diff --git a/dummy-vars-playground.R b/dummy-vars-playground.R
index d956522..5a696db 100644
--- a/dummy-vars-playground.R
+++ b/dummy-vars-playground.R
@@ -8,6 +8,8 @@
 rm(seqs)
 load(here("notebooks/data/nback_seqs.Rd"))
 
+f <- as.formula("correct ~ stimulus + stimulus_type + n")
+
 set.seed(42)
 
 # 1. dummy vars
@@ -27,13 +29,14 @@
 
 
 seqs.train.balanced <- seqs[train.indices,]
-seqs.train <- ROSE(correct ~ ., data = seqs.train.balanced)$data
+seqs.train <- seqs.train.balanced
+# seqs.train <- ROSE(correct ~ ., data = seqs.train.balanced)$data
 
-seqs.train.x <- model.matrix(correct ~ stimulus + stimulus_type + n, seqs.train)[,-1]
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
 seqs.train.y <- seqs.train$correct
 
 seqs.test  <- seqs[-train.indices,]
-seqs.test.x <-  model.matrix(correct ~ stimulus + stimulus_type + n, seqs.test)[,-1]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
 seqs.test.observed_y <- seqs.test$correct
   
 # model <- cv.glmnet(seqs.train.x,
@@ -48,13 +51,15 @@
 ctrl <- trainControl(method="cv",
                      number=5, 
                      classProbs=T,
+                     sampling = "up",
+                     savePredictions = T,
                      summaryFunction=twoClassSummary)
 
 # glmnet tune
-tune <- expand.grid(alpha = 0:1, lambda = seq(0, 0.01, length = 100),ncomp=1:10)
+tune <- expand.grid(alpha = 0:1, lambda = seq(0, 0.01, length = 100))
 
 # pls tune
-tune <- expand.grid(ncomp=1:10)
+tune <- expand.grid(ncomp=1:6)
 
 model <- train(seqs.train.x,
                seqs.train.y, 
@@ -69,15 +74,31 @@
 plot(model)
 
 seqs.test.y <- model %>% predict(seqs.test.x)
+seqs.test.y_prob <- model %>% predict(seqs.test.x, type="prob")
 
 confusionMatrix(seqs.test.y, seqs.test.observed_y)
 
+library(pROC)
+
+roc(seqs.test.observed_y,
+    seqs.test.y_prob$YES,
+    legacy.axes=T,
+    plot = T,
+    lwd=2,
+    col="black",
+    print.auc=T,
+    percent = T,
+    print.auc.y = 40,
+    print.auc.x = 55,
+    lty = 1,
+    of = "se",
+    boot.n = 100,
+    ci = T)
+
+
 # RT
 # data.frame(
 #   RMSE = RMSE(y.test, seqs.test$correct),
 #   Rsquare = R2(y.test, seqs.test$correct)
 # )
 
-
-#dmy <- dummyVars(~.-stimulus-stimulus_type,seqs,fullRank = T)
-#dmy.rt <- dummyVars(~correct+stimulus_type,seqs)