diff --git a/ccn2019.rev3.Rmd b/ccn2019.rev3.Rmd
index 9f38d9b..7a879be 100644
--- a/ccn2019.rev3.Rmd
+++ b/ccn2019.rev3.Rmd
@@ -13,6 +13,7 @@
 library(rsample)
 library(inspectdf)
 library(caTools)
+library(pROC)
 ```
 
 
@@ -45,12 +46,14 @@
   mutate(tl = slide2_dbl(stimulus_type, rt, ~length(which(.x=='target'))/length(which(!is.na(.y))), .partial=T,.size=window_size),
          ll = slide2_dbl(stimulus_type, rt, ~length(which(.x=='lure'))/length(which(!is.na(.y))), .partial=T, .size=window_size),
          sl = slide_dbl(stimulus_type, ~sum(sort(table(.x), decreasing = T)[1:2]) - 1, .partial=T, .size=window_size),
+         ul = slide_dbl(stimulus, ~max(table(.))-1, .partial=T, .size=window_size),
+         vl = slide_dbl(stimulus, ~length(unique(.)), .partial=T, .size=window_size),
+         al = slide2_dbl(correct, rt, ~length(which(.x))/length(which(!is.na(.y))), .partial=T, .size=window_size),
          sl = ifelse(is.na(sl), 0, sl),
          tl = ifelse(is.na(tl), NA, tl),
          ll = ifelse(is.na(ll), NA, ll),
-         ul = slide_dbl(stimulus, ~max(table(.))-1, .partial=T, .size=window_size),
-         vl = slide_dbl(stimulus, ~length(unique(.)), .partial=T, .size=window_size),
-         al = slide2_dbl(correct, rt, ~length(which(.x))/length(which(!is.na(.y))), .partial=T, .size=window_size)) %>%
+         al = ifelse(is.na(al), NA, al)
+) %>%
   nest(.key='local_stats') %>%
   #mutate(stimuli = map(local_stats, ~paste0(.x$stimulus,collapse = ''))) %>%
   mutate(a  = map_dbl(local_stats, ~length(which(.x$correct)))) %>%
@@ -60,29 +63,41 @@
   mutate(v  = map_dbl(local_stats, ~length(unique(.x$stimulus)))) %>%
   mutate(local_stats = map(local_stats, ~.x %>% select(-trial,-stimulus,-stimulus_type,-choice))) %>%
   ungroup() %>%
-  select(-participant,-block,-condition)
+  select(-participant,-block,-condition) %>%
+  unnest(local_stats)
 
 #! ===============================================
 #! visualize correlations
-inspect_cor(seqs %>% unnest(local_stats), show_plot = T)
+#DEBUG inspect_cor(seqs, show_plot = T)
 ```
 
 ```{r models}
 #! ===============================================
 #! prepare data for modeling (remove na, etc)
-#! it also restructures "correct" column to avoid caret errors. C stands for "CORRECT", and I is "INCORRECT"
-data <- seqs %>% 
-  unnest(local_stats) %>%
-  mutate(correct=factor(as.numeric(correct),labels=c("C","I"))) %>%
-  filter(!is.na(correct), !is.na(rt))
+seqs <- seqs %>% 
+  filter(!is.na(correct), !is.na(rt)) %>%
+  #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
+
+# replace factors with dummy data
+#seqs.dummy <- predict(dummyVars(~.,data=seqs[,-1]),seqs[,-1])
+
+# impute missing values
+#seqs.imputed <- predict(preProcess(seqs.dummy, "bagImpute"), seqs.dummy)
+#DEBUG View(seqs.imputed)
 
 #! ===============================================
-#! Prepare train and test partials
-shuff <- sample(nrow(data))
-split <- nrow(data) * 0.8
+#! split into train/test (consider class-inbalence for the correct)
+set.seed(42)
+train_indexes <- createDataPartition(seqs$correct,
+                                     times = 1,
+                                     p = 0.7,
+                                     list = F)
 
-train_data <- data[1:split,]
-test_data <- data[(split+1):nrow(data),]
+seqs.train <- seqs[train_indexes,]
+seqs.test <- seqs[-train_indexes,]
 
 #! ===============================================
 #! training parameters for the PLS models
@@ -92,28 +107,28 @@
   )
 
 #==================================================#
-# Train PLS model (accuracy)
-model_pls_accuracy <- train(
+# Train PLS model (block-level accuracy)
+pls.fit.accuracy <- train(
   a ~ .-rt-al-correct,
   data = train_data,
   method = "pls",
   tuneLength = 20,
   trControl = plsTrControl,
-  preProc = c("zv","center","scale"))
+  preProc = c("center","scale"))
  
 # Check CV profile
-plot(model_pls_accuracy)
+plot(pls.fit.accuracy)
 
 # PLS variable importance
-plot(varImp(model_pls_accuracy), main="Accuracy - Variable Importance")
+plot(varImp(pls.fit.accuracy), main="Accuracy - Variable Importance")
 
 
 #==================================================#
 # Train PLS model (rt)
-train_data_x <- data %>% select(-rt,-a,-correct)
-train_data_y <- (data %>% select(rt))$rt
+train_data_x <- train_data %>% select(-rt,-a,-correct) # all except rt
+train_data_y <- (train_data %>% select(rt))$rt # only rt
 
-model_pls_rt <- train(
+pls.fit.rt <- train(
   train_data_x,
   train_data_y,
   method = "pls",
@@ -122,47 +137,47 @@
   preProc = c("center","scale"))
  
 # Check CV profile
-plot(model_pls_rt)
+plot(pls.fit.rt)
 
 # PLS variable importance
-plot(varImp(model_pls_rt), main="RT - Variable Importance")
+plot(varImp(pls.fit.rt), main="RT - Variable Importance")
 
-predicted_rt_data <- predict(model_pls_rt, test_data)
+pls.predicted.rt <- predict(pls.fit.rt, test_data)
 
 #FIXME
-confusionMatrix(predicted_rt_data,test_data$rt)
-colAUC(predicted_rt_data,test_data$rt, plotROC=T)
+confusionMatrix(pls.predicted.rt,test_data$rt)
+colAUC(pls.predicted.rt,test_data$rt, plotROC=T)
 
 #==================================================#
 # training control params for "correct" column
-trControl <- trainControl(
+glmTrControl <- trainControl(
     method = "cv",
     number = 5,
     classProbs = T,
     summaryFunction = twoClassSummary
   )
   
-model_glm_correct <- train(
+glm.fit.correct <- train(
   correct ~ .-rt-a-al,
   data = train_data,
   method = "glm",
   family = "binomial",
-  trControl = trControl
+  trControl = glmTrControl
 )
 
-model_glm_correct
-varImp(model_glm_correct)
+glm.fit.correct
+varImp(glm.fit.correct)
 
 
-predicted_correct_data <- predict(model_glm_correct, test_data, type="prob")
+glm.predicted.correct <- predict(glm.fit.correct, test_data, type="prob")
 
 #FIXME
-confusionMatrix(predicted_correct_data, test_data$correct)
-colAUC(predicted_correct_data, test_data$correct, plotROC=T)
+confusionMatrix(glm.predicted.correct, test_data$correct)
+colAUC(glm.predicted.correct, test_data$correct, plotROC=T)
 
 #==================================================#
 ## OLD MODEL (only global features)
-model_glm_correct_old <- train(
+glm.fit.correct.old <- train(
   correct ~ n+t+v,
   data = train_data,
   method = "glm",
@@ -170,11 +185,12 @@
   trControl = trControl
 )
 
-model_glm_correct_old
+glm.fit.correct.old
 
-predicted_old_correct_data <- predict(model_glm_correct_old, test_data, type="prob")
+glm.fit.predicted.old <- predict(glm.fit.correct.old, test_data, type="prob")
 
 #FIXME
-confusionMatrix(test_data$correct, predicted_old_correct_data)
-colAUC(predicted_old_correct_data,test_data$correct, plotROC=T)
+confusionMatrix(glm.fit.predicted.old, test_data$correct, )
+colAUC(glm.fit.predicted.old, test_data$correct, plotROC=T)
+#TODO use pROC to viz AUX roc(test_data$correct,glm.fit.predicted.old)
 ```