diff --git a/ccn2019.rev3.Rmd b/ccn2019.rev3.Rmd
index 01ebefb..468f63b 100644
--- a/ccn2019.rev3.Rmd
+++ b/ccn2019.rev3.Rmd
@@ -14,17 +14,18 @@
 library(inspectdf)
 library(caTools)
 library(pROC)
+
+#! ===============================================
+#! load data set and set running window size
+load(here('data/CL2015.RData'))
+window_size <- 8
+
 ```
 
 
 ```{r preprocessing}
 
 #! ===============================================
-#! load data set and set running window size
-load(here('notebooks/data/CL2015.RData'))
-window_size <- 8
-
-#! ===============================================
 #! A function to mark lures in a sequence
 with_lures <- function(stimulus, stimulus_type, n) {
   sapply(1:length(stimulus), function(i) {
@@ -61,7 +62,14 @@
   mutate(l  = map_dbl(local_stats, ~length(which(.x$stimulus_type=='lure')))) %>%
   mutate(s  = map_dbl(local_stats, ~sum(sort(table(.x$stimulus), decreasing = T)[1:2]) - 1)) %>%
   mutate(v  = map_dbl(local_stats, ~length(unique(.x$stimulus)))) %>%
+  mutate(dp = map_dbl(local_stats,
+                      ~length(which(.x$stimulus_type=="target" & .x$correct)) -
+                        length(which(.x$stimulus_type!="target" & !.x$correct)))) %>%
+  mutate(cr = map_dbl(local_stats,
+                      ~-(length(which(.x$stimulus_type=="target" & .x$correct==T)) +
+                        length(which(.x$stimulus_type!="target" & .x$correct==F)))/2)) %>%
   mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-stimulus_type,-choice))) %>%
+  #mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-choice))) %>%
   ungroup() %>%
   select(-participant,-block,-condition) %>%
   unnest(local_stats)
@@ -69,6 +77,7 @@
 #! ===============================================
 #! visualize correlations
 #DEBUG inspect_cor(seqs, show_plot = T)
+save(seqs,file=here("data/nback_seqs.Rd"))
 ```
 
 ```{r remove_highly_correlated_predictors}
@@ -85,8 +94,8 @@
 #! prepare data for modeling (remove na, etc)
 seqs <- seqs %>% 
   filter(!is.na(correct), !is.na(rt)) %>%
-  #mutate(correct=factor(as.numeric(correct),labels=c("I","C")))
-  mutate(correct=as.numeric(correct))
+  mutate(correct=factor(as.numeric(correct),labels=c("I","C")))
+  #mutate(correct=as.numeric(correct))
 
 #FIXME remove outcomes before dummy out the data and imputing missing values
 
@@ -112,14 +121,15 @@
 #! training parameters for the PLS models
 plsTrControl <- trainControl(
     method = "cv",
-    number = 5
+    number = 5,
+    verboseIter = TRUE
   )
 
 #==================================================#
 # Train PLS model (block-level accuracy)
 pls.fit.accuracy <- train(
-  a ~ .-rt-al-correct,
-  data = train_data,
+  a ~ .-rt-al-correct-dp-cr,
+  data = seqs.train,
   method = "pls",
   tuneLength = 20,
   trControl = plsTrControl,
@@ -131,11 +141,16 @@
 # PLS variable importance
 plot(varImp(pls.fit.accuracy), main="Accuracy - Variable Importance")
 
+pls.fit.accuracy.predicted <- predict(pls.fit.accuracy, seqs.test, type="raw")
+
+ggplot(seqs.test, aes(pls.fit.accuracy.predicted, a)) +
+  geom_point()
+
 
 #==================================================#
 # Train PLS model (rt)
-train_data_x <- train_data %>% select(-rt,-a,-correct) # all except rt
-train_data_y <- (train_data %>% select(rt))$rt # only rt
+train_data_x <- seqs.train %>% select(-rt,-a,-correct) # all except rt
+train_data_y <- (seqs.train %>% select(rt))$rt # only rt
 
 pls.fit.rt <- train(
   train_data_x,
@@ -151,52 +166,33 @@
 # PLS variable importance
 plot(varImp(pls.fit.rt), main="RT - Variable Importance")
 
-pls.predicted.rt <- predict(pls.fit.rt, test_data)
+pls.predicted.rt <- predict(pls.fit.rt, seqs.test)
 
