# BIC.dnc: Compute BIC as in Handcock et al. 2007 In dnc: Dynamic Network Clustering

### Description

The following uses a BIC estimate of π(Y,\hat{X}|M) to perform model selection. Note that this usage is not the typical BIC encountered in simpler contexts.

### Usage

 1 2 ## S3 method for class 'dnc' BIC(object, ...) 

### Arguments

 object A dnc object, a result of running dnc(...) ... optional additional arguments. None are used.

### Details

Rather than estimating the integrated likelihood π(Y|G), this instead incorporates the MAP estimates of the latent positions and corresponds to π(Y,\hat{X}|M). The BIC value returned is the following sum:

-2 log(π(Y|\hat{X},\hat{θ_1})) + dim(θ_1)log(∑ y_{ijt}) -2 log(π(\hat{X}|\hat{θ_2})) + dim(θ_2) \log(nT)

. See Sewell and Chen (2016) for more details.

### Value

A scalar. Lower values are better.

### References

Handcock, M. S., A.E. Raftery, and J. M. Tantrum (2007). Model-based clustering for social networks. J.R. Statist. Soc. A, 170, p. 301-354.

### Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ## Not run: dncObjs = list() BICvals = numeric(9) for(i in 2:10){ print(i) dncObjs[[i]] <- dnc(friendship,M=i,p=3,method="VB", controls=list(nDraws=500,burnin=100, MaxItStg2=50,epsilonStg2=1e-15)) BICvals[i-1] <- BIC(dncObjs[[i]]) } plot(BICvals~c(2:10),type="b",pch=16, xlab="Number of communities",ylab="BIC value") ( MBest = which.min(BICvals)+1 ) abline(v=MBest,lty=2,col="blue") ## End(Not run) 

dnc documentation built on May 29, 2017, 10:56 a.m.

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