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r comment # Build a 'graph-like' object having 'nodes' nodes belonging to 'classes' classes.
r comment # Class distribution is given by 'proba', and connectivity probabilities are given
r comment # by 'intraproba' and 'interproba'.
r code generateGraph<-function(nodes,classes,proba=rep(1/classes,classes),
r code intraproba=0.1,crossproba=0.02)
r code {
r code mat_pi=CreateConnectivityMat(classes,intraproba,crossproba)
r code igraph=Fast2SimuleERMG(nodes,proba,mat_pi[1],mat_pi[2])
r code adjacency=get.adjacency(igraph$graph)
r code cmgraph=list(nodes=nodes,classes=classes,adjacency=adjacency,nodeclasses=igraph$node.classes,proba=proba,
r code intraproba=intraproba,crossproba=crossproba)
r code attr(cmgraph,'class')<-c('cmgraph')
r code cmgraph
r code }
r blank
r comment # Return explicit member names for the different attributes of graph objects.
r code labels.cmgraph<-function(object,...)
r code {
r code c("Nodes","Classes","Adjacency Matrix","Node Classification","Class Probability Distribution","Intra Class Edge Probability","Cross Class Edge Probability")
r code }
r blank
r comment # Override the summmary function for graph objects.
r code summary.cmgraph<-function(object,...)
r code {
r blank
r code cat(c("Nodes : ",object$nodes,"\n",
r code "Edges : ",length(which(object$adjacency!=0)),"\n",
r code "Classes : ",object$classes,"\n",
r code "Class Probability Distribution: ",object$proba,"\n"))
r code }
r blank
r comment # Override the plot function for graph objects.
r code plot.cmgraph<-function(x,...)
r code {
r code RepresentationXGroup(x$adjacency,x$nodeclasses)
r code }
r blank
r comment # Generate covariable data for the graph 'g'. Covariables are associated to vertex data, and
r comment # their values are drawn according to 2 distributions: one for vertices joining nodes of
r comment # the same class, and another for vertices joining nodes of different classes.
r comment # The two distributions have different means but a single standard deviation.
r code generateCovariablesCondZ<-function(g,sameclustermean=0,otherclustermean=2,sigma=1)
r code {
r code mu=CreateMu(g$classes,sameclustermean,otherclustermean)
r code res=SimDataYcondZ(g$nodeclasses,mu,sigma)
r code cmcovars=list(graph=g,sameclustermean=sameclustermean,otherclustermean=otherclustermean,sigma=sigma,mu=mu,y=res)
r code attr(cmcovars,'class')<-c('cmcovarz','cmcovar')
r code cmcovars
r code }
r blank
r comment # Generate covariable data for the graph 'g'. Covariables are associated to vertex data, and
r comment # their values are drawn according to 2 distributions: one for vertices joining nodes of
r comment # the same class, and another for vertices joining nodes of different classes.
r comment # The two distributions have different means but a single standard deviation.
r comment # This function generates two sets of covariables.
r code generateCovariablesCondXZ<-function(g,sameclustermean=c(0,3),otherclustermean=c(2,5),sigma=1)
r code {
r code mux0=CreateMu(g$classes,sameclustermean[1],otherclustermean[1])
r code mux1=CreateMu(g$classes,sameclustermean[2],otherclustermean[2])
r code res=SimDataYcondXZ(g$nodeclasses,g$adjacency,mux0,mux1,sigma)
r code cmcovars=list(graph=g,sameclustermean=sameclustermean,otherclustermean=otherclustermean,sigma=sigma,mu=c(mux0,mux1),y=res)
r code attr(cmcovars,'class')<-c('cmcovarxz','cmcovar')
r code cmcovars
r code }
r blank
r blank
r comment # Override the print function for a cleaner covariable output.
r code print.cmcovar<-function(x,...)
r code {
r code cat("Classes : ",x$graph$classes,"\n",
r code "Intra cluster mean: ",x$sameclustermean,"\n",
r code "Cross cluster mean: ",x$otherclustermean,"\n",
r code "Variance : ",x$sigma,"\n",
r code "Covariables :\n",x$y,"\n")
r code }
r blank
r blank
r comment # Perform parameter estimation on 'graph' given the covariables 'covars'.
r code estimateCondZ<-function(graph,covars,maxiterations,initialclasses,selfloops)
r code {
r code res=EMalgorithm(initialclasses,covars$y,graph$adjacency,maxiterations,FALSE,selfloops)
r code cmestimation=list(mean=res$MuEstimated,variance=res$VarianceEstimated,pi=res$PIEstimated,alpha=res$AlphaEstimated,tau=res$TauEstimated,jexpected=res$EJ,graph=graph)
r code attr(cmestimation,'class')<-c('cmestimationz')
r code cmestimation
r code }
r blank
r comment # Private generic estimation function used to allow various call conventions for estimation functions.
r code privateestimate<-function(covars,graph,maxiterations,initialclasses,selfloops,...) UseMethod("privateestimate")
r blank
r comment # Private estimation function used to allow various call conventions for estimation functions.
r comment # Override of generic function for single covariables.
r code privateestimate.cmcovarz<-function(covars,graph,maxiterations,initialclasses,selfloops,...)
