loopcov-ergmTerm-bbc2e00a: Covariate effect on self-loops

In latentnet: Latent Position and Cluster Models for Statistical Networks

Covariate effect on self-loops

Description

This term adds one covariate to the model, for which x[i,i]=attrname(i) and x[i,j]=0 for i!=j. This term only makes sense if the network has self-loops.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: loopcov(attrname, mean=0, var=9)

# valued: loopcov(attrname, mean=0, var=9)

Arguments

attrname	a character string giving the name of a numeric (not categorical) attribute in the network's vertex attribute list.
mean, var	prior mean and variance.

See Also

ergmTerm for index of model terms currently visible to the package.

\Sexpr[results=rd,stage=render]{ergm:::.formatTermKeywords("ergmTerm", "loopcov", "subsection")}

latentnet documentation built on Sept. 11, 2025, 9:09 a.m.