library(SBICgraph)#>#> Attaching package: 'SBICgraph'#> The following object is masked from 'package:stats':#>#> simulatelibrary(network)# for visualization#> network: Classes for Relational Data#> Version 1.16.1 created on 2020-10-06.#> copyright (c) 2005, Carter T. Butts, University of California-Irvine#> Mark S. Handcock, University of California -- Los Angeles#> David R. Hunter, Penn State University#> Martina Morris, University of Washington#> Skye Bender-deMoll, University of Washington#> For citation information, type citation("network").#> Type help("network-package") to get started.# to reset parresetPar<-function() {dev.new() op<-par(no.readonly =TRUE)dev.off() op}The functioncomparison allows for comparison between the true network and the estimated network from the SBIC method.
First we create a simulated data set using the embeddedsimulate function within SBIC. The functionsimulate generates a data frame, a real network adjacency matrix and a prior network adjacency matrix.
p<-200m1<-100m2<-30d<-simulate(n=100,p=p,m1=m1,m2=m2)data<- d$datareal<- d$realnetworkpriori<- d$priornetworkWe can visualize the networks
prior_net<-network(priori)real_net<-network(real)par(mfrow =c(1,2))plot(prior_net,main ="Prior network")plot(real_net,main ="Real network")par(resetPar())We examine some features of both the prior network and the real network
sum(priori[lower.tri(priori)])#> [1] 100sum(priori[lower.tri(priori)])/(p*(p-1)/2)#> [1] 0.005025126sum(real[lower.tri(real)])#> [1] 100sum(real[lower.tri(real)])/(p*(p-1)/2)#> [1] 0.005025126Then we can fit SBIC using one function
lambda<-exp(seq(-10,10,length=30))# calculating the error rate from the number of edges in the true graph and the number of discordant pairsr1<- m2/m1r2<-m2/(p*(p-1)/2-m1)r<- (r1+r2)/2model<-sggm(data = data,lambda = lambda,M=priori,prob = r)Comparing the estimated network to the true and prior network. Our comparison function above calcualtes the Positive selection rate (PSR) and the False positive rate (FDR)
print("Comparing estimated model with the real network")#> [1] "Comparing estimated model with the real network"comparison(real = real,estimate = model$networkhat)#> $PSR#> [1] 0.4#>#> $FDR#> [1] 0.4666667print("Comparing the prior network with the real network")#> [1] "Comparing the prior network with the real network"comparison(real = real,estimate = priori)#> $PSR#> [1] 0.7#>#> $FDR#> [1] 0.3We can also compare visualizations
estimated_net<-network(model$networkhat)par(mfrow =c(1,3))plot(prior_net,main ="Prior Network")plot(real_net,main ="Real Network")plot(estimated_net,main ="Estimated Network")par(resetPar())The model object also stores all the candidate models generated.
length(model$candidate)#> [1] 64