multipartiteBMFixedModel function

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

Returns

Estimated parameters and a classification

Examples

namesFG <- c('A','B')
list_pi <- list(c(0.5,0.5),c(0.3,0.7)) # prop of blocks in each FG
E  <-  rbind(c(1,2),c(2,2)) # architecture of the multipartite net.
typeInter <- c( "inc","diradj")
v_distrib <- c('poisson','bernoulli')
list_theta <- list()
list_theta[[1]]   <- matrix(c(6.1, 8.9, 6.6, 3), 2, 2)
list_theta[[2]] <- matrix(c(0.7,1.0, 0.4, 0.6),2, 2)
list_Net <- rMBM(v_NQ = c(20,20),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(1,2),
#                                               nbCores = 2)