ergm.estfun {ergm} | R Documentation |
Compute the Sample Estimating Function Values of an ERGM.
Description
The estimating function for an ERGM is the score function: the
gradient of the log-likelihood, equalling \eta'(\theta)^\top
\{g(y)-\mu(\theta)\}
, where g(y)
is a p
-vector of
observed network sufficient statistic, \mu(\theta)
is the
expected value of the sufficient statistic under the model for
parameter value \theta
, and \eta'(\theta)
is the
p
by q
Jacobian matrix of the mapping from curved
parameters to natural parmeters. If the model is linear, all
non-offset statistics are passed. If the model is curved, the score
estimating equations (3.1) by Hunter and Handcock (2006) are given
instead.
Usage
ergm.estfun(stats, theta, model, ...)
## S3 method for class 'numeric'
ergm.estfun(stats, theta, model, ...)
## S3 method for class 'matrix'
ergm.estfun(stats, theta, model, ...)
## S3 method for class 'mcmc'
ergm.estfun(stats, theta, model, ...)
## S3 method for class 'mcmc.list'
ergm.estfun(stats, theta, model, ...)
Arguments
stats |
An object representing sample statistics with observed values subtracted out. |
theta |
Model parameter |
model |
An |
... |
Additional arguments for methods. |
Value
An object of the same class as stats
containing
q
-vectors of estimating function values.
Methods (by class)
-
ergm.estfun(numeric)
: Method for numeric vectors of lengthp
. -
ergm.estfun(matrix)
: Method for matrices withp
columns. -
ergm.estfun(mcmc)
: Method formcmc
objects withp
variables. -
ergm.estfun(mcmc.list)
: Method formcmc.list
objects withp
variables.