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Using GREMLINS from sbm

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Louis 2026-02-16 15:58:01 +01:00
parent c13fe8bf3e
commit 29728d8205
1 changed files with 24 additions and 0 deletions
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24
multipartite_fungustree.R
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@ -5,8 +5,13 @@ library(sbm)
data(fungusTreeNetwork)
tree_genetic_dist_matrix <- fungusTreeNetwork$covar_tree$genetic_dist
tree_taxonomic_dist_matrix <- (fungusTreeNetwork$covar_tree$taxonomic_dist)
tree_genet_sbm <- estimateSimpleSBM(netMat = tree_genetic_dist_matrix, model = "gaussian", estimOptions = list(plot = FALSE))
plot(tree_genet_sbm)
tree_taxonomic_sbm <- estimateSimpleSBM(netMat = tree_taxonomic_dist_matrix, model = "gaussian", estimOptions = list(plot = FALSE))
plot(tree_taxonomic_sbm)
tree_geo_sbm <- estimateSimpleSBM(netMat = fungusTreeNetwork$covar_tree$geographic_dist, model = "gaussian", estimOptions = list(plot = FALSE))
plot(tree_geo_sbm)
@ -22,6 +27,14 @@ namesFG <- c("Tree", "Fungus")
givenclassif <- list(tree_geo_sbm$memberships, fungus_tree_sbm$memberships[["row"]])
geo_multi_noinit <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE)
Tree_Geo_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$geographic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
Tree_Taxo_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$taxonomic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
Tree_Genetic_dist_sbm <- defineSBM(fungusTreeNetwork$covar_tree$genetic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
FungusTree_sbm <- defineSBM(fungusTreeNetwork$fungus_tree, dimLabels = c("Fungus", "Tree"), model = "bernoulli", type = "bipartite")
geo_multi_noinit_sbm <- estimateMultipartiteSBM(list(Tree_Geo_dist_sbm, FungusTree_sbm), estimOptions = list(initBM = TRUE, ))
geo_multi_noinit_nobm <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE)
# Les kmins donnent le même résultat ICL -449.21
geo_multi_noinit_nobm_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE, v_Kmin = c(7, 2))
@ -32,4 +45,15 @@ geo_multi_init_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib
# Le meilleur ICL est ici : -408.05 avec une classif de base issue de blockmodels indépendants
geo_multi_init <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif)
library(knitr)
kable(data.frame("Given classif" = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), "InitBM" = c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE), "Kmin given" = c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE), ICL = c(
geo_multi_noinit$fittedModel[[1]]$ICL,
geo_multi_noinit_nobm$fittedModel[[1]]$ICL,
geo_multi_noinit_nobm_kmin$fittedModel[[1]]$ICL,
geo_multi_noinit_kmin$fittedModel[[1]]$ICL,
geo_multi_init_kmin$fittedModel[[1]]$ICL,
geo_multi_init$fittedModel[[1]]$ICL
)), format = "markdown")
dataR6 <- formattingData(list_Net, v_distrib)