sdsm: Flexible Stochastic Degree Sequence Model

In schochastics/levelnet: Methods to Analyze One-mode Projections of Two-mode Networks

Flexible Stochastic Degree Sequence Model

Description

Flexible Stochastic Degree Sequence Model.

Usage

fsdsm(
  g,
  row_constr,
  proj = "true",
  model = "logit",
  max_iter = 1000,
  alpha = 0.05,
  params = list(b0 = 0.1, b1 = 5e-05, b2 = 5e-05, b3 = 5e-05, a = 0.01),
  verbose = FALSE
)

sdsm_prob(
  g,
  proj = "true",
  model = "logit",
  max_iter = 1000,
  params = list(b0 = 0.1, b1 = 5e-05, b2 = 5e-05, b3 = 5e-05, a = 0.01),
  verbose = FALSE
)

Arguments

g	igraph object. The two-mode network
row_constr	constraint matrix
proj	string. Which mode to project on ("true"/"false")
model	string. which link to be used ('logit','probit','cloglog' or 'scobit')
max_iter	number of randomly sampled networks
alpha	significance level
params	named parameter list for scobit model
verbose	print status during execution

Details

a flexible implementation of the stochastic degree sequence model, allowing for the addition of constraints (use sdsm from the backbone package for the regular model)

Value

backbone of one-mode projection

Author(s)

David Schoch

References

Neal, Zachary (2014). The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors

schochastics/levelnet documentation built on Feb. 3, 2023, 4:20 a.m.