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
)ergmm.control(
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
)
A list with the arguments as components.
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.
ergmm
# \donttest{
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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