R: A List of ERGMM Parameter Configuration

ergmm.par.list {latentnet} | R Documentation

## 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 matrixes become 3-dimensional arrays, with the original matrixes indexed by their first dimension. See `terms.ergmm` 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,β,μ,σ,δ,γ,σ_δ,σ_γ,|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|…), depending on the model, the log-probability or log-density of the network conditional on all the parameters.

`lpZ`

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

`lpbeta`

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

`lpRE`

  \log p(δ,γ|σ_δ,σ_γ), the log-density of all random effects, conditional on their respective variances.

`lpLV`

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

`lpREV`

  \log p(σ_δ,σ_γ), 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`

[Package latentnet version 2.10.5 Index]