library(statnet) library(latentnet) data(sampson) plot (samplike,displaylabels=TRUE) #Fit a model with expansiveness only p1.0<-pstar(samplike,effects="outdegree") summary(p1.0) #Fit a model with expansiveness and popularity p1.1<-pstar(samplike,effects=c("outdegree","indegree")) #Fit a model with expansiveness, popularity, and mutuality (fixed for all pairs) p1.2<-pstar(samplike,effects=c("outdegree","indegree","mutuality")) summary(p1.2) #Compare the model AICs -- use ONLY as heuristics!!! extractAIC(p1.0) extractAIC(p1.1) extractAIC(p1.2) pstar.1 = pstar(samplike,effects=c("stransitivity")) summary(pstar.1) pstar.2 = pstar(samplike,effects=c("stransitivity","reciprocity")) summary(pstar.2) extractAIC(pstar.1) extractAIC(pstar.2) # another view on null model erg = ergm(samplike~edges) summary(erg) erg2 = ergmm(samplike~euclidean(2)) plot (erg2,labels=T) summary(erg2) erg3 = ergmm(samplike~euclidean(3)) plot (erg3,labels=TRUE) summary(erg3) erg2.3 = ergmm(samplike~euclidean(d=2,G=3)) plot (erg2.3,labels=T) summary(erg2.3) monks = unlist(sapply (samplike$val,"[","group")) names(monks) = sapply (samplike$val,"[","vertex.names") names(monks)[monks =="Outcasts"] ## try ecoli? data(ecoli) plot (ecoli1, displaylabels=T) pstar.1 = pstar(ecoli1,effects=c("stransitivity")) summary(pstar.1) pstar.2 = pstar(ecoli1,effects=c("stransitivity","reciprocity")) summary(pstar.2) extractAIC(pstar.1) extractAIC(pstar.2) erg2.ec = ergmm(ecoli1~euclidean(2)) plot (erg2.ec,labels=T) summary(erg2.ec)