gof {ergm} | R Documentation |
gof
calculates p-values for geodesic
distance, degree, and reachability summaries to
diagnose the goodness-of-fit of exponential family random graph
models. See ergm
for more information on these models.
## Default S3 method: gof(object,...) ## S3 method for class 'formula': gof(formula, ..., theta0=NULL, nsim=100, burnin=10000, interval=1000, GOF=~degree+espartners+distance, constraints=~., control=control.gof.formula(), seed=NULL, verbose=FALSE) ## S3 method for class 'ergm': gof(object, ..., nsim=100, GOF=~degree+espartners+distance, burnin=10000, interval=1000, constraints=NULL, control=control.gof.ergm(), seed=NULL, theta0=NULL, verbose=FALSE)
object |
an R object. Either a formula or an ergm object.
See documentation for ergm . |
formula |
formula; An R formula object, of the form
y ~ <model terms> ,
where y is a network object or a matrix that can be
coerced to a network object. This specifies the model to simulate from.
For the details on the possible
<model terms> , see ergm-terms . To create a
network object in R, use the network() function,
then add nodal attributes to it using the %v%
operator if necessary. |
theta0 |
When given either a formula or an object of class ergm, theta0 are the parameters from which the sample is drawn. By default set to a vector of 0. |
nsim |
The number of simulations to use for the MCMC p-values. This is the size of the sample of graphs to be randomly drawn from the distribution specified by the object on the set of all graphs. |
GOF |
formula; an R formula object, of the form
~ <model terms> specifying the
statistics to use to diagnosis the goodness-of-fit of the model.
They do not need to be in the model formula specified in
formula , and typically are not.
Examples are the degree distribution ("degree"),
minimum geodesic distances ("dist"), and shared partner distributions
("espartners" and "dspartners").
For the details on the possible
<model terms> , see ergm-terms . |
burnin |
Number of proposed edge toggles before any MCMC sampling is done. If the model is correct this can be 0. Currently, there is no support for any check of the Markov chain mixing, so burnin should be set to a fairly large number. |
interval |
Number of proposed edge toggles between sampled statistics. The program prints a warning if too few proposed toggles are being accepted (if the number of proposed toggles between sampled observations ever equals an integral multiple of 100*(1+the number of toggles accepted)). |
constraints |
A one-sided formula specifying one or more constraints
on the support of the distribution of the networks being
modeled. See the help for similarly-named argument in
ergm for more information. For
gof.formula , defaults to unconstrained. For gof.ergm ,
defaults to the constraints with which object was fitted. |
control |
A list to control parameters, constructed using
control.gof.formula or control.gof.ergm
(which have different defaults). |
seed |
integer; random number integer seed. Defaults to NULL to
use whatever the state of the random number generater is at the time
of the call. |
verbose |
Provide verbose information on the progress of the simulation. |
... |
Additional arguments, to be passed to lower-level functions in the future. |
A sample of graphs is randomly drawn from the specified model.
The first argument is typically
the output of a call to ergm
and the model
used for that call is the one fit.
A plot of the summary measures is plotted.
More information can be found by looking at the documentation of
ergm
.
gof
, gof.ergm
, and gof.formula
return an object of class gofobject
.
This is a list of the tables of statistics and p-values.
This is typically plotted using plot.gofobject
.
ergm, network, simulate.ergm, summary.ergm, plot.gofobject
data(florentine) gest <- ergm(flomarriage ~ edges + kstar(2)) gest summary(gest) # test the gof.ergm function gofflo <- gof(gest) gofflo # Plot all three on the same page # with nice margins par(mfrow=c(1,3)) par(oma=c(0.5,2,1,0.5)) plot(gofflo) # And now the log-odds plot(gofflo, plotlogodds=TRUE) # Use the formula version of gof gofflo2 <-gof(flomarriage ~ edges + kstar(2), theta0=c(-1.6339, 0.0049)) plot(gofflo2)