launder: Data laundering
In pievos101/DFNET: Network-guided greedy decision forest for feature subset selection
launder R Documentation
Data laundering
Description
Launders input graph and features.
Usage
launder(graph, features, threshold = NaN, threshold.quantile = NaN)
Arguments
graph
The original graph.
features
list of matrices.
threshold
see cut_off.
threshold.quantile
see cut_off.
Value
A list graph, features with the laundered graph and features.
See Also
Other data laundering techniques:
common_features(),
cut_off(),
graphed_features(),
induced.subgraph.by_name()
pievos101/DFNET documentation built on Dec. 1, 2022, 3:44 p.m.
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| launder | R Documentation |
Data laundering
Description
Launders input graph and features.
Usage
launder(graph, features, threshold = NaN, threshold.quantile = NaN)
Arguments
graph |
The original graph. |
features |
list of matrices. |
threshold |
see cut_off. |
threshold.quantile |
see cut_off. |
Value
A list graph, features with the laundered graph and features.
See Also
Other data laundering techniques:
common_features(),
cut_off(),
graphed_features(),
induced.subgraph.by_name()
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.
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