export_filters: Export filters
In bhklab/mRMRe: Parallelized Minimum Redundancy, Maximum Relevance (mRMR)
Description
Arguments
Value
Author(s)
Description
Export the filters
Arguments
childrenCountPerLevel
...
dataMatrix
...
priorsMatrix
...
priorsWeight
...
sampleStrata
...
sampleWeights
...
featureTypes
...
sampleCount
...
featureCount
...
sampleStratumCount
...
targetFeatureIndices
...
continuousEstimator
...
outX
...
bootstrapCount
...
miMatrix
...
Value
Exhaustively computes the minimum redudancy maximim relevance features from
the mutual information matrix, returing a list of solutions where each item
is a numeric index of selected features.
Author(s)
Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains
bhklab/mRMRe documentation built on Sept. 3, 2021, 10:50 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Arguments Value Author(s)
Description
Export the filters
Arguments
childrenCountPerLevel |
... |
dataMatrix |
... |
priorsMatrix |
... |
priorsWeight |
... |
sampleStrata |
... |
sampleWeights |
... |
featureTypes |
... |
sampleCount |
... |
featureCount |
... |
sampleStratumCount |
... |
targetFeatureIndices |
... |
continuousEstimator |
... |
outX |
... |
bootstrapCount |
... |
miMatrix |
... |
Value
Exhaustively computes the minimum redudancy maximim relevance features from the mutual information matrix, returing a list of solutions where each item is a numeric index of selected features.
Author(s)
Nicolas De Jay, Simon Papillon-Cavanagh, Benjamin Haibe-Kains
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.