Model.fitness.meandegree

Exponent of the power law of the degree distribution. Must be &lt;0.

alpha

Lower endpoint of the relative expected out degree
(expected out degree divided by n-1). Must be &gt;=0.

beta

Upper endpoint of the relative expected out degree
(expected out degree divided by n-1). Must be at least beta and at most 1.

gamma

Computes the relative mean out-degree of a randomly chosen node
given fitness <code>x</code> in the fitness model implemented in
<code><a rd-options="" href="/link/Model.additivelink.exponential.fitness?package=systemicrisk&version=0.4.2" data-mini-rdoc="systemicrisk::Model.additivelink.exponential.fitness">Model.additivelink.exponential.fitness</a></code>. The function
returns the mean out-degree divided by n-1.

A toolbox for systemic risk based on liabilities matrices. Contains
a Gibbs sampler for liabilities matrices where only row and column sums of the
liabilities matrix as well as some other fixed entries are observed. Includes models
for power law distribution on the degree distribution.

Axel Gandy

systemicrisk

A Toolbox for Systemic Risk

Model.fitness.meandegree function

Computes the relative mean out-degree of a randomly chosen node
given fitness <code>x</code> in the fitness model implemented in
<code><a rd-options='' href='Model.additivelink.exponential.fitness'>Model.additivelink.exponential.fitness</a></code>. The function
returns the mean out-degree divided by n-1.

Model.fitness.meandegree: Mean out-degree of a random node the fitness model

Description

Usage

Arguments