diff --git a/ccn2019.Rmd b/ccn2019.Rmd
index 02322ca..d01e020 100644
--- a/ccn2019.Rmd
+++ b/ccn2019.Rmd
@@ -8,6 +8,7 @@
 library(ggplot2)
 library(stringi)
 library(GA)
+library(dbscan)
 ```
 
 # Introduction
@@ -147,7 +148,7 @@
 
 with_targets_ratio <- function(stimulus_type, history) {
   sapply(1:length(history), function(i) {
-    trials <- stimulus_type[(i-str_length(history[i])):i]
+    trials <- stimulus_type[(i-stri_length(history[i])):i]
     trials <- unlist(trials, use.names=FALSE)
     length(trials[trials=="target"])
   })
@@ -177,11 +178,12 @@
   })
 }
 
-with_history <- function(stims, max=8) {
+with_history <- function(stims, size=16) {
   res <- c('')
   for (i in 2:length(stims)) {
-    res[i] <- stri_sub(paste(res[i-1], stims[i], sep=''),from=-max,length=max)
+    res[i] <- stri_sub(paste(res[i-1], stims[i], sep=''),from=-size,length=size)
   }
+  #res <- ifelse(stri_length(res)==size, res, NA)
   res
 }
 
@@ -191,8 +193,6 @@
 }
 
 NB_modified <- NB %>%
-  #filter(participant=='P4') %>%
-  #filter(participant %in% c('P1','P2','P3','P5'), !is.na(correct)) %>%
   group_by(participant, condition, block) %>%
   mutate(history = with_history(stimulus)) %>%
   #mutate(stimulus_type = map_chr(.x=stimulus, stim_type=stimulus_type, history=history,.f=with_lures))
@@ -201,18 +201,70 @@
   mutate(l = with_lures_ratio(stimulus_type_2, history)) %>%
   mutate(s = with_skewness_score(stimulus, history)) %>%
   mutate(u = with_lumpiness_score(stimulus, history)) %>%
+  filter(stri_length(history)==16) %>%
   #normalize_scores(targets_ratio, lures_ratio, skewness, lumpiness) %>%
   ungroup()
 
+NB_modified %>%
+  mutate(correct = ifelse(stimulus_type=='burn-in',NA,correct)) 
+## group-level averaged NB, a single row represent an observation for a single subject
+## in a single condition
+
+NB_avg <- NB_modified %>%
+  group_by(participant, condition) %>%
+  mutate(correct = ifelse(stimulus_type=='burn-in',NA,correct)) %>%
+  summarise(targets=sum(t), lures=sum(l), skewness=sum(s), lumpiness=sum(u), rts = mean(rt, na.rm=T), accuracy=sum(correct,na.rm=T)/90) %>%
+  ungroup()
+
 # print
 # NB_modified %>%
 #   filter(participant=='P1') %>%
 #   View()
 #   
   
-pca=prcomp(NB_modified[,c('t','l','s','u')], center = TRUE,scale. = TRUE)
+pca <- prcomp(NB_modified[,c('t','l','s','u')], center = TRUE,scale. = TRUE)
 
-# rt/accuracy and lures
+NB_modified <- NB_modified %>% mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
+
+fit <- lm(correct ~ t * s, NB_modified)
+
+```
+
+
+```{r}
+
+# DBSCAN Clustering (RT+ACCURACY against skewness)
+NB_avg %>%
+  mutate(cluster = dbscan(cbind(accuracy,rts), eps = 0.5, minPts = 3)$cluster) %>%
+  ggplot(aes(skewness, accuracy, color=factor(cluster))) +
+  ggtitle("targets (window = 16 trials)", "NOTE: each point is a single participant") +
+  geom_point(alpha=0.3) +
+  #geom_smooth(method='lm', se = F) +
+  facet_wrap(~condition)
+
+```
+
+
+```{r}
+## single-subject figures
+NB_modified %>%
+  ggplot(aes(t,s,color=correct)) +
+  geom_jitter() +
+  geom_point() +
+  stat_summary(fun.y="mean")
+NB_modified %>%
+  filter(!is.na(correct)) %>%
+  ggplot(aes(jitter(s),jitter(t),color=correct)) +
+    geom_jitter() +
+    geom_point(alpha=0.1)
+
+NB_modified %>%
+  filter(!is.na(correct)) %>%
+  ggplot(aes(jitter(s),jitter(u),color=correct)) +
+    geom_jitter() +
+    geom_point() +
+    facet_wrap(~condition)
+
 # rt/accuracy and lures
 NB_modified %>%
   filter(!is.na(correct)) %>%
@@ -220,15 +272,30 @@
     geom_jitter() +
     geom_point(shape=16) +
     geom_smooth(method="lm",se = F) + 
-  facet_wrap(~condition, scales="free")
-# 
-# NB_modified %>%
-#   mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2']) %>%
-#   filter(!is.na(correct)) %>%
-#   ggplot(aes(jitter(pc1),jitter(pc2),color=rt)) +
-#     geom_jitter() +
-#     geom_point() +
-#     geom_smooth(method="lm",se = F) #+ facet_wrap(~correct, scales="free")
+    facet_wrap(~condition, scales="free")
+
+
+NB_modified %>%
+  filter(!is.na(correct)) %>%
+  ggplot(aes(pc1,pc2,color=correct)) +
+    geom_point() +
+    geom_smooth(method="lm",se = F) + 
+    facet_wrap(~condition, scales="free")
+
+NB_modified %>%
+  filter(!is.na(correct)) %>%
+  ggplot(aes(pc1,rt,color=correct)) +
+    geom_point() +
+    geom_smooth(method="lm",se = F) + 
+    facet_wrap(~condition, scales="free")
+
+NB_modified %>%
+  filter(!is.na(correct)) %>%
+  ggplot(aes(pc1,correct,color=correct)) +
+    geom_point() +
+    geom_smooth(method="lm",se = F) + 
+    facet_wrap(~condition, scales="free")
+
 
 ```