estimateMultipartiteSBM: Estimation for multipartite SBM

Description Usage Arguments Details Value Examples

View source: R/estimate.R

Description

Estimation for multipartite SBM

Usage

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estimateMultipartiteSBM(listSBM, estimOptions = list())

Arguments

listSBM

list of networks that were defined by the defineSBM function

estimOptions

options for the inference procedure

Details

The list of parameters estimOptions essentially tunes the optimization process and the variational EM algorithm, with the following parameters

Value

a MultipartiteSBM_fit object with the estimated parameters and the blocks in each Functional Group

Examples

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## About the Parts/Functional Groups (FG)
blockProp <- list(c(0.16 ,0.40 ,0.44),c(0.3,0.7)) # prop of blocks in each FG
archiMultipartite <-  rbind(c(1,2),c(2,2),c(1,1)) # architecture of the multipartite net.
nbNodes <- c(60,50)
## About the connection matrices
directed <- c(NA, TRUE, FALSE) # type of each network
model <- c('gaussian','bernoulli','poisson')
C1 <-
 list(mean = matrix(c(6.1, 8.9, 6.6, 9.8, 2.6, 1.0), 3, 2),
      var  = matrix(c(1.6, 1.6, 1.8, 1.7 ,2.3, 1.5),3, 2))
C2 <- list(mean = 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 )
C3 <- list(mean = .5 * (m3 + t(m3)))
connectParam <- list(C1, C2, C3)
## Graph Sampling
mySampleMSBM <- sampleMultipartiteSBM(nbNodes, blockProp,
                                      archiMultipartite, connectParam, model,
                                      directed, dimLabels = c('A','B'), seed = 2)
listSBM <- mySampleMSBM$listSBM
estimOptions <- list(initBM = FALSE, nbCores  = 2)
myMSBM <- estimateMultipartiteSBM(listSBM,estimOptions)
plot(myMSBM, type = "data")
plot(myMSBM, type = "expected")
plot(myMSBM, type = "meso")

GrossSBM/sbm documentation built on April 8, 2021, 5:53 a.m.