gof.vblpcm: Goodness of fit based on simulations from the fitted object.

In VBLPCM: Variational Bayes Latent Position Cluster Model for Networks

Goodness of fit based on simulations from the fitted object.

Description

Create a goodness of fit statistics and plots based on the degree distributions of networks simulated fitted from a fitted variational approximation.

Usage

## S3 method for class 'vblpcm'
gof(object, ...,
         nsim=100,
         GOF=NULL,
         verbose=FALSE)

Arguments

object	fitted VBLPCM object; usually output from vblpcmfit() or vblpcmstart()
...	optional arguments for lower level functions
nsim	number of networks to simulate
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.
verbose	Provide verbose information on the progress of the simulation.

Details

A sample of graphs is randomly drawn from the posterior of the vblpcmfit() result.

A plot of the summary measures may then be plotted using plot().

Author(s)

Michael Salter-Townshend

See Also

latentnet::gof.ergmm

Examples

data(sampson,package="VBLPCM")
v.start<-vblpcmstart(samplike,G=3,model="rreceiver",LSTEPS=1e3)
v.fit<-vblpcmfit(v.start,STEPS=20)
### plot the mean posterior positions
plot(v.fit, R2=0.05,main="Sampson's Monks: VB with Receiver Effects")
### Look at gof plots
plot(gof(v.fit,GOF=~distance,nsim=50))

VBLPCM documentation built on March 31, 2023, 9:21 p.m.