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
deep
Whether 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