diff --git a/resources/robustness_check_plots.R b/resources/robustness_check_plots.R
index 83cd6ca..b7c0ac6 100644
--- a/resources/robustness_check_plots.R
+++ b/resources/robustness_check_plots.R
@@ -1,4 +1,4 @@
-x1library(data.table)
+library(data.table)
 library(ggplot2)
 source('resources/functions.R')
 
@@ -98,7 +98,6 @@ plot.robustness.2.iv <- function(iv, n.annotations=100, n.classifications=5000){
     new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
 
     robust_df <- robust_df[,method := new.levels[method]]
-    robust_df <- robust_df[method != "Feasible"]
     p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
     
     p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
@@ -121,13 +120,11 @@ plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){
     r <- readRDS("robustness_2_dv.RDS")
     robust_df <- data.table(r[['robustness_2_dv']])
 
-        
     robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
 
     new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
 
     robust_df <- robust_df[,method := new.levels[method]]
-    
     p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
     
     p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
@@ -150,7 +147,7 @@ plot.robustness.3.iv <- function(iv, n.annotations=200, n.classifications=5000){
 
     robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
     robust_df <- robust_df[,method := new.levels[method]]
-    robust_df <- robust_df[(method != "Feasible") & (Bzx==0.3)]
+    robust_df <- robust_df[(Bzx==0.3)]
     robust_df <- robust_df[(method != "PL")]
 
     p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM","MLA", "PL", "Feasible"))
@@ -173,7 +170,6 @@ plot.robustness.3.dv <- function(iv, n.annotations=100, n.classifications=1000){
     robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
 
     robust_df <- robust_df[,method := new.levels[method]]
-    robust_df <- robust_df[method != "Feasible"]
     robust_df <- robust_df[,Py := round(plogis(B0),2)]
     p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
     robust_df <- robust_df[(method != "PL")]
@@ -195,6 +191,7 @@ plot.robustness.4.iv <- function(iv, n.annotations=200, n.classifications=5000){
     robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
 
     robust_df <- robust_df[,method := new.levels[method]]
+    robust_df <- robust_df[method != "Feasible"]
 
     robust_df <- robust_df[,y_bias:=factor(robust_df$y_bias,levels=sort(unique(robust_df$y_bias),decreasing=TRUE))]
     robust_df <- robust_df[Bzx==1]