-#FIXME
-confusionMatrix(pls.predicted.rt,test_data$rt)
-colAUC(pls.predicted.rt,test_data$rt, plotROC=T)
-
-#==================================================#
-# training control params for "correct" column
-glmTrControl <- trainControl(
-    method = "cv",
-    number = 5,
-    classProbs = T,
-    summaryFunction = twoClassSummary
-  )
-  
-glm.fit.correct <- train(
-  correct ~ .-rt-a-al,
-  data = train_data,
-  method = "glm",
-  family = "binomial",
-  trControl = glmTrControl
-)
-
-glm.fit.correct
-varImp(glm.fit.correct)
-
-
-glm.predicted.correct <- predict(glm.fit.correct, test_data, type="prob")
-
-#FIXME
-confusionMatrix(glm.predicted.correct, test_data$correct)
-colAUC(glm.predicted.correct, test_data$correct, plotROC=T)
+#FIXME measure performance with accuracy, etc.
+#confusionMatrix(pls.predicted.rt, seqs.test$rt)
+#colAUC(pls.predicted.rt, seqs.test$rt, plotROC=T)
 
 #==================================================#
 ## OLD MODEL (only global features)
-glm.fit.correct.old <- train(
-  correct ~ n+t+v,
-  data = train_data,
-  method = "glm",
-  family = "binomial",
-  trControl = trControl
-)
 
-glm.fit.correct.old
+pls.fit.accuracy.old <- train(
+  a ~ n+t+v,
+  data = seqs.train,
+  method = "pls",
+  tuneLength = 20,
+  trControl = plsTrControl,
+  preProc = c("center","scale"))
+ 
+# Check CV profile
+plot(pls.fit.accuracy.old)
 
-glm.fit.predicted.old <- predict(glm.fit.correct.old, test_data, type="prob")
+# PLS variable importance
+plot(varImp(pls.fit.accuracy.old), main="Accuracy (old) - Variable Importance")
+
+pls.fit.accuracy.old.predicted <- predict(pls.fit.accuracy.old, seqs.test, type="raw")
+
+ggplot(seqs.test, aes(pls.fit.accuracy.old.predicted, a)) +
+  geom_point()
 
 #FIXME
 confusionMatrix(glm.fit.predicted.old, test_data$correct, )

diff --git a/ccn2019.rev3.Rmd b/ccn2019.rev3.Rmd
index 01ebefb..468f63b 100644
--- a/ccn2019.rev3.Rmd
+++ b/ccn2019.rev3.Rmd
@@ -14,17 +14,18 @@
 library(inspectdf)
 library(caTools)
 library(pROC)
+
+#! ===============================================
+#! load data set and set running window size
+load(here('data/CL2015.RData'))
+window_size <- 8
+
 ```
 
 
 ```{r preprocessing}
 
 #! ===============================================
-#! load data set and set running window size
-load(here('notebooks/data/CL2015.RData'))
-window_size <- 8
-
-#! ===============================================
 #! A function to mark lures in a sequence
 with_lures <- function(stimulus, stimulus_type, n) {
   sapply(1:length(stimulus), function(i) {
@@ -61,7 +62,14 @@
   mutate(l  = map_dbl(local_stats, ~length(which(.x$stimulus_type=='lure')))) %>%
   mutate(s  = map_dbl(local_stats, ~sum(sort(table(.x$stimulus), decreasing = T)[1:2]) - 1)) %>%
   mutate(v  = map_dbl(local_stats, ~length(unique(.x$stimulus)))) %>%
+  mutate(dp = map_dbl(local_stats,
+                      ~length(which(.x$stimulus_type=="target" & .x$correct)) -
+                        length(which(.x$stimulus_type!="target" & !.x$correct)))) %>%
+  mutate(cr = map_dbl(local_stats,
+                      ~-(length(which(.x$stimulus_type=="target" & .x$correct==T)) +
+                        length(which(.x$stimulus_type!="target" & .x$correct==F)))/2)) %>%
   mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-stimulus_type,-choice))) %>%
+  #mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-choice))) %>%
   ungroup() %>%
   select(-participant,-block,-condition) %>%
   unnest(local_stats)
@@ -69,6 +77,7 @@
 #! ===============================================
 #! visualize correlations
 #DEBUG inspect_cor(seqs, show_plot = T)
+save(seqs,file=here("data/nback_seqs.Rd"))
 ```
 
