- # Get summary "tableauRecap" from models
- tableauRecap = t( sapply( seq_along(model_list), function(model) {
- llh = matrix(ncol = 2)
- for (l in seq_along(model))
- llh = rbind(llh, model[[l]]$llh)
- LLH = llh[-1,1]
- D = llh[-1,2]
- c(LLH, D, rep(k, length(model)), 1:length(model))
- } ) )
+ modSel <- capushe::capushe(tableauRecap, n)
+ indModSel <- if (selecMod == "DDSE")
+ as.numeric(modSel@DDSE@model) else if (selecMod == "Djump")
+ as.numeric(modSel@Djump@model) else if (selecMod == "BIC")
+ modSel@BIC_capushe$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) * gdet(modelSel$rho[, , r])
+ }
+ }
+ Gam <- Gam/rowSums(Gam)
+ modelSel$affec <- apply(Gam, 1, which.max)
+ modelSel$proba <- Gam