CID-helpers: Functions to aid in the use and construction of CIDnetworks...

In bdabbs13/CIDnetworks: Generative Models for Complex Networks with Conditionally Independent Dyadic Structure

Description

Functions to aid in the use and construction of CIDnetworks objects

Usage

  l.diag (nn)
  u.diag (nn)
  ordinal.maker (vec, cuts=quantile(vec, c(0.25, 0.5, 0.75)))

  unwrap.CID.Gibbs (gibbs.out)
  mat.cov.to.edge.list.cov (Xmat, n.nodes = dim(Xmat)[1],
                            arc.list = make.arc.list(n.nodes))

Arguments

nn	The number of rows in the square matrix for which we wish to extract the lower or upper diagonal matrix.
vec	The elements to be divided into ordinal categories.
cuts	The cut points at which to divide vec into ordinal categories. Default values separate vec into quartiles.
gibbs.out	The list object of draws from the Gibbs sampler. This re-sorts the object into a matrix form for easier consumption.
Xmat	A three-dimensional array of covariates, with n.nodes rows and columns. Each slice is a different covariate.
n.nodes	Number of nodes in network
arc.list	List of potential edges in network.

Details

These functions are included for the convenience of users of CIDnetworks. l.diag and u.diag provide the indices of a matrix to extract the lower and upper diagonal elements. ordinal.maker will turn any numeric vector into a series of ordinal integers for easy use in a CIDnetworks outcome. Xmat converts a sociomatrix-style array of covariates into one that can easily be used by the COV() component.

Author(s)

A.C. Thomas <[e-mail]>

bdabbs13/CIDnetworks documentation built on Nov. 15, 2019, 2:41 a.m.