r code {
r code res=estimateCondZ(graph,covars,maxiterations,initialclasses,selfloops)
r code attr(res,'class')<-c(attr(res,'class'),'cmestimation')
r code res
r blank
r code }
r blank
r comment # Perform parameter estimation on 'graph' given the covariables 'covars'.
r code estimateCondXZ<-function(graph,covars,maxiterations,initialclasses,selfloops)
r code {
r comment #resSimXZ = EMalgorithmXZ(TauIni,Y2,Adjacente,30,SelfLoop=FALSE)
r code res=EMalgorithmXZ(initialclasses,covars$y,graph$adjacency,maxiterations,selfloops)
r code cmestimation=list(mean=c(res$MuEstimated1,res$MuEstimated2),variance=res$VarianceEstimated,pi=res$PIEstimated,alpha=res$AlphaEstimated,tau=res$TauEstimated,jexpected=res$EJ,graph=graph)
r code attr(cmestimation,'class')<-c('cmestimationxz')
r code cmestimation
r code }
r blank
r comment # Private estimation function used to allow various call conventions for estimation functions.
r comment # Override of generic function for multiple covariables.
r code privateestimate.cmcovarxz<-function(covars,graph,maxiterations,initialclasses,selfloops,...)
r code {
r code res=estimateCondXZ(graph,covars,maxiterations,initialclasses,selfloops)
r code attr(res,'class')<-c(attr(res,'class'),'cmestimation')
r code res
r code }
r blank
r comment # Generic estimation function applicable to graphs with covariables.
r code estimate<-function(graph,covars,...) UseMethod("estimate")
r blank
r comment # Override of the generic estimation function. Performs the actual function dispatch depending on the class of covariables.
r code estimate.cmgraph<-function(graph,covars,maxiterations=20,initialclasses=t(rmultinom(size=1,prob=graph$proba,n=graph$nodes)),selfloops=FALSE,method=NULL,...)
r code {
r code if (length(method) == 0) {
r code res=privateestimate(covars,graph,maxiterations,initialclasses,selfloops,...)
r code } else {
r code res=method(graph,covars,maxiterations,initialclasses,selfloops)
r code attr(res,'class')<-c(attr(res,'class'),'cmestimation')
r code }
r code res
r code }
r blank
r comment # Override of the generic pliot function for estimation results.
r code plot.cmestimation<-function(x,...)
r code {
r code par(mfrow = c(1,2))
r code plot(x$jexpected)
r code title("Expected value of J: Convergence criterion")
r blank
r code map=MAP(x$tau)
r code gplot(x$graph$adjacency,vertex.col=map$node.classes+2)
r code title("Network with estimated classes")
r blank
r code }
r blank
r comment # Generic private ICL computation function for graphs and covariables.
r code privatecomputeICL<-function(covars,graph,qmin,qmax,loops,maxiterations,selfloops) UseMethod("privatecomputeICL")
r blank
r blank
r comment # Private ICL computation function for graphs with single covariables.
r code privatecomputeICL.cmcovarz<-function(covars,graph,qmin,qmax,loops,maxiterations,selfloops)
r code {
r code res=ICL(X=graph$adjacency,Y=covars$y,Qmin=qmin,Qmax=qmax,loop=loops,NbIteration=maxiterations,SelfLoop=selfloops,Plot=FALSE)
r code attr(res,'class')<-c('cmiclz')
r code res
r blank
r code }
r blank
r comment # Private ICL computation function for graphs with multiple covariables.
r code privatecomputeICL.cmcovarxz<-function(covars,graph,qmin,qmax,loops,maxiterations,selfloops)
r code {
r code res=ICL(X=graph$adjacency,Y=covars$y,Qmin=qmin,Qmax=qmax,loop=loops,NbIteration=maxiterations,SelfLoop=selfloops,Plot=FALSE)
r code attr(res,'class')<-c('cmiclxz')
r code res
r code }
r blank
r blank
r comment # Generic public ICL computation function applicable to graph objects.
r code computeICL<-function(graph,covars,qmin,qmax,...) UseMethod("computeICL")
r blank
r comment # Override of ICL computation function applicable to graph objects.
r comment # Performs the actual method dispatch to private functions depending on the type of covariables.
r code computeICL.cmgraph<-function(graph,covars,qmin,qmax,loops=10,maxiterations=20,selfloops=FALSE,...)
r code {
r code res=privatecomputeICL(covars,graph,qmin,qmax,loops,maxiterations,selfloops)
r code res$qmin=qmin
r code res$qmax=qmax
r code res$graph=graph
r code res$covars=covars
r code attr(res,'class')<-c(attr(res,'class'),'cmicl')
r code res
r code }
r blank
r comment # Override of the plot function for results of ICL computation.
r code plot.cmicl<-function(x,...)
r code {
r code par(mfrow = c(1,2))
r code result=x$iclvalues
r code maxi=which(max(result)==result)
r code plot(seq(x$qmin,x$qmax),result,type="b",xlab="Number of classes",ylab="ICL value")
r code points(maxi+x$qmin-1,result[maxi],col="red")
r code title("ICL curve")
r code best=x$EMestimation[[maxi+x$qmin-1]]
r code tau=best$TauEstimated
r code map=MAP(tau)
r code gplot(x$graph$adjacency,vertex.col=map$node.classes+2)
r code title("Network with estimated classes")
r code }
r blank
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