backbone (version 1.0.0)

sdsm: The stochastic degree sequence model (sdsm)

Description

`sdsm` computes the proportion of generated edges above or below the observed value using the stochastic degree sequence model. Once computed, use backbone.extract to return the backbone matrix for a given alpha value.

Usage

sdsm(B, trials = 1000, model = "logit", sparse = TRUE,
  maxiter = 25, dyad = NULL, progress = FALSE)

Arguments

B

Matrix: Bipartite network

trials

Integer: Number of random bipartite graphs generated

model

String: A generalized linear model (glm) used to generate random bipartite graphs.

sparse

Boolean: If sparse matrix manipulations should be used

maxiter

Integer: Maximum number of iterations if "model" is a glm.

dyad

vector length 2: two row entries i,j. Saves each value of the i-th row and j-th column in each projected B* matrix. This is useful for visualizing an example of the empirical null edge weight distribution generated by the model. These correspond to the row and column indices of a cell in the projected matrix , and can be written as their string row names or as numeric values.

progress

Boolean: If txtProgressBar should be used to measure progress

Value

list(positive, negative, dyad_values). positive: matrix of proportion of times each entry of the projected matrix B is above the corresponding entry in the generated projection. negative: matrix of proportion of times each entry of the projected matrix B is below the corresponding entry in the generated projection. dyad_values: list of edge weight for i,j in each generated projection.

Details

The 'model' parameter can take in a 'link' function, as described by glm and family. This can be one of c('logit', 'probit', 'cauchit', 'log', 'cloglog').

During each iteration, sdsm computes a new B* matrix. This is a random bipartite matrix with about the same row and column sums as the original matrix B. If the dyad_parameter is indicated to be used in the parameters, when the B* matrix is projected, the projected value for the corresponding row and column will be saved. This allows the user to see the distribution of the edge weights for desired row and column.

References

Neal, Z. P. (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance, and other co-behaviors. Social Networks, 39, Elsevier: 84-97. DOI: 10.1016/j.socnet.2014.06.001

Examples

# NOT RUN {
sdsm_props <- sdsm(davis, trials = 100,dyad = c("EVELYN", "CHARLOTTE" ))
# }