ergm.mple  R Documentation 
The ergm.mple
function finds a maximizer to the psuedolikelihood
function (MPLE). It is the default method for finding the ERGM starting
coefficient values. It is normally called internally the ergm process and
not directly by the user. Generally ergmMPLE
would be called
by users instead.
ergm.pl
is an even more internal workhorse
function that prepares many of the components needed by
ergm.mple
for the regression routines that are used to
find the MPLE estimated ergm. It should not be called directly by
the user.
ergm.mple(
s,
s.obs,
init = NULL,
family = "binomial",
control = NULL,
verbose = FALSE,
...
)
ergm.pl(
state,
state.obs,
dummy,
theta.offset = NULL,
control,
ignore.offset = FALSE,
verbose = FALSE
)
init 
a vector of initial theta coefficients 
family 
the family to use in the R native routine

control 
A list of control parameters for algorithm tuning,
typically constructed with 
verbose 
A logical or an integer to control the amount of
progress and diagnostic information to be printed. 
... 
additional parameters passed from within; all will be ignored 
state, state.obs 

dummy 
A dummy parameter for backwards compatibility. It will be removed in a future version. 
theta.offset 
a numeric vector of length equal to the number of statistics of the model, specifying (positionally) the coefficients of the offset statistics; elements corresponding to free parameters are ignored. 
ignore.offset 
If 
According to \insertCiteHuHa08e;textualergm: "The maximizer of the pseudolikelihood
may thus easily be found (at least in principle) by using logistic
regression as a computational device." In order for this to work, the
predictors of the logistic regression model must be calculated. These are
the change statistics as described in Section 3.2 of \insertCiteHuHa08e;textualergm,
put into matrix form so that each pair of nodes is one row whose values are
the vector of change statistics for that node pair. The ergm.pl function
computes these change statistics and the ergm.mple function implements the
logistic regression using R's glm()
function. Generally, neither ergm.mple
nor ergm.pl should be called by users if the logistic regression output is
desired; instead, use the ergmMPLE
function.
In the case where the ERGM is a dyadic independence model, the MPLE is the same as the MLE. However, in general this is not the case and, as \insertCiteDuGi09f;textualergm warn, the statistical properties of MPLEs in general are somewhat mysterious.
MPLE values are used even in the case of dyadic dependence models as starting points for the MCMC algorithm.
ergm.mple
returns an ergm object as a list
containing several items; for details see the return list in the
ergm
ergm.pl
returns a list containing:
xmat 
the compressed and possibly sampled matrix of change statistics 
xmat.full 
as 
zy 
the corresponding vector of responses, i.e. tie values 
foffset 
if 
wend 
the vector of weights for 
ergmMPLE
,
ergm
,control.ergm
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