Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
# fig.path = "man/figures/README-",
out.width = "100%"
)
## ----param FG-----------------------------------------------------------------
namesFG <- c('A','B')
v_NQ <- c(60,50) #size of each FG
list_pi = list(c(0.16 ,0.40 ,0.44),c(0.3,0.7)) #proportion of each block in each FG
list_pi[[1]]
## ----param 1 net--------------------------------------------------------------
E <- rbind(c(1,2),c(2,2),c(1,1))
typeInter <- c( "inc","diradj", "adj")
v_distrib <- c('ZIgaussian','bernoulli','poisson')
## ----param net----------------------------------------------------------------
list_theta <- list()
list_theta[[1]] <- list()
list_theta[[1]]$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, 0.6, 0.5 , 0.2, 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# for symetrisation
## ----simul 2, eval = TRUE, echo = TRUE----------------------------------------
library(GREMLINS)
dataSim <- rMBM(v_NQ,E , typeInter, v_distrib, list_pi,
list_theta, namesFG = namesFG, seed = 4,keepClassif = TRUE)
list_Net <- dataSim$list_Net
length(list_Net)
names(list_Net[[1]])
list_Net[[1]]$typeInter
list_Net[[1]]$rowFG
list_Net[[1]]$colFG
## ----MBM simul, echo = TRUE, eval = TRUE--------------------------------------
res_MBMsimu <- multipartiteBM(list_Net,
v_distrib = v_distrib,
namesFG = c('A','B'),
v_Kinit = c(2,2),
nbCores = 2,
initBM = FALSE,
keep = FALSE)
## ----estim param, eval=TRUE---------------------------------------------------
res_MBMsimu$fittedModel[[1]]$paramEstim$list_theta$AB$mean
## ----extract cluster, eval=TRUE-----------------------------------------------
Cl <- extractClustersMBM(res_MBMsimu)
## ----MBM fixed, echo = TRUE, eval = TRUE--------------------------------------
res_MBMsimu_fixed <- multipartiteBMFixedModel(list_Net, v_distrib = v_distrib, nbCores = 2,namesFG = namesFG, v_K = c(3,2))
res_MBMsimu_fixed$fittedModel[[1]]$paramEstim$v_K
extractClustersMBM(res_MBMsimu_fixed)$A
## ----sim NA-------------------------------------------------------------------
############# NA data at random in any matrix
epsilon = 10/100
list_Net_NA <- list_Net
for (m in 1:nrow(E)){
U <- sample(c(1,0),v_NQ[E[m,1]]*v_NQ[E[m,2]],replace=TRUE,prob = c(epsilon, 1-epsilon))
matNA <- matrix(U,v_NQ[E[m,1]],v_NQ[E[m,2]])
list_Net_NA[[m]]$mat[matNA== 1] = NA
if (list_Net_NA[[m]]$typeInter == 'adj') {
M <- list_Net_NA[[m]]$mat
diag(M) <- NA
M[lower.tri(M)] = t(M)[lower.tri(M)]
list_Net_NA[[m]]$mat <- M
}
}
## ----MBM simul NA, echo = TRUE, eval = TRUE-----------------------------------
res_MBMsimuNA <- multipartiteBM(list_Net_NA,
v_distrib = v_distrib,
namesFG = c('A','B'),
v_Kinit = c(2,2),
nbCores = 2,
keep = FALSE)
## ----MBM predict NA, echo = TRUE, eval = TRUE---------------------------------
pred <- predictMBM(res_MBMsimuNA)
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