receivercov-ergmTerm-ea158ece: Receiver covariate effect

In latentnet: Latent Position and Cluster Models for Statistical Networks

Receiver covariate effect

Description

Deprecated for networks without self-loops. Use nodeicov-ergmTerm, nodeifactor-ergmTerm, nodecov-ergmTerm or nodefactor-ergmTerm instead.

If the attribute is numeric, this term adds one covariate to the model equaling attrname(i). If the attribute is not numeric or force.factor==TRUE, this term adds p-1 covariates to the model, where p is the number of unique values of attrname. The kth such covariate has the value attrname(i) == value(k+1), where value(k) is the kth smallest unique value of the attrname attribute. This term only makes sense if the network is directed.

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

Usage

# binary: receivercov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: receivercov(attrname, force.factor=FALSE, mean=0, var=9)

Arguments

attrname	a character string giving the name of an attribute in the network's vertex attribute list.
force.factor	logical, indicating if attrname's value should be interpreted as categorical even if numeric.
mean, var	prior mean and variance.

Note

This term can only be used with directed networks.

See Also

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

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

latentnet documentation built on May 29, 2024, 2:36 a.m.