new simulation results

YunyiShen · Apr 15, 2021 · f175190 · f175190
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diff --git a/tests/Formal/Accurancy/Figs/MCC_k10_Asmallprior_Omega2.pdf b/tests/Formal/Accurancy/Figs/MCC_k10_Asmallprior_Omega2.pdf
diff --git a/tests/Formal/Accurancy/Figs/MCC_k30_Asmallprior_Omega2.pdf b/tests/Formal/Accurancy/Figs/MCC_k30_Asmallprior_Omega2.pdf
diff --git a/tests/Formal/Accurancy/Figs/Stein_k30_Asmallprior2.pdf b/tests/Formal/Accurancy/Figs/Stein_k30_Asmallprior2.pdf
diff --git a/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso.csv b/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso.csv
diff --git a/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso_aug.csv b/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso_aug.csv
diff --git a/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso_aug1.csv b/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso_aug1.csv
diff --git a/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso_aug2.csv b/tests/Formal/Accurancy/k10/results/graph_Omega_Glasso_aug2.csv
diff --git a/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso3.csv b/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso3.csv
diff --git a/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso4.csv b/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso4.csv
diff --git a/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso_aug1.csv b/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso_aug1.csv
diff --git a/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso_aug2.csv b/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso_aug2.csv
diff --git a/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso_aug3.csv b/tests/Formal/Accurancy/k30/results/graph_Omega_Glasso_aug3.csv
diff --git a/tests/Formal/Accurancy/k30/results/loss_CAR3 - Copy.csv b/tests/Formal/Accurancy/k30/results/loss_CAR3 - Copy.csv
diff --git a/tests/Formal/Accurancy/plot_graph_learning.R b/tests/Formal/Accurancy/plot_graph_learning.R
@@ -16,7 +16,8 @@ all_Omega_k30 <- within(all_Omega_k30, p<-factor(p, levels=c("5 predictors", "10
 all_Omega_k30$mod <- paste0("model ",all_Omega_k30$mod)
 
 all_Omega_bayes <- all_Omega_k30[!is.na(all_Omega_k30$TP_bayes) & all_Omega_k30$mod!="model 6",c(1:11,20)]
-all_Omega_multireg <- all_Omega_k30[(all_Omega_k30$algo=="multireg"|all_Omega_k30$algo=="multireg_mu0"|all_Omega_k30$algo=="multireg_mu0-aug") & all_Omega_k30$mod!="model 6",c(1:7,16:19,20)]
+all_Omega_multireg <- all_Omega_k30[(all_Omega_k30$algo=="multireg"|all_Omega_k30$algo=="multireg_mu0"|all_Omega_k30$algo=="multireg_mu0-aug"|
+                                       all_Omega_k30$algo=="ad-hoc-aug"|all_Omega_k30$algo=="ad-hoc") & all_Omega_k30$mod!="model 6",c(1:7,16:19,20)]
 
 all_Omega_bayes$Sensitivity <- all_Omega_bayes$TP_bayes/(all_Omega_bayes$TP_bayes+all_Omega_bayes$FN_bayes)
 all_Omega_bayes$Specificity <- all_Omega_bayes$TN_bayes/(all_Omega_bayes$TN_bayes+all_Omega_bayes$FP_bayes)
@@ -50,7 +51,7 @@ Graph_MCC
 ggsave("./tests/Formal/Accurancy/Figs/MCC_k30_Asmallprior_Omega.pdf",Graph_MCC,width = 9,height = 5.5,unit = "in")
 
 ## New plot
-  Omega_learning_k30 <- within(Omega_learning_k30, algo<-factor(algo, levels=c("CAR-LASSO", "CAR-ALASSO", "SRG-LASSO","GLASSO-aug", "GALASSO-aug", "ad-hoc-aug" ,"GLASSO", "GALASSO","multireg_mu0-aug" ,"multireg", "multireg_mu0", "ad-hoc")))
+Omega_learning_k30 <- within(Omega_learning_k30, algo<-factor(algo, levels=c("CAR-LASSO", "CAR-ALASSO", "SRG-LASSO","GLASSO-aug", "GALASSO-aug", "ad-hoc-aug" ,"GLASSO", "GALASSO","multireg_mu0-aug" ,"multireg", "multireg_mu0", "ad-hoc")))
 Graph_MCC <- ggplot(data = Omega_learning_k30,aes(x=algo,y = MCC)) + 
   geom_point(aes(color = beta.sparsity), alpha=0.1, size=1)+
   geom_boxplot(aes(fill = beta.sparsity)) + 

diff --git a/tests/Formal/Accurancy/plot_res.R b/tests/Formal/Accurancy/plot_res.R
@@ -1,13 +1,13 @@
 library(ggplot2)
-all_res <- list.files("./tests/Formal/Accurancy/k10/results",full.names = T)
+all_res <- list.files("./tests/Formal/Accurancy/k30/results",full.names = T)
 ##all_res <- list.files("results",full.names = T)
 
 all_loss_files <- all_res[grep("loss",all_res)]
 all_loss <- lapply(all_loss_files,read.csv,row.names=1)
 all_loss <- Reduce(rbind,all_loss)
 
 all_loss_k30 <- all_loss[!is.na(all_loss$steinOmega),]
-all_loss_k30 <- all_loss_k30[all_loss_k30$k==10,]
+all_loss_k30 <- all_loss_k30[all_loss_k30$k==30,]
 
 all_loss_k30$beta.sparsity <- factor( 1-all_loss_k30$s,levels = c(0.8,0.5))
 #all_loss_k30$p <- paste0("#.predictors:",all_loss_k30$p)
@@ -42,7 +42,7 @@ Stein_k30 <- ggplot(data = all_loss_k30,aes(x=algo,y=log(steinOmega))) +
         plot.margin = margin(.15, .15, .15, .15, "cm"))
 
 Stein_k30
-ggsave("./tests/Formal/Accurancy/Figs/Stein_k10_Asmallprior2.pdf",Stein_k30,width = 12,height = 8,unit = "in")
+ggsave("./tests/Formal/Accurancy/Figs/Stein_k30_Asmallprior2.pdf",Stein_k30,width = 12,height = 8,unit = "in")
 ggsave("Figs/Stein_k10_Asmallprior2.jpg",Stein_k30,width = 10,height = 8,unit = "in")
 
 Stein_k30_aggregate_mean <- aggregate(steinOmega~p+mod+algo+beta.sparsity,data = all_loss_k30,FUN = mean)