solutions: Basic result of the mRMR procedure

In mRMRe: Parallelized Minimum Redundancy, Maximum Relevance (mRMR)

Basic result of the mRMR procedure

Description

The 'solutions' method allows one to access the set of selected features resulting of the mRMR algorithm. More generally, the set of feature are identified by their indices in the inputed feature set (1 being the first feature (column)). At the network level, 'solutions' consists of the topology of the network, identifying which features is connected to others.

Usage

## S4 method for signature 'mRMRe.Filter'
solutions(object, mi_threshold, causality_threshold, with_fixed_features)
## S4 method for signature 'mRMRe.Network'
solutions(object)

Arguments

object	a mRMRe.Filter or mRMRe.Network object.
mi_threshold	a numeric value used in filtering the features based on their mRMR scores, features that do not pass the threshold will be set at NA.
causality_threshold	a numeric value used in filtering the features based on their causality scores, features that do not pass the threshold will be set at NA
with_fixed_features	a boolean indicating if fixed features are used in the computation, default TRUE

Author(s)

Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains

Examples

set.thread.count(2)
data(cgps)
feature_data <- mRMR.data(data =  data.frame(cgps.ge))

# Create an mRMR filter and obtain the indices of selected features
filter <- mRMR.classic("mRMRe.Filter", data = feature_data, target_indices = 3:5,
						feature_count = 2)
solutions(filter)

# Build an mRMR-based network and obtain feature connections (topology)
network <- new("mRMRe.Network", data = feature_data, target_indices = c(1, 2),
			levels = c(2, 1), layers = 1)
solutions(network)

mRMRe documentation built on April 25, 2023, 9:13 a.m.