diff --git a/dummy-vars-playground.R b/dummy-vars-playground.R
index 3cdb681..7740ebe 100644
--- a/dummy-vars-playground.R
+++ b/dummy-vars-playground.R
@@ -8,17 +8,17 @@
 rm(seqs)
 load(here("notebooks/data/nback_seqs.Rd"))
 
-f <- as.formula("correct ~ n + stimulus + sl")
+f <- as.formula("correct ~ n + vl + sl")
 
-set.seed(42)
+set.seed(654321)
 
 # 1. dummy vars
 # INPUTS : seqs
 # OUTPUTS: seqs.dmy
 
-seqs <- seqs %>% filter(!is.na(correct) & !is.na(rt))
+seqs <- seqs %>% 
+  drop_na(rt, correct, tl,sl)
 
-#
 
 train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
 
@@ -45,22 +45,24 @@
 ctrl <- trainControl(method="cv",
                      number=5, 
                      classProbs=T,
-                     sampling = "up",
+                     verbose = T,
+#                     sampling = "up",
                      savePredictions = T,
                      summaryFunction=twoClassSummary)
 
 # glmnet tune
 tune <- expand.grid(alpha = 0:1, lambda = seq(0, 0.01, length = 100))
 
+max_components <- n_distinct(attr(terms(f),"term.labels"))
 # pls tune
-tune <- expand.grid(ncomp=1:20)
+tune <- expand.grid(ncomp=1:max_components)
 
 model <- train(seqs.train.x,
                seqs.train.y, 
                method = "pls",
                family = "binomial",
                metric = "ROC",
-               preProc = c("center", "scale"),
+               preProc = c("zv","center", "scale"),
                tuneGrid = tune,
                trControl = ctrl)
 
@@ -72,6 +74,8 @@
 
 confusionMatrix(seqs.test.y, seqs.test.observed_y)
 
+plot(varImp(model, scale = F))
+
 library(pROC)
 
 roc(seqs.test.observed_y,
@@ -86,6 +90,7 @@
     print.auc.x = 55,
     lty = 1,
     of = "se",
+    boot.n = 200,
     ci = T)