updatebeta: Update the logistic regression parameters in the link...

In MEclustnet: Fit the Mixture of Experts Latent Position Cluster Model to Network Data

Description

The Metropolis-Hastings update step for the logistic regression parameters in the link probabilities model, using a surrogate proposal distribution.

Usage

updatebeta(beta, p, x.link, delta, y, epsilon, psi, psi.inv, pis,
  countbeta, rho, n.tilde)

Arguments

beta	Vector of regression coefficients in the link probabilities.
p	Length of beta.
x.link	Matrix, with n^2 - n rows and the same number of columns as covariates (including the intercept), giving the differences in covariates for all pairs of nodes.
delta	Vector of Euclidean distances between locations in the latent space of all pairs of nodes.
y	Vector version of the adjacency matrix, with the diagonal removed.
epsilon	Mean of the multivariate normal prior on beta.
psi	Covariance of the multivariate normal prior on beta.
psi.inv	Inverse covariance of the multivariate normal prior on beta.
pis	Vector of length n^2 - n providing the link probabilities between all pairs of nodes.
countbeta	Counter for number of steps for which the proposed beta value was accepted.
rho	Scaling factor to be used to adjust the acceptance rate.
n.tilde	Length of the vector version of the adjacency matrix, with the diagonal removed i.e. n^2 - n.

Details

See appendix of the paper detailed below for details.

Value

A list:

beta

The returned version of the beta parameter vector.

countbeta

The count of the number of acceptances of beta to that point in the MCMC chain.

References

Isobel Claire Gormley and Thomas Brendan Murphy. (2010) A Mixture of Experts Latent Position Cluster Model for Social Network Data. Statistical Methodology, 7 (3), pp.385-405.

See Also

MEclustnet

MEclustnet documentation built on Oct. 10, 2019, 5:04 p.m.