ergm.eta function calculates and returns eta, mapped
from theta using the
etamap object, usually attached as the
$etamap element of an
ergm.etagrad function caculates and returns
the gradient of eta mapped from theta using the etamap object
ergm.etamap. If the gradient is only intended
to be a multiplier for some vector, the more efficient
ergm.etagradmult is recommended.
ergm.etagradmult function calculates and
returns the product of the gradient of eta with a vector
1 2 3 4 5
the curved model parameters
the list of values that describes the theta -> eta
mapping, usually attached as
a vector of the same length as the vector of mapped eta parameters
These functions are mainly important in the case of curved exponential family models, i.e., those in which the parameter of interest (theta) is not a linear function of the natural parameters (eta) in the exponential-family model. In non-curved models, we may assume without loss of generality that eta(theta)=theta.
A succinct description of how eta(theta) is incorporated into an ERGM is given by equation (5) of Hunter (2007). See Hunter and Handcock (2006) and Hunter (2007) for further details about how eta and its derivatives are used in the estimation process.
ergm.eta, the canonical eta parameters as mapped
ergm.etagrad, a matrix of the gradient of eta
with respect to theta.
ergm.etagradmult, the vector that is the product
of the gradient of eta and
Hunter, D. R. and M. S. Handcock (2006). Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15: 565–583.
Hunter, D. R. (2007). Curved exponential family models for social networks. Social Networks, 29: 216–230.
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