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.
control.ergmm(sample.size=4000,
burnin=10000,
interval=10,
threads=1,
kl.threads=1,
mle.maxit=100,
Z.delta=0.6,
RE.delta=0.6,
group.deltas=0.4,
pilot.runs=4,
pilot.factor=0.8,
pilot.discard.first=0.5,
target.acc.rate=0.234,
backoff.threshold=0.05,
backoff.factor=0.2,
accept.all=FALSE,
store.burnin=FALSE,
refine.user.start=TRUE)The number of draws to be taken from the posterior distribution.
The number of initial MCMC iterations to be discarded.
The number of iterations between consecutive draws.
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.
If greather than 1, uses an experimental parallelized label-switching algorithm. This is not guaranteed to work.
Maximum number of iterations for computing the starting values, posterior modes, MLEs, MKL estimates, etc..
Standard deviation of the proposal for the jump in the individual latent space position, or its starting value for the tuner.
Standard deviation of the proposal for the jump in the individual random effects values, or its starting value for the tuner.
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.
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.
Initial value for the factor by which the coefficients gotten by a Choletsky decomposition of the pilot sample covariance matrix are multiplied.
Proportion of draws from the pilot run to discard for estimating acceptance rate and group proposal covariance.
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.
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.
Factor by which to multiply the relevant proposal standard deviation if its acceptance rate fell below the backoff threshold.
Forces all proposals to be accepted unconditionally. Use only in debugging proposal distributions!
If TRUE, the samples from the burnin are
also stored and returned, to be used in MCMC diagnostics.
If TRUE, the values passed to
ergmm in the user.start argument can be updated by the mode-finding algorithm.
A list with the arguments as components.
# NOT RUN {
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))
# }
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