met.instrength: Instrength

In SebastianSosa/ant: Animal Network Toolkit Software

Instrength

Description

Calculates the node metric met.instrength for all vertices.

Usage

met.instrength(M, df = NULL, dfid = NULL)

Arguments

M	a square adjacency matrix, or a list of square adjacency matrices, or an output of ANT functions stat.ds.grp, stat.df.focal, stat.net.lk.
df	a data frame of same length as the input matrix or a list of data frames if argument M is a list of matrices or an output of ANT functions stat.ds.grp, stat.df.focal, stat.net.lk.
dfid	an integer or a string indicating the column with individual ids in argument df.

Details

Instrength of a node i is the sum of the strengths of all edges directed towards node i.

Value

• An integer vector of nodes instrength if argument df is NULL.

• A list of integer vectors of nodes instrength if argument M is a list of matrices and if argument df is NULL.

• A list of arguments df with a new column for nodes instrength titled 'instrength', if argument df is not NULL.

• A list of arguments df with a new column for nodes instrength if 1) argument df is not NULL, 2) argument M is an output from ANT functions stat.ds.grp, stat.df.focal, stat.net.lk for multiple matrices permutations, and 3) argument df is a list of data frames of same length as argument M. The name of the column of each element of the list is adapted according to argument value binary.

Author(s)

Sebastian Sosa, Ivan Puga-Gonzalez.

References

Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239.

Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11), 3747-3752.

Sosa, S. (2018). Social Network Analysis, in: Encyclopedia of Animal Cognition and Behavior. Springer.

Examples

met.instrength(sim.m)
head(sim.df)
met.instrength(sim.m,df=sim.df)

SebastianSosa/ant documentation built on Sept. 23, 2023, 7:06 a.m.