predictMBM: Predict NAs in a Collection of Networks from a fitted MBM

In Demiperimetre/GREMLIN: Generalized Multipartite Networks

Predict NAs in a Collection of Networks from a fitted MBM

Description

Predict NAs in a Collection of Networks from a fitted MBM

Usage

predictMBM(RESMBM, whichModel = 1)

Arguments

RESMBM	a fitted multipartite blockmodel
whichModel	The index corresponding to the model used for prediction (default is 1, the best model)

Value

the collection of matrices of predictions (probability for binary, intensity for weighted network) a

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('gaussian','bernoulli')
list_theta <- list()
list_theta[[1]] <- list()
list_theta[[1]]$mean  <- matrix(c(6.1, 8.9, 6.6, 3), 2, 2)
list_theta[[1]]$var  <-  matrix(c(1.6, 1.6, 1.8, 1.5),2, 2)
list_theta[[2]] <- matrix(c(0.7,1.0, 0.4, 0.6),2, 2)
list_Net <- rMBM(v_NQ = c(30,30),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)
pred <- predictMBM(res_MBMsimu)

Demiperimetre/GREMLIN documentation built on March 14, 2023, 12:55 p.m.