Auxiliary function as user interface for ergmm
fitting. Typically only used when calling ergmm. It is used to
set parameters that affect the sampling but do not affect the posterior distribution.
The number of draws to be taken from the posterior distribution.
burnin
The number of initial MCMC iterations to be discarded.
interval
The number of iterations between consecutive draws.
threads
The number of chains to run. If greater than 1,
package snowFT is used to take
advantage of any multiprocessing or distributed computing
capabilities that may be available. Currently, only PVM (via
rpvm) has been tested. Note, also, that PVM daemon needs to
be started before the package is loaded.
kl.threads
If greather than 1, uses an experimental parallelized
label-switching algorithm. This is not guaranteed to work.
mle.maxit
Maximum number of iterations for computing the
starting values, posterior modes, MLEs, MKL estimates, etc..
Z.delta
Standard deviation of the proposal for the jump in the
individual latent space position, or its starting value for the tuner.
RE.delta
Standard deviation of the proposal for the jump in the
individual random effects values, or its starting value for the
tuner.
group.deltas
A scalar, a vector, or a matrix of an
appropriate size, giving the initial proposal structure for the
“group proposal” of a jump in covariate coefficients, scaling of
latent space positions, and a shift in random ffects. If a matrix of
an appropriate size is given, it is used as a matrix of coefficients
for a correlated proposal. If a vector is given, an independent
proposal is used with the corresponding elements being proposal
standard deviations. If a scalar is given, it is used as a
multiplier for an initial heuristic for the proposal structure. It
is usually best to leave this argument alone and let the adaptive
sampling be used.
pilot.runs
Number of pilot runs into which to split the
burn-in period. After each pilot run, the proposal standard
deviations and coefficients Z.delta, RE.delta, and
group.deltas are reevaluated. If set to 0, disables
adaptive sampling, and only makes a single burn-in run.
pilot.factor
Initial value for the factor by which the
coefficients gotten by a Choletsky decomposition of the pilot sample
covariance matrix are multiplied.
pilot.discard.first
Proportion of draws from the pilot run to
discard for estimating acceptance rate and group proposal
covariance.
target.acc.rate
Taget acceptance rate for the proposals
used. After a pilot run, the proposal variances are adjusted upward
if the acceptance rate is above this, and downward if below.
backoff.threshold
If a pilot run's acceptance rate is below
this, redo it with drastically reduced proposal standard deviation.
Set to 0 to disable this behavior.
backoff.factor
Factor by which to multiply the relevant
proposal standard deviation if its acceptance rate fell below the
backoff threshold.
accept.all
Forces all proposals to be accepted
unconditionally. Use only in debugging proposal distributions!
store.burnin
If TRUE, the samples from the burnin are
also stored and returned, to be used in MCMC diagnostics.
refine.user.start
If TRUE, the values passed to
ergmm in the user.start argument can be updated by the mode-finding algorithm.
Value
A list with the arguments as components.
See Also
ergmm
Examples
data(sampson)
## Shorter run than default.
samp.fit<-ergmm(samplike~euclidean(d=2,G=3)+rreceiver,
control=ergmm.control(burnin=1000,sample.size= 2000,interval=5))