+ # Get summary "tableauRecap" from models
+ tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+ models <- models_list[[i]]
+ #For a collection of models (same k, several lambda):
+ LLH <- sapply( models, function(model) model$llh[1] )
+ k = length(models[[1]]$pi)
+ sumPen = sapply(models, function(model)
+ k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
+ data.frame(model=paste(i,".",seq_along(models),sep=""),
+ pen=sumPen/n, complexity=sumPen, contrast=-LLH)
+ } ) )
+
+ print(tableauRecap)
+ tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
+
+ return(tableauRecap)
+
+ # modSel = capushe::capushe(tableauRecap, n)
+ # indModSel <-
+ # if (selecMod == 'DDSE')
+ # as.numeric(modSel@DDSE@model)
+ # else if (selecMod == 'Djump')
+ # as.numeric(modSel@Djump@model)
+ # else if (selecMod == 'BIC')
+ # modSel@BIC_capushe$model
+ # else if (selecMod == 'AIC')
+ # modSel@AIC_capushe$model
+ #
+ # mod = as.character(tableauRecap[indModSel,1])
+ # listMod = as.integer(unlist(strsplit(mod, "[.]")))
+ # modelSel = models_list[[listMod[1]]][[listMod[2]]]
+ #
+ # ##Affectations
+ # Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
+ # for (i in 1:n){
+ # for (r in 1:length(modelSel$pi)){
+ # sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
+ # Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
+ # }
+ # }
+ # Gam = Gam/rowSums(Gam)
+ # modelSel$affec = apply(Gam, 1,which.max)
+ # modelSel$proba = Gam
+ #
+ # if (plot){
+ # print(plot_valse(X,Y,modelSel,n))
+ # }
+ #
+ # return(modelSel)