Merge branch 'master' of https://github.com/DecisionNeurosciencePsych…

…opathology/pie

pie_inspect.R

@@ -14,34 +14,43 @@ library(stargazer)
 load("pie_data.rdata")
 
 df <- as.tibble(pie_data_proc$df)
-ff <- as.tibble(pie_firstfree)
+
+df = df %>% as_tibble %>% arrange(ID, block_num, trial)
 
 
 df<-df[!as.logical(df$forced_choice),]
 # value sampled on the first free choice as a function of even/uneven sampling -- should be lower in uneven
 
-# how often do they pick the never-sampled option in the uneven condition?
-uff <- ff[ff$even_uneven==1,]
-
 # inspect relative value and uncertainty signals
 df$num_segments <- as.factor(df$num_segments)
 df$show_points <- as.factor(df$show_points)
+df$even_uneven <- as.factor(df$even_uneven)
+df$forced_sampling <- NA
+df$forced_sampling[df$even_uneven==0] <- 'uneven'
+df$forced_sampling[df$even_uneven==1] <- 'even'
 
 
+ff <- as.tibble(df[(df$trial==5 & df$num_segments==4) | (df$trial==9 & df$num_segments==8),])
+
 # df$logVrel <- log(df$v_l)
 # df$logUrel <- log(df$u_l)
 
 # log-log value-uncertainty relationship
-ggplot(df,aes(logVrel,logUrel,color = num_segments)) + geom_point() + facet_wrap(~ID)
+ggplot(df,aes(v_l,u_l,color = num_segments)) + geom_point() + facet_wrap(~ID)
 
 # linear value-uncertainty relationship
 ggplot(df,aes(v_l,u_l,color = num_segments)) + geom_point() + facet_wrap(~ID)
 
 # do they switch from exploration to exploitation
 ggplot(df,aes(trial, selected_prob,color = num_segments, lty = show_points)) + geom_smooth() + facet_wrap(~ID)
+ggplot(df,aes(trial, selected_prob,color = num_segments, lty = show_points)) + 
+  geom_smooth(method = 'gam')
+
+ggplot(df,aes(trial, v_l,color = num_segments, lty = show_points)) + 
+  geom_smooth(method = 'gam')
 
 # formal look
-m1 <- lmer(selected_prob ~ num_segments + show_points + trial + (1|ID), df