strong.comm: Best Friend Connector algorithm
In LucaGiudice/Simpati: Pathway-based classifier exploits patient similarity network paradigm
Description
Usage
Arguments
Value
View source: R/funs_top_clustering_bffs_v7.R
Description
Undefined
Usage
1
strong.comm(m, thW = 0.6, thGroup = 0.6)
Arguments
m
adjacency matrix of a patient similarity network
thW
Default 0.6, double value between 0 and 1 determining the percentage of best friends to considers for a root (1BFS are the 60 percentage of the patients most similar to the root)
thGroup
Default 0.6, double value between 0 and 1 determining the percentage of best friends to keep in the end as final subclass
Value
name of the nodes/columns which compose the best group with the most similar best friends and the root
LucaGiudice/Simpati documentation built on Jan. 27, 2022, 11:42 p.m.
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Description Usage Arguments Value
View source: R/funs_top_clustering_bffs_v7.R
Description
Undefined
Usage
1 | strong.comm(m, thW = 0.6, thGroup = 0.6)
|
Arguments
m |
adjacency matrix of a patient similarity network |
thW |
Default 0.6, double value between 0 and 1 determining the percentage of best friends to considers for a root (1BFS are the 60 percentage of the patients most similar to the root) |
thGroup |
Default 0.6, double value between 0 and 1 determining the percentage of best friends to keep in the end as final subclass |
Value
name of the nodes/columns which compose the best group with the most similar best friends and the root
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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