ergmm.control {latentnet} | R Documentation |
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)
sample.size |
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 |
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 |
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 |
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 |
refine.user.start |
If |
A list with the arguments as components.
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))