Predict NAs in a Collection of Networks from a fitted MBM
predictMBM(RESMBM, whichModel = 1)
| RESMBM | a fitted multipartite blockmodel |
|---|---|
| whichModel | The index corresponding to the model used for prediction (default is 1, the best model) |
the collection of matrices of predictions (probability for binary, intensity for weighted network) a
namesFG <- c('A','B') list_pi = list(c(0.16 ,0.40 ,0.44),c(0.3,0.7)) E <- rbind(c(1,2),c(2,2),c(1,1)) typeInter <- c( "inc","diradj", "adj") v_distrib <- c('ZIgaussian','bernoulli','poisson') list_theta <- list() list_theta[[1]] <- list() list_theta[[1]]$mean <- matrix(c(6.1, 8.9, 6.6, 9.8, 2.6, 1.0), 3, 2) list_theta[[1]]$var <- matrix(c(1.6, 1.6, 1.8, 1.7 ,2.3, 1.5),3, 2) list_theta[[1]]$p0 <-matrix(c(0.4, 0.1, 0.8 , 0.5 , 0.7, 0),3, 2) list_theta[[2]] <- matrix(c(0.7,1.0, 0.4, 0.6),2, 2) m3 <- matrix(c(2.5, 2.6 ,2.2 ,2.2, 2.7 ,3.0 ,3.6, 3.5, 3.3),3,3 ) list_theta[[3]] <- (m3 + t(m3))/2 list_Net <- rMBM(v_NQ = c(60,50),E , typeInter, v_distrib, list_pi, list_theta, namesFG = namesFG, seed = 2)$list_Net res_MBMsimu <- multipartiteBM(list_Net, v_distrib, namesFG = c('A','B'), v_Kinit = c(2,2), nbCores = 2,initBM = FALSE)#> [1] "------------Nb of entities in each functional group--------------" #> A B #> 60 50 #> [1] "------------Probability distributions on each network--------------" #> [1] "ZIgaussian" "bernoulli" "poisson" #> [1] "-------------------------------------------------------------------" #> [1] " ------ Searching the numbers of blocks starting from [ 2 2 ] blocks" #> [1] "ICL : -6061.81 . Nb of blocks: [ 2 2 ]" #> [1] "ICL : -4894.06 . Nb of blocks: [ 3 2 ]" #> [1] "Best model------ ICL : -4894.06 . Nb of clusters: [ 3 2 ] for [ A , B ] respectively"pred <- predictMBM(res_MBMsimu)