mlr_filters_univariate_cox: Univariate Cox Survival Filter

In mlr-org/mlr3featsel: Filter Based Feature Selection for 'mlr3'

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

• FilterUnivariateCox$new()

• FilterUnivariateCox$clone()

Inherited methods

• mlr3filters::Filter$calculate()
• mlr3filters::Filter$format()
• mlr3filters::Filter$help()
• mlr3filters::Filter$print()

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Method new()

Create a FilterUnivariateCox object.

Usage

FilterUnivariateCox$new()

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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.

• Dictionary of Filters: mlr_filters

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

mlr-org/mlr3featsel documentation built on April 14, 2024, 12:17 p.m.