 ```{r remove_highly_correlated_predictors}
@@ -85,8 +94,8 @@
 #! prepare data for modeling (remove na, etc)
 seqs <- seqs %>% 
   filter(!is.na(correct), !is.na(rt)) %>%
-  #mutate(correct=factor(as.numeric(correct),labels=c("I","C")))
-  mutate(correct=as.numeric(correct))
+  mutate(correct=factor(as.numeric(correct),labels=c("I","C")))
+  #mutate(correct=as.numeric(correct))
 
 #FIXME remove outcomes before dummy out the data and imputing missing values
 
@@ -112,14 +121,15 @@
 #! training parameters for the PLS models
 plsTrControl <- trainControl(
     method = "cv",
-    number = 5
+    number = 5,
+    verboseIter = TRUE
   )
 
 #==================================================#
 # Train PLS model (block-level accuracy)
 pls.fit.accuracy <- train(
-  a ~ .-rt-al-correct,
-  data = train_data,
+  a ~ .-rt-al-correct-dp-cr,
+  data = seqs.train,
   method = "pls",
   tuneLength = 20,
   trControl = plsTrControl,
@@ -131,11 +141,16 @@
 # PLS variable importance
 plot(varImp(pls.fit.accuracy), main="Accuracy - Variable Importance")
 
+pls.fit.accuracy.predicted <- predict(pls.fit.accuracy, seqs.test, type="raw")
+
+ggplot(seqs.test, aes(pls.fit.accuracy.predicted, a)) +
+  geom_point()
+
 
 #==================================================#
 # Train PLS model (rt)
-train_data_x <- train_data %>% select(-rt,-a,-correct) # all except rt
-train_data_y <- (train_data %>% select(rt))$rt # only rt
+train_data_x <- seqs.train %>% select(-rt,-a,-correct) # all except rt
+train_data_y <- (seqs.train %>% select(rt))$rt # only rt
 
 pls.fit.rt <- train(
   train_data_x,
@@ -151,52 +166,33 @@
 # PLS variable importance
 plot(varImp(pls.fit.rt), main="RT - Variable Importance")
 
-pls.predicted.rt <- predict(pls.fit.rt, test_data)
+pls.predicted.rt <- predict(pls.fit.rt, seqs.test)
 
-#FIXME
-confusionMatrix(pls.predicted.rt,test_data$rt)
-colAUC(pls.predicted.rt,test_data$rt, plotROC=T)
-
-#==================================================#
-# training control params for "correct" column
-glmTrControl <- trainControl(
-    method = "cv",
-    number = 5,
-    classProbs = T,
-    summaryFunction = twoClassSummary
-  )
-  
-glm.fit.correct <- train(
-  correct ~ .-rt-a-al,
-  data = train_data,
-  method = "glm",
-  family = "binomial",
-  trControl = glmTrControl
-)
-
-glm.fit.correct
-varImp(glm.fit.correct)
-
-
-glm.predicted.correct <- predict(glm.fit.correct, test_data, type="prob")
-
-#FIXME
-confusionMatrix(glm.predicted.correct, test_data$correct)
-colAUC(glm.predicted.correct, test_data$correct, plotROC=T)
+#FIXME measure performance with accuracy, etc.
+#confusionMatrix(pls.predicted.rt, seqs.test$rt)
+#colAUC(pls.predicted.rt, seqs.test$rt, plotROC=T)
 
 #==================================================#
 ## OLD MODEL (only global features)
-glm.fit.correct.old <- train(
-  correct ~ n+t+v,
-  data = train_data,
-  method = "glm",
-  family = "binomial",
-  trControl = trControl
-)
 
-glm.fit.correct.old
+pls.fit.accuracy.old <- train(
+  a ~ n+t+v,
+  data = seqs.train,
+  method = "pls",
+  tuneLength = 20,
+  trControl = plsTrControl,
+  preProc = c("center","scale"))
+ 
+# Check CV profile
+plot(pls.fit.accuracy.old)
 
