sub-.SDEFSR_Rules: Filter rules in a 'SDEFSR_Rules' object returning a new...

In aklxao2/SDR: Subgroup Discovery Algorithms for R

Description

Generates a new SDEFSR_Rules object containing the rules that passed the filter specified

Usage

## S3 method for class 'SDEFSR_Rules'
SDEFSR_RulesObject[condition = T]

Arguments

SDEFSR_RulesObject	The SDEFSR_RulesObject object to filter
condition	Expression to filter the SDEFSR_Rules object

Details

This functions allows to filter the rule set by a given quality measure. The quality measures that are available are: nVars, Coverage, Unusualness, Significance, FuzzySupport, Support, FuzzyConfidence, Confidence, TPr and FPr

Examples

library(SDEFSR)
#Apply filter by unusualness
habermanRules[Unusualness > 0.05]

#Also, you can make the filter as complex as you can
#Filter by Unusualness and TPr
habermanRules[Unusualness > 0.05 & TPr > 0.9]

aklxao2/SDR documentation built on May 12, 2019, 5:35 a.m.