ergmm.par.list {latentnet}

A List of ERGMM Parameter Configuration

Description

A class ergmm.par.list to represent a series of parameter configurations for the same exponential random graph mixed model.

Usage

as.ergmm.par.list(x, ...)

## S3 method for class 'ergmm.par.list'
x[i]

## S3 method for class 'ergmm.par.list'
length(x)

## S3 method for class 'ergmm.par.list'
x[[i]]

## S3 method for class 'ergmm.par.list'
unstack(x, ...)

Arguments

x

an ergm.par.list object.

...

extra arguments, currently unused.

i

index of the iteration to extract.

Details

[[ operator with a numeric or integer index returns a list with the the configuration with that index. [ operator given a numeric vector returns a ergmm.par.list object with the subset of configurations with the indices given.

The structure of ergmm.par.list is derived from named lists, with each entry having an additional dimension (always the first one), indexed by configuration. That is, scalars become vectors, vectors become matrixes with the original vectors in rows, and matrices become 3-dimensional arrays, with the original matrices indexed by their first dimension. See term documentation for comon elements of these configurations. In some cases, such as when representing MCMC or optimization output, the object may also have some of the following elements:

mlp

\log p(Y,Z,\beta,\mu,\sigma,\delta,\gamma,\sigma_\delta,\sigma_\gamma,|K), the joint probability/density of network, the covariate coefficients, the latent space positions and parameters, and the random effects and their variances, conditional on cluster assignments.

lpY

\log p(Y|\dots), depending on the model, the log-probability or log-density of the network conditional on all the parameters.

lpZ

\log p(Z|\mu,\sigma,K), the log-density of latent space positions conditional on latent space or cluster parameters and cluster assignments.

lpbeta

\log p(\beta), the prior log-density of the covariate coefficients.

lpRE

\log p(\delta,\gamma|\sigma_\delta,\sigma_\gamma), the log-density of all random effects, conditional on their respective variances.

lpLV

\log p(\mu,\sigma), the prior log-density of latent space or cluster parameters (but not that of the cluster assignments).

lpREV

\log p(\sigma_\delta,\sigma_\gamma), the prior log-density of all random effect variances.

Z.rate

Proportion of single-vertex proposals accepted over the preceding interval.

beta.rate

Proportion of group proposals accepted over the preceding interval.

See Also

ergmm