Adds a term to the model equal to the negative
Eucledean distance \(-||Z_i-Z_j||\), where
\(Z_i\) and \(Z_j\) are the positions of their
respective actors in an unobserved social space. These positions
may optionally have a finite spherical Gaussian mixture
clustering structure. This term was previously called
latent.
Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.
# binary: euclidean(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)# valued: euclidean(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)
The dimension of the latent space.
The number of groups (0 for no clustering).
In the absence of 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.
If given, the scale parameter for the
scale-inverse-chi-squared prior distribution of the
within-cluster variance. To set it in the prior argument
to ergmm, use Z.var.
In the absence of 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.
The degrees of freedom parameter for the
scale-inverse-chi-squared prior distribution of the
within-cluster variance. To set it in the prior argument
to ergmm, use Z.var.df.
In the absence of 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.
The variance of the spherical Gaussian prior
distribution of the cluster means. To set it in the prior
argument to ergmm, use Z.mean.var.
In the absence of 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.
The parameter of the Dirichilet prior distribution of
cluster assignment probabilities. To set it in the prior
argument to ergmm, use Z.pK.
The following parameters are associated with this term:
ZNumeric matrix with rows being latent space positions.
Z.K (when \(\code{G}>0\))Integer vector of cluster assignments.
Z.mean (when \(\code{G}>0\))Numeric matrix with rows being cluster means.
Z.var (when \(\code{G}>0\))Depending on the model, either a numeric vector with within-cluster variances or a numeric scalar with the overal latent space variance.
Z.pK (when \(\code{G}>0\))Numeric vector of probabilities of a vertex being in a particular cluster.
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "euclidean", "subsection")