| mlr_filters_univariate_cox {mlr3filters} | R Documentation |
Univariate Cox Survival Filter
Description
Calculates scores for assessing the relationship between individual features and the time-to-event outcome (right-censored survival data) using a univariate Cox proportional hazards model. The goal is to determine which features have a statistically significant association with the event of interest, typically in the context of clinical or biomedical research.
This filter fits a Cox Proportional Hazards model using
each feature independently and extracts the p-value that quantifies the
significance of the feature's impact on survival. The filter value is
-log10(p) where p is the p-value. This transformation is necessary
to ensure numerical stability for very small p-values. Also higher
values denote more important features. The filter works only for numeric
features so please ensure that factor variables are properly encoded, e.g.
using PipeOpEncode.
Super class
mlr3filters::Filter -> FilterUnivariateCox
Methods
Public methods
Inherited methods
Method new()
Create a FilterUnivariateCox object.
Usage
FilterUnivariateCox$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
FilterUnivariateCox$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
-
PipeOpFilter for filter-based feature selection.
Other Filter:
Filter,
mlr_filters,
mlr_filters_anova,
mlr_filters_auc,
mlr_filters_boruta,
mlr_filters_carscore,
mlr_filters_carsurvscore,
mlr_filters_cmim,
mlr_filters_correlation,
mlr_filters_disr,
mlr_filters_find_correlation,
mlr_filters_importance,
mlr_filters_information_gain,
mlr_filters_jmi,
mlr_filters_jmim,
mlr_filters_kruskal_test,
mlr_filters_mim,
mlr_filters_mrmr,
mlr_filters_njmim,
mlr_filters_performance,
mlr_filters_permutation,
mlr_filters_relief,
mlr_filters_selected_features,
mlr_filters_variance
Examples
filter = flt("univariate_cox")
filter