diff --git a/ccn2019-featsel.R b/ccn2019-featsel.R
new file mode 100644
index 0000000..d59cd52
--- /dev/null
+++ b/ccn2019-featsel.R
@@ -0,0 +1,39 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+seqs <- seqs %>% drop_na(rt, correct, tl,sl)
+
+f <- correct ~ n + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- ROSE(f, data = seqs.train.balanced)$data
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+
+set.seed(10)
+
+ctrl <- rfeControl(functions = lrFuncs,
+                   method = "cv",
+                   number = 5,
+                   verbose = T)
+
+rmProfile <- rfe(seqs.train.x, seqs.train.y,
+                 rfeControl = ctrl)
+
+rmProfile

diff --git a/ccn2019-featsel.R b/ccn2019-featsel.R
new file mode 100644
index 0000000..d59cd52
--- /dev/null
+++ b/ccn2019-featsel.R
@@ -0,0 +1,39 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+seqs <- seqs %>% drop_na(rt, correct, tl,sl)
+
+f <- correct ~ n + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- ROSE(f, data = seqs.train.balanced)$data
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+
+set.seed(10)
+
+ctrl <- rfeControl(functions = lrFuncs,
+                   method = "cv",
+                   number = 5,
+                   verbose = T)
+
+rmProfile <- rfe(seqs.train.x, seqs.train.y,
+                 rfeControl = ctrl)
+
+rmProfile
diff --git a/ccn2019-svm.R b/ccn2019-svm.R
new file mode 100644
index 0000000..a1590a5
--- /dev/null
+++ b/ccn2019-svm.R
@@ -0,0 +1,79 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+
+f <- correct ~ n + stimulus_type + stimulus + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+seqs <- seqs %>%drop_na(rt, correct, tl,sl)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- seqs.train.balanced
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+ctrl <- trainControl(method="cv",
+                     number=10,
+#                     repeats = 1, #repeatedcv
+                     sampling = "up",
+                     savePredictions = T,
+                     verbose = T)
+
+tune <- expand.grid(C = seq(0,5,by=0.25))
+
+model <- train(seqs.train.x,
+               seqs.train.y, 
+               method = "svmLinear",
+               preProc = c("center", "scale"),
+               tuneLength = 10,
+               tuneGrid = tune,
+               trControl = ctrl)
+
+model$bestTune
+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)
+
+plot(varImp(model, scale = F, useModel = F))
+
+library(pROC)
+
+roc(seqs.test.observed_y,
+    seqs.test.y_prob$YES,
+    legacy.axes=T,
+    plot = T,
+    lwd=2,
+    col="black",
+    print.auc=T,
+    percent = T,
+    print.auc.y = 40,
+    print.auc.x = 55,
+    lty = 1,
+    of = "se",
+    boot.n = 200,
+    ci = T)
+
+
+# RT
+# data.frame(
+#   RMSE = RMSE(y.test, seqs.test$correct),
+#   Rsquare = R2(y.test, seqs.test$correct)
+# )
+

diff --git a/ccn2019-featsel.R b/ccn2019-featsel.R
new file mode 100644
index 0000000..d59cd52
--- /dev/null
+++ b/ccn2019-featsel.R
@@ -0,0 +1,39 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+seqs <- seqs %>% drop_na(rt, correct, tl,sl)
+
+f <- correct ~ n + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- ROSE(f, data = seqs.train.balanced)$data
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+
+set.seed(10)
+
+ctrl <- rfeControl(functions = lrFuncs,
+                   method = "cv",
+                   number = 5,
+                   verbose = T)
+
+rmProfile <- rfe(seqs.train.x, seqs.train.y,
+                 rfeControl = ctrl)
+
+rmProfile
diff --git a/ccn2019-svm.R b/ccn2019-svm.R
new file mode 100644
index 0000000..a1590a5
--- /dev/null
+++ b/ccn2019-svm.R
@@ -0,0 +1,79 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+
+f <- correct ~ n + stimulus_type + stimulus + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+seqs <- seqs %>%drop_na(rt, correct, tl,sl)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- seqs.train.balanced
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+ctrl <- trainControl(method="cv",
+                     number=10,
+#                     repeats = 1, #repeatedcv
+                     sampling = "up",
+                     savePredictions = T,
+                     verbose = T)
+
+tune <- expand.grid(C = seq(0,5,by=0.25))
+
+model <- train(seqs.train.x,
+               seqs.train.y, 
+               method = "svmLinear",
+               preProc = c("center", "scale"),
+               tuneLength = 10,
+               tuneGrid = tune,
+               trControl = ctrl)
+
+model$bestTune
+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)
+
+plot(varImp(model, scale = F, useModel = F))
+
+library(pROC)
+
+roc(seqs.test.observed_y,
+    seqs.test.y_prob$YES,
+    legacy.axes=T,
+    plot = T,
+    lwd=2,
+    col="black",
+    print.auc=T,
+    percent = T,
+    print.auc.y = 40,
+    print.auc.x = 55,
+    lty = 1,
+    of = "se",
+    boot.n = 200,
+    ci = T)
+
+
+# RT
+# data.frame(
+#   RMSE = RMSE(y.test, seqs.test$correct),
+#   Rsquare = R2(y.test, seqs.test$correct)
+# )
+
diff --git a/dummy-vars-playground.R b/dummy-vars-playground.R
index 1097da7..d79b629 100644
--- a/dummy-vars-playground.R
+++ b/dummy-vars-playground.R
@@ -8,7 +8,13 @@
 rm(seqs)
 load(here("notebooks/data/nback_seqs.Rd"))
 
-f <- as.formula("correct ~ n + vl + sl")
+# seqs %>%
+#   ggplot(aes(x=v,y=a,col=correct)) +
+#   geom_jitter() +
+#   geom_point(alpha=0.1) +
+#   geom_smooth()
+
+f <- correct ~ n + stimulus + stimulus_type + t + s + v + l + vl + sl + tl + ul + ll
 
 set.seed(654321)
 
@@ -33,6 +39,10 @@
 seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
 seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
   
+
+# ROC for each var
+filterVarImp(as.data.frame(seqs.train.x), seqs.train.y)
+
 # model <- cv.glmnet(seqs.train.x,
 #                    seqs.train.y,
 #                    alpha = 1,
@@ -62,7 +72,9 @@
                method = "pls",
                family = "binomial",
                metric = "ROC",
-               preProc = c("zv","center", "scale"),
+               preProc = c("center", "scale"),
+               verboseIter = TRUE,
+               tuneLength = 10,
                tuneGrid = tune,
                trControl = ctrl)