-glm.fit.predicted.old <- predict(glm.fit.correct.old, test_data, type="prob")
+# PLS variable importance
+plot(varImp(pls.fit.accuracy.old), main="Accuracy (old) - Variable Importance")
+
+pls.fit.accuracy.old.predicted <- predict(pls.fit.accuracy.old, seqs.test, type="raw")
+
+ggplot(seqs.test, aes(pls.fit.accuracy.old.predicted, a)) +
+  geom_point()
 
 #FIXME
 confusionMatrix(glm.fit.predicted.old, test_data$correct, )
diff --git a/cnn2019-correct.R b/cnn2019-correct.R
new file mode 100644
index 0000000..a4e0b4c
--- /dev/null
+++ b/cnn2019-correct.R
@@ -0,0 +1,83 @@
+#==================================================#
+# model the "correct" column
+
+library(here)
+library(tidyverse)
+library(caret)
+library(inspectdf)
+
+
+load(here("data/nback_seqs.Rd"))
+
+set.seed(42)
+
+seqs.imputed <- seqs %>% 
+  filter(!is.na(correct), !is.na(rt)) %>%
+  mutate(correct=factor(correct,labels=c("INCORRECT","CORRECT")))
+
+inspect_cat(seqs.imputed)
+inspect_num(seqs.imputed)
+
+seqs.dummy <- predict(dummyVars(~.,data=seqs.imputed),seqs.imputed)
+
+
+train_indexes <- createDataPartition(seqs.imputed$correct,
+                                     times = 1,
+                                     p = 0.7,
+                                     list = F)
+
+train_data <- seqs.imputed[train_indexes,]
+test_data <- seqs.imputed[-train_indexes,]
+
+control <- trainControl(
+  method = "cv",
+  number = 5,
+  classProbs = T,
+  verboseIter = T,
+  summaryFunction = twoClassSummary
+)
+
+pls.model1 <- train(
+  correct ~ t + l + s + v + n + tl + ll + sl + ul + vl,
+  data = train_data,
+  method = "pls",
+  trControl = control
+)
+
+pls.model2 <- train(
+  correct ~ t + n + v,
+  data = train_data,
+  method = "pls",
+  trControl = control
+)
+
+pls.model1
+pls.model2
+varImp(pls.model1)
+varImp(pls.model2)
+
+trellis.par.set(caretTheme())
+densityplot(pls.model1, pch = "|")
+densityplot(pls.model2, pch = "|")
+
+resamps <- resamples(list(model1 = pls.model1, model2 = pls.model2))
+summary(resamps)
+dotplot(resamps, metric = "ROC")
+difValues <- diff(resamps)
+bwplot(difValues, layout=c(1,3))
+
+
+pls.train_predicted1 <- predict(pls.model1, train_data, type="prob")
+pls.train_predicted2 <- predict(pls.model2, train_data, type="raw")
+pls.predicted1 <- predict(pls.model1, test_data, type="raw")
+pls.predicted2 <- predict(pls.model2, test_data, type="raw")
+
+#FIXME
+confusionMatrix(pls.train_predicted1, train_data$correct)
+confusionMatrix(pls.train_predicted2, train_data$correct)
+confusionMatrix(pls.predicted1, test_data$correct)
+confusionMatrix(pls.predicted2, test_data$correct)
+
+colAUC(pls.predicted1, test_data$correct, plotROC=T)
+colAUC(pls.predicted2, test_data$correct, plotROC=T)
+

diff --git a/ccn2019.rev3.Rmd b/ccn2019.rev3.Rmd
index 01ebefb..468f63b 100644
--- a/ccn2019.rev3.Rmd
+++ b/ccn2019.rev3.Rmd
@@ -14,17 +14,18 @@
 library(inspectdf)
 library(caTools)
 library(pROC)
+
+#! ===============================================
+#! load data set and set running window size
+load(here('data/CL2015.RData'))
+window_size <- 8
+
 ```
 
 
 ```{r preprocessing}
 
 #! ===============================================
-#! load data set and set running window size
-load(here('notebooks/data/CL2015.RData'))
-window_size <- 8
-
-#! ===============================================
 #! A function to mark lures in a sequence
 with_lures <- function(stimulus, stimulus_type, n) {
   sapply(1:length(stimulus), function(i) {
@@ -61,7 +62,14 @@
   mutate(l  = map_dbl(local_stats, ~length(which(.x$stimulus_type=='lure')))) %>%
   mutate(s  = map_dbl(local_stats, ~sum(sort(table(.x$stimulus), decreasing = T)[1:2]) - 1)) %>%
   mutate(v  = map_dbl(local_stats, ~length(unique(.x$stimulus)))) %>%
+  mutate(dp = map_dbl(local_stats,
+                      ~length(which(.x$stimulus_type=="target" & .x$correct)) -
+                        length(which(.x$stimulus_type!="target" & !.x$correct)))) %>%
+  mutate(cr = map_dbl(local_stats,
+                      ~-(length(which(.x$stimulus_type=="target" & .x$correct==T)) +
+                        length(which(.x$stimulus_type!="target" & .x$correct==F)))/2)) %>%
   mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-stimulus_type,-choice))) %>%
+  #mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-choice))) %>%
   ungroup() %>%
   select(-participant,-block,-condition) %>%
   unnest(local_stats)
@@ -69,6 +77,7 @@
 #! ===============================================
 #! visualize correlations
 #DEBUG inspect_cor(seqs, show_plot = T)
+save(seqs,file=here("data/nback_seqs.Rd"))
 ```
 
 ```{r remove_highly_correlated_predictors}
@@ -85,8 +94,8 @@
 #! prepare data for modeling (remove na, etc)
 seqs <- seqs %>% 
   filter(!is.na(correct), !is.na(rt)) %>%
-  #mutate(correct=factor(as.numeric(correct),labels=c("I","C")))
-  mutate(correct=as.numeric(correct))
+  mutate(correct=factor(as.numeric(correct),labels=c("I","C")))
+  #mutate(correct=as.numeric(correct))
 
 #FIXME remove outcomes before dummy out the data and imputing missing values
 
@@ -112,14 +121,15 @@
 #! training parameters for the PLS models
 plsTrControl <- trainControl(
     method = "cv",
-    number = 5
+    number = 5,
+    verboseIter = TRUE
   )
 
 #==================================================#
 # Train PLS model (block-level accuracy)
 pls.fit.accuracy <- train(
-  a ~ .-rt-al-correct,
-  data = train_data,
+  a ~ .-rt-al-correct-dp-cr,
+  data = seqs.train,
   method = "pls",
   tuneLength = 20,
   trControl = plsTrControl,
@@ -131,11 +141,16 @@
 # PLS variable importance
 plot(varImp(pls.fit.accuracy), main="Accuracy - Variable Importance")
 
+pls.fit.accuracy.predicted <- predict(pls.fit.accuracy, seqs.test, type="raw")
+
+ggplot(seqs.test, aes(pls.fit.accuracy.predicted, a)) +
+  geom_point()
+
 
 #==================================================#
 # Train PLS model (rt)
-train_data_x <- train_data %>% select(-rt,-a,-correct) # all except rt
-train_data_y <- (train_data %>% select(rt))$rt # only rt
+train_data_x <- seqs.train %>% select(-rt,-a,-correct) # all except rt
+train_data_y <- (seqs.train %>% select(rt))$rt # only rt
 
 pls.fit.rt <- train(
   train_data_x,
@@ -151,52 +166,33 @@
 # PLS variable importance
 plot(varImp(pls.fit.rt), main="RT - Variable Importance")
 
-pls.predicted.rt <- predict(pls.fit.rt, test_data)
+pls.predicted.rt <- predict(pls.fit.rt, seqs.test)
 
-#FIXME
-confusionMatrix(pls.predicted.rt,test_data$rt)
-colAUC(pls.predicted.rt,test_data$rt, plotROC=T)
-
-#==================================================#
-# training control params for "correct" column
-glmTrControl <- trainControl(
-    method = "cv",
-    number = 5,
-    classProbs = T,
-    summaryFunction = twoClassSummary
-  )
-  
-glm.fit.correct <- train(
-  correct ~ .-rt-a-al,
-  data = train_data,
-  method = "glm",
-  family = "binomial",
-  trControl = glmTrControl
-)
-
-glm.fit.correct
-varImp(glm.fit.correct)
-
-
-glm.predicted.correct <- predict(glm.fit.correct, test_data, type="prob")
-
-#FIXME
-confusionMatrix(glm.predicted.correct, test_data$correct)
-colAUC(glm.predicted.correct, test_data$correct, plotROC=T)
+#FIXME measure performance with accuracy, etc.
+#confusionMatrix(pls.predicted.rt, seqs.test$rt)
+#colAUC(pls.predicted.rt, seqs.test$rt, plotROC=T)
 
 #==================================================#
 ## OLD MODEL (only global features)
-glm.fit.correct.old <- train(
-  correct ~ n+t+v,
-  data = train_data,
-  method = "glm",
-  family = "binomial",
-  trControl = trControl
-)
 
-glm.fit.correct.old
+pls.fit.accuracy.old <- train(
+  a ~ n+t+v,
+  data = seqs.train,
+  method = "pls",
+  tuneLength = 20,
+  trControl = plsTrControl,
+  preProc = c("center","scale"))
+ 
+# Check CV profile
+plot(pls.fit.accuracy.old)
 
-glm.fit.predicted.old <- predict(glm.fit.correct.old, test_data, type="prob")
+# PLS variable importance
+plot(varImp(pls.fit.accuracy.old), main="Accuracy (old) - Variable Importance")
+
+pls.fit.accuracy.old.predicted <- predict(pls.fit.accuracy.old, seqs.test, type="raw")
+
+ggplot(seqs.test, aes(pls.fit.accuracy.old.predicted, a)) +
+  geom_point()
 
 #FIXME
 confusionMatrix(glm.fit.predicted.old, test_data$correct, )
diff --git a/cnn2019-correct.R b/cnn2019-correct.R
new file mode 100644
index 0000000..a4e0b4c
--- /dev/null
+++ b/cnn2019-correct.R
@@ -0,0 +1,83 @@
+#==================================================#
+# model the "correct" column
+
+library(here)
+library(tidyverse)
+library(caret)
+library(inspectdf)
+
+
+load(here("data/nback_seqs.Rd"))
+
+set.seed(42)
+
+seqs.imputed <- seqs %>% 
+  filter(!is.na(correct), !is.na(rt)) %>%
+  mutate(correct=factor(correct,labels=c("INCORRECT","CORRECT")))
+
+inspect_cat(seqs.imputed)
+inspect_num(seqs.imputed)
+
+seqs.dummy <- predict(dummyVars(~.,data=seqs.imputed),seqs.imputed)
+
+
+train_indexes <- createDataPartition(seqs.imputed$correct,
+                                     times = 1,
+                                     p = 0.7,
+                                     list = F)
+
+train_data <- seqs.imputed[train_indexes,]
+test_data <- seqs.imputed[-train_indexes,]
+
+control <- trainControl(
+  method = "cv",
+  number = 5,
+  classProbs = T,
+  verboseIter = T,
+  summaryFunction = twoClassSummary
+)
+
+pls.model1 <- train(
+  correct ~ t + l + s + v + n + tl + ll + sl + ul + vl,
+  data = train_data,
+  method = "pls",
+  trControl = control
+)
+
+pls.model2 <- train(
+  correct ~ t + n + v,
+  data = train_data,
+  method = "pls",
+  trControl = control
+)
+
+pls.model1
+pls.model2
+varImp(pls.model1)
+varImp(pls.model2)
+
+trellis.par.set(caretTheme())
+densityplot(pls.model1, pch = "|")
+densityplot(pls.model2, pch = "|")
+
+resamps <- resamples(list(model1 = pls.model1, model2 = pls.model2))
+summary(resamps)
+dotplot(resamps, metric = "ROC")
+difValues <- diff(resamps)
+bwplot(difValues, layout=c(1,3))
+
+
+pls.train_predicted1 <- predict(pls.model1, train_data, type="prob")
+pls.train_predicted2 <- predict(pls.model2, train_data, type="raw")
+pls.predicted1 <- predict(pls.model1, test_data, type="raw")
+pls.predicted2 <- predict(pls.model2, test_data, type="raw")
+
+#FIXME
+confusionMatrix(pls.train_predicted1, train_data$correct)
+confusionMatrix(pls.train_predicted2, train_data$correct)
+confusionMatrix(pls.predicted1, test_data$correct)
+confusionMatrix(pls.predicted2, test_data$correct)
+
+colAUC(pls.predicted1, test_data$correct, plotROC=T)
+colAUC(pls.predicted2, test_data$correct, plotROC=T)
+
diff --git a/data/nback_seqs.Rd b/data/nback_seqs.Rd
new file mode 100644
index 0000000..a355010
--- /dev/null
+++ b/data/nback_seqs.Rd
Binary files differ