Model selection and estimation of multipartite blockmodels

Estimate the parameters and give the clustering for given numbers of blocks

multipartiteBMFixedModel(
  list_Net,
  v_distrib,
  namesFG,
  v_K,
  classifInit = NULL,
  nbCores = NULL,
  maxiterVE = NULL,
  maxiterVEM = NULL,
  verbose = TRUE
)

Arguments

list_Net	A list of network (defined via the function DefineNetwork)
v_distrib	Type of proababilistic distributions in each network : if 0/1 then bernoulli, if counting then poisson, gaussian or Zero Inflated Gaussian (ZIgaussian) My default = Bernoulli. Must give a vector whose length is the number of networks in list_Net
namesFG	Names of functional groups (must correspond to names in listNet)
v_K	A vector with the numbers of blocks per functional group
classifInit	A list of initial classification for each functional group in the same order as in namesFG
nbCores	Number or cores used for the estimation. Not parallelized on windows. By default : half of the cores
maxiterVE	Maximum number of iterations in the VE step of the VEM algorithm. Default value = 1000
maxiterVEM	Maximum number of iterations of the VEM algorithm. Default value = 1000
verbose	Set to TRUE to display the current step of the search algorithm

Value

Estimated parameters and a classification

Examples

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('gaussian','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[[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_fixed <- multipartiteBMFixedModel(list_Net,
                                              v_distrib,
                                              namesFG = namesFG,
                                              v_K = c(3,2),
                                              nbCores = 2)
#> [1] "====================== First Forward Step =================="
#> [1] "====================== First Backward Step =================="
#> [1] "====================== Last Forward Step =================="
#> [1] "====================== Last Backward Step =================="