checkdegeneracy {btergm} | R Documentation |
Check for degeneracy in fitted TERGMs
Description
Check for degeneracy in fitted TERGMs.
Usage
checkdegeneracy(object, ...)
## S4 method for signature 'mtergm'
checkdegeneracy(object, ...)
## S4 method for signature 'btergm'
checkdegeneracy(
object,
nsim = 1000,
MCMC.interval = 1000,
MCMC.burnin = 10000,
verbose = FALSE
)
## S3 method for class 'degeneracy'
print(
x,
center = FALSE,
t = 1:length(x$sim),
terms = 1:length(x$target.stats[[1]]),
...
)
## S3 method for class 'degeneracy'
plot(
x,
center = TRUE,
t = 1:length(x$sim),
terms = 1:length(x$target.stats[[1]]),
vbar = TRUE,
main = NULL,
xlab = NULL,
target.col = "red",
target.lwd = 3,
...
)
Arguments
object |
A |
... |
Arbitrary further arguments for subroutines. |
nsim |
The number of networks to be simulated at each time step. This number should be sufficiently large for a meaningful comparison. If possible, much more than 1,000 simulations. |
MCMC.interval |
Internally, this package uses the simulation facilities
of the ergm package to create new networks against which to compare
the original network(s) for goodness-of-fit assessment. This argument sets
the MCMC interval to be passed over to the simulation command. The default
value is |
MCMC.burnin |
Internally, this package uses the simulation facilities of
the ergm package to create new networks against which to compare the
original network(s) for goodness-of-fit assessment. This argument sets the
MCMC burnin to be passed over to the simulation command. The default value
is |
verbose |
Print details? |
x |
A |
center |
If |
t |
Time indices to include, e.g., |
terms |
Indices of the model terms to include, e.g., |
vbar |
Show vertical bar for target statistic in histogram. |
main |
Main title of the plot. |
xlab |
Label on the x-axis. Defaults to the name of the statistic. |
target.col |
Color of the vertical bar for the target statistic. Defaults to red. |
target.lwd |
Line width of the vertical bar for the target statistic. Defaults to 3. |
Details
The methods for the generic degeneracy
function implement a degeneracy
check for btergm
and mtergm
objects. For btergm
, this
works by comparing the global statistics of simulated networks to those of
the observed networks at each observed time step. If the global statistics
differ significantly, this is indicated by small p-values. If there are many
significant results, this indicates degeneracy. For mtergm
, the
mcmc.diagnostics
function from the ergm package is used.
Value
A list with target statistics and simulations.
References
Hanneke, Steve, Wenjie Fu and Eric P. Xing (2010): Discrete Temporal Models of Social Networks. Electronic Journal of Statistics 4: 585–605. doi:10.1214/09-EJS548.
Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2018): Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 1-36. doi:10.18637/jss.v083.i06.