diff --git a/ccn2019/ccn2019.rev3.Rmd b/ccn2019/ccn2019.rev3.Rmd
index d8c501e..0db19ef 100644
--- a/ccn2019/ccn2019.rev3.Rmd
+++ b/ccn2019/ccn2019.rev3.Rmd
@@ -4,8 +4,6 @@
   chunk_output_type: inline
 ---
 
-$P=\langle V,D,C,W \rangle$
-
 ```{r setup, message=FALSE, include=FALSE, paged.print=FALSE}
 #! ===============================================
 #! load required packages
@@ -54,7 +52,7 @@
 
 The new data set is then saved in a file for further analysis. One final chuck of codes clean up the dataset by removing outlier participants and adding RT_CAT which shows high or low response times.
 
-```{r preprocessing}
+```{r preprocessing, message=FALSE, warning=FALSE, include=FALSE}
 
 #! ===============================================
 #! A function to mark lures in a sequence
@@ -100,6 +98,7 @@
 save(seqs.raw,file=here("data/nback_seqs.Rd"))
 #load(here("notebooks/data/nback_seqs.Rd"))
 
+# seqs is the main dataset of sequences including all stats extracted from a block or the sliding history window.
 ## classify RTs (mid and high), low RTs will be removed for now
 seqs <- seqs.raw %>% 
   mutate(
@@ -115,11 +114,11 @@
 The following chunk marks highly correlated predictors, but does not work anymore.
 ```{r remove_highly_correlated_predictors}
 # WIP: This is an extra step for non-pls methods to remove highly correlated predictors
-cor_matrix <- cor(seqs[,-1])
-cor_high <- findCorrelation(cor_matrix, 0.8)
-high_cor_remove <- row.names(cor_matrix)[cor_high]
+#cor_matrix <- cor(seqs[,-1])
+#cor_high <- findCorrelation(cor_matrix, 0.8)
+#high_cor_remove <- row.names(cor_matrix)[cor_high]
 #FIXME remove by column name
-seqs.uncorr <- seqs %>% select(-high_cor_remove)
+#seqs.uncorr <- seqs %>% select(-high_cor_remove)
 ```