The ergm.eta function calculates and returns eta, mapped from
theta using the etamap object created by ergm.etamap.
The ergm.etagrad function caculates and returns the gradient of eta
mapped from theta using the etamap object created by ergm.etamap. If the
gradient is only intended to be a multiplier for some vector, the more
efficient ergm.etagradmult is recommended.
The ergm.etagradmult function calculates and returns the product of the
gradient of eta with a vector v
The ergm.etamap function takes a model object and creates a mapping
from the model parameters, theta, to the canonical (linear) eta parameters;
the mapping is carried out by ergm.eta
ergm.eta(theta, etamap)ergm.etagrad(theta, etamap)
ergm.etagradmult(theta, v, etamap)
ergm.etamap(model)
the curved model parameters
the list of values that constitutes the theta-> eta mapping and is returned by ergm.etamap
a vector of the same length as the vector of mapped eta parameters
model object, as returned by ergm.getmodel
for ergm.eta: eta the canonical eta parameters as mapped from theta
for ergm.etagrad: etagrad a matrix of the gradient of eta
for ergm.etagradmult: ans the vector that is the product of the gradient of eta and v; infinite values are replaced by (+-)10000
for ergm.etamap the theta -> eta mapping given by a list of the following:
canonical : a numeric vector whose ith entry specifies whether the ith component of theta is canonical (via non- negative integers) or curved (via zeroes)
offsetmap : a logical vector whose ith entry tells whether the ith coefficient of the canonical parameterization was "offset", i.e fixed
offset : a logical vector whose ith entry tells whether the ith model term was offset/fixed
offsettheta: a logical vector whose ith entry tells whether the ith curved theta coeffient was offset/fixed;
curved : a list with one component per curved EF term in the model containing
from : the indices of the curved theta parameter that are to be mapped from
to : the indices of the canonical eta parameters to be mapped to
map : the map provided by <InitErgmTerm>
gradient: the gradient function provided by InitErgmTerm
cov : the eta covariance ??, possibly always NULL (no <Init> function creates such an item)
etalength : the length of the eta vector
This function is only 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 sufficient statistics (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.
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.