ergmm formula and
their parameter names.beta1(mean=0, var=9) a.k.a. intercept
a.k.a. Interceptlm, it can be excluded by adding +0 or
-1 into the model formula). It adds one covariate to the
model, for which x[i,j]=1 for all i and j. It can be used explicitly to set prior mean and variance for the
intercept term.
This term differs from the ergm's
edges term if g has self-loops.
loopcov(attrname, mean=0, var=9)attrname is a character string
giving the name of a numeric (not categorical) attribute in the
network's vertex attribute list. 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
g has self-loops.loopfactor(attrname, base=1, mean=0,
var=9)attrname argument is a character vector giving one or
more names of categorical attributes in the network's vertex
attribute list. This term adds multiple covariates to the
model, one for each of (a subset of) the unique values of the
attrname attribute (or each combination of the
attributes given). Each of these covariates has
x[i,i]=1 if attrname(i)==l, where l is
that covariate's level, and x[i,j]=0 otherwise. To
include all attribute values se base=0 -- because the
sum of all such statistics equals twice the number of self-loops
and hence a linear dependency would arise in any model also
including loops. Thus, the base argument tells
which value(s) (numbered in order according to the sort
function) should be omitted. The default value, base=1,
means that the smallest (i.e., first in sorted order)
attribute value is omitted. For example, if the nodefactor("fruit",
base=2:3). For an analogous term for quantitative vertex
attributes, see nodecov.attrname is a character
string giving the name of a numeric (not categorical)
attribute in the network's vertex attribute list. 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 g has self-loops.latentcov(x, attrname=NULL, mean=0, var=9) Deprecated for networks without self-loops. Use
edgecov instead.
x is either a matrix of
covariates on each pair of vertices, a network, or an edge attribute on g;
if the latter, optional argument
attrname provides the name of the edge attribute to
use for edge values. latentcov can be called more
than once, to model the effects of multiple covariates. Note that
some covariates can be more conveniently specified using the
following terms.
sendercov(attrname, force.factor=FALSE, mean=0, var=9) Deprecated for networks without self-loops. Use
nodeocov, nodeofactor,
nodecov or nodefactor instead.
attrname is a character string giving the name of an
attribute in the network's vertex attribute list.
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 $k$th such covariate has the value attrname(i) == value(k+1), where
value(k) is the $k$th smallest unique value of the
attrname attribute. This term only makes
sense if g is directed.
receivercov(attrname, force.factor=FALSE, mean=0, var=9) Deprecated for networks without self-loops. Use
nodeicov, nodeifactor,
nodecov or nodefactor instead.
attrname is a character string giving the name of an
attribute in the network's vertex attribute list.
If the attribute is numeric, This term adds one covariate
to the model equaling attrname(j). 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 $k$th such covariate has the value attrname(j) == value(k+1), where
value(k) is the $k$th smallest unique value of the
attrname attribute. This term only makes
sense if g is directed.
socialitycov(attrname, force.factor=FALSE, mean=0, var=9) Deprecated for networks without self-loops. Use
nodecov instead.
attrname is a character string giving the name of an
attribute in the network's vertex attribute list.
If the attribute is numeric, This term adds one covariate
to the model equaling attrname(i)+attrname(j). 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 $k$th such covariate has the value attrname(i) ==
value(k+1) + attrname(j) == value(k+1), where
value(k) is the $k$th smallest unique value of the
attrname attribute. This term makes sense whether or not
g is directed.
ergmm
terms-ergm