terms.ergmm {latentnet} | R Documentation |
Model terms that can be used in an ergmm
formula.
The latentnet
package itself allows only
dyad-independent terms. In the formula for the model, the model terms are various function-like
calls, some of which require arguments, separated by +
signs.
Random Effects
latent(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)
d
G
var.mul
var
, this
argument will be used as a scaling factor for a
function of average cluster size and latent space dimension to
set var
. To set it in the prior
argument to ergmm
, use Z.var.mul
.var
prior
argument to ergmm
, use
Z.var
.var.df.mul
var.df
, this
argument is the multiplier for the square root of average
cluster size, which serves in place of var.df
. To set it in the prior
argument to ergmm
, use
Z.var.df.mul
.var.df
prior
argument to ergmm
, use
Z.var.df
.mean.var.mul
mean.var
,
the multiplier for a function of number of vertices and latent space
dimension to set mean.var
. To set it in the
prior
argument to ergmm
, use Z.mean.var.mul
.mean.var
prior
argument to ergmm
, use
Z.mean.var
.pK.mul
pK
, this argument
is the multiplier for the square root of the average cluster size,
which is used as pK
. To set it in
the prior
argument to ergmm
, use Z.pK
.pK
prior
argument to ergmm
, use Z.pK
.
Fixed Effects
Each coefficient for a fixed effect covariate has a normal prior whose
mean and variance are set by the mean
and var
parameters
of the term. For those formula terms that add more than one covariate,
a vector can be given for mean and variance. If not, the vectors given
will be repeated until the needed length is reached.
latentcov(x, attrname=NULL, mean=0, var=9)
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.
absdiff(attrname, mean=0, var=9)
attrname
is a character string giving the
name of an attribute in the network's vertex attribute list. This
term adds a covariate with the value
abs(attrname(i)-attrname(j))
for all edges.
nodematch(attrname, diff=FALSE, mean=0, var=9)
attrname
is a
character string giving the name of an attribute in the
network's vertex attribute list. When diff=FALSE
,
this term adds one covariate with the value
attrname(i)==attrname(j)
. When diff=TRUE
,
p covariates are added to the model, where p is the
number of unique values of the attrname
attribute.
The kth such covariate has the value attrname(i) == attrname(j) == value(k)
, where
value(k)
is the kth smallest unique value of the
attrname
attribute.
sendercov(attrname, force.factor=FALSE, mean=0, var=9)
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 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 g
is directed.receivercov(attrname, force.factor=FALSE, mean=0, var=9)
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 kth such covariate has the value attrname(j) == value(k+1)
, where
value(k)
is the kth 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)
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 kth such covariate has the value attrname(i) ==
value(k+1) + attrname(j) == value(k+1)
, where
value(k)
is the kth smallest unique value of the
attrname
attribute. This term makes sense whether or not
g
is directed.