome column. The outcome type determines many aspects of the overall process, e.g. the available feature selection methods and learners, but also the type of assessments that can be conducted to evaluate the resulting models. Implemented outcome types are: binomial: categorical outcome with 2 levels. multinomial: categorical outcome with 2 or more levels. count: Poisson-distributed numeric outcomes. continuous: general continuous numeric outcomes. survival: survival outcome for time-to-event data. If not provided, the algorithm will attempt to obtain outcome_type from contents of the outcome column. This may lead to unexpected results, and we therefore advise to provide this information manually. Note that competing_risk survival analysis are not fully supported, and is currently not a valid choice for outcome_type.
outcome_column	(recommended) Name of the column containing the outcome of interest. May be identified from a formula, if a formula is provided as an argument. Otherwise an error is raised. Note that survival and competing_risk outcome type outcomes require two columns that indicate the time-to-event or the time of last follow-up and the event status.

familiar documentation built on Sept. 30, 2024, 9:18 a.m.