CalculateRolesMultipleNetworks: Calculate roles of species in mutliple networks

In SimmonsBI/RolePredict: What the Package Does (One Line, Title Case)

Description

Calculate roles of species in mutliple networks

Usage

CalculateRolesMultipleNetworks(network_list, ...)

Arguments

network_list

A list of numeric biadjacency matrices representing interactions between two groups of nodes. Each row corresponds to a node in one level and each column corresponds to a node in the other level. Elements of M are positive numbers if nodes do interact, and 0 otherwise. Formally, M is a biadjacency matrix. When nodes i and j interact, m_ij > 0; if they do not interact, m_ij = 0. Matrix can be binary or quantitative.

...

Arguments to be passed to bmotif, such as whether motifs up to six nodes should be considered, and whether species roles should be based on weighted or binary interactions. See ?node_positions for details of the arguments that can be passed to bmotif.

Value

A list of length two, where the first element (row_roles) contains the roles of all row species from across all networks in network_list and the second element (column_roles) contains the roles of all column species from across all networks in network_list.

Each element is a data frame with one row for each occurrence of each species. The first column is the network in which that row's species occurred. The second column is the species name itself. Columns 3 onwards are one column for each node position: 23 columns if six_node is FALSE, and 74 columns if six_node is TRUE (depending on which was specified via the optional arguments passed through to bmotif using the '...' argument; default is six_node is FALSE).

Columns names are given as "npx" where x is the ID of the position as described in Simmons et al. (2019) (and originally in Appendix 1 of Baker et al. (2015)). To view the 'motif dictionary' showing which node position a given ID corresponds to, load bmotif via library(bmotif) then enter vignette("bmotif-dictionary").

For a network with A rows and P columns, by default (where level = "all") the data frame has A + P rows, one for each node. If level = "rows", the data frame will have A rows, one for each row node; if level = "columns", it will have P rows, one for each column node.

By default, the elements of this data frame will be the raw binary or weighted position measures (depending on which was requested). If normalisation is set to something other than "none", the elements will be normalised position counts as described above.

If weights_method is set to 'all', node_positions instead returns a list of length five, each containing a data.frame corresponding to one of the five weighting methods described above.

References

Baker, N., Kaartinen, R., Roslin, T., and Stouffer, D. B. (2015). Species’ roles in food webs show fidelity across a highly variable oak forest. Ecography, 38(2):130–139.

Simmons, BI, Sweering, MJM, Schillinger, M, Dicks, LV, Sutherland, WJ, Di Clemente, R. (2019). bmotif: A package for motif analyses of bipartite networks. Methods Ecol Evol; 10: 695– 701. https://doi.org/10.1111/2041-210X.13149

Examples

m1 <- matrix(c(1,0,0,1),2,2) # a sample network
m2 <- matrix(c(1,1,0,1,0,1),3,2) # another sample network
CalculateRolesMultipleNetworks(network_list = list(m1,m2))

# for six_nodes we can pass an optional argument to bmotif
CalculateRolesMultipleNetworks(network_list = list(m1,m2), six_node = TRUE)

# if networks are weighted, we can specify KJFKABDKAFHEKJAFEHFJKAEHFAKJHAFKJHFBKHAFBLJHADFVLJFHAVBLJFHBALFJHBFLJADHBVAL<GJFVJ<FGHAVAJHDF

SimmonsBI/RolePredict documentation built on Dec. 2, 2020, 8:05 a.m.