gs.poisson.fit.zip: Zero-Inflated Poisson Model
In neurodata/graphstats: Graph Statistics

Description Usage Arguments Value Author(s)

A function for fitting the 3-parameter (number of vertices, number of non-zero edges, lambda poisson rate parameter) Zero-Inflated Poisson Model to a graph. A poisson model is fitted to the non-zero edges.

1	gs.poisson.fit.zip(g, edge.attr = NULL, output = "matrix")

`g`	an `igraph` object or an `nxn` adjacency matrix with `n` vertices.
`edge.attr`	if `g` is a `igraph`, the name of the attribute to use for weights. Defaults to `NULL`, which assumes the graph is binary.
`communities`	an `n` vector containing the community label for each of the `n` vertices in `g`.
`edge.attr`	if `g` is a `igraph`, the name of the attribute to use for weights. Defaults to `NULL`, which assumes the graph is binary. `is.null(edge.attr)` constructs sbm on the graph as a binary adjacency matrix. `is.character(edge.attr)` constructs sbm of the graph the graph as a weighted adjacency matrix, with edge-weights for `E(g)` given by `get.edge.attribute(g, attr=edge.attr)`.
`output.type`	the type of output to produce for the between community expectations. Defaults to `matrix`. `"matrix"` produces a matrix for the between-communitity interactions. `"graph"` produces an `igraph` object for the between-community interactions.

An object of class ZIP containing the following:

n.v: the number of vertices in g.
ne.nz: the number of non-zero edges in g.
lambda: the poisson rate parameter for non-zero edges in g; also, the average non-zero edge weight of g.