diff --git a/ccn2019.rev3.Rmd b/ccn2019.rev3.Rmd
index 306ff8c..d286a1c 100644
--- a/ccn2019.rev3.Rmd
+++ b/ccn2019.rev3.Rmd
@@ -12,6 +12,7 @@
 library(broom)
 library(rsample)
 library(inspectdf)
+library(caTools)
 ```
 
 
@@ -30,12 +31,6 @@
   })
 }
 
-
-invs <- function(s) {
-  print(length(s)!=8)
-  1
-}
-
 seqs <- NB %>%
   group_by(participant, block, condition) %>%
   mutate(n = ifelse(condition=='2-back',2,3)) %>%
@@ -84,8 +79,9 @@
     number = 5
   )
 
+#==================================================#
 # Train PLS model (accuracy)
-model_pls_rt <- train(
+model_pls_accuracy <- train(
   a ~ .-rt-al-correct,
   data = train_data,
   method = "pls",
@@ -94,8 +90,13 @@
   preProc = c("zv","center","scale"))
  
 # Check CV profile
-plot(model_pls_rt)
+plot(model_pls_accuracy)
 
+# PLS variable importance
+varImp(model_pls_accuracy)
+
+
+#==================================================#
 # Train PLS model (rt)
 train_data_x <- data %>% select(-rt,-a,-correct)
 train_data_y <- (data %>% select(rt))$rt
@@ -111,7 +112,17 @@
 # Check CV profile
 plot(model_pls_rt)
 
+# PLS variable importance
+varImp(model_pls_rt)
 
+
+predicted_rt_data <- predict(model_pls_rt, test_data)
+
+confusionMatrix(predicted_rt_data,test_data$rt)
+
+colAUC(predicted_rt_data,test_data$rt, plotROC=T)
+
+#==================================================#
 # training control params for "correct" column
 trControl <- trainControl(
     method = "cv",
@@ -134,10 +145,9 @@
 
 confusionMatrix(test_data$correct, predicted_correct_data)
 
-library(caTools)
 colAUC(predicted_correct_data, test_data$correct, plotROC=T)
 
-
+#==================================================#
 ## OLD MODEL (only global features)
 model_glm_correct_old <- train(
   correct ~ n+t+v,
@@ -153,6 +163,5 @@
 
 confusionMatrix(test_data$correct, predicted_old_correct_data)
 
-library(caTools)
 colAUC(predicted_old_correct_data,test_data$correct, plotROC=T)
 ```