mcmc.diagnostics.ergmm {latentnet} | R Documentation |
This function creates simple diagnostic plots for the MCMC sampled statistics produced from a fit. It also prints the Raftery-Lewis diagnostics, indicates if they are sufficient, and suggests the run length required.
## S3 method for class 'ergmm': mcmc.diagnostics(object, sample = "sample", smooth=TRUE, r = 0.0125, digits = 6, maxplot = 1000, verbose = TRUE, mcmc.title = "Summary of MCMC samples", ...) ergmm.raftery.diag(data, q = 0.025, rmargin = 0.005, s = 0.95, converge.eps = 0.001)
object |
An object. See documentation for ergmm . |
sample |
The component of object on which the
diagnosis is based. The two usual ones are thetasample from the
auxiliary sample of the natural parameter and sample the (default)
sample of the sufficient statistics from the model. |
smooth |
Draw a smooth line through trace plots |
r |
Percentile of the distribution to estimate |
digits |
Number of digits to print |
maxplot |
Maximum number of statistics to plot |
data |
an 'mcmc' object, typically the component of
object on which the diagnosis is based. |
q |
the quantile to be estimated. |
rmargin |
the desired margin of error of the estimate. |
s |
the probability of obtaining an estimate in the interval (q-r,q+r). |
converge.eps |
Precision required for estimate of time to convergence. |
verbose |
If this is TRUE , print out more information about the MCMC runs including lag correlations. |
mcmc.title |
Figure title for the diagnostic plots. |
... |
Additional arguments, to be passed to lower-level functions in the future. |
The plots produced are a trace of the sampled output and a density estimate for each variable in the chain.
The Raftery-Lewis diagnostic is a run length control diagnostic based on a criterion of accuracy of estimation of the quantile q. It is intended for use on a short pilot run of a Markov chain. The number of iterations required to estimate the quantile q to within an accuracy of +/- r with probability p is calculated. Separate calculations are performed for each variable within each chain.
In fact, object
contains the matrix of
statistics from the MCMC run as component sample
.
This matrix is actually an object of class mcmc
and
can be used directly in the CODA
package to assess MCMC
convergence. Hence MCMC diagnostic methods available
in coda
may be available directly. See the examples and
the coda
package.
This function depends on the coda
package.
More information can be found by looking at the documentation of
ergmm
.
mcmc.diagnostics.ergmm
returns a table of Raftery-Lewis diagnostics.
ergmm, network, coda
# data(sampson) # # test the mcmc.diagnostics function # gest <- ergmm(samplike ~ latent(k=2)) summary(gest) # # Plot the traces and densities # mcmc.diagnostics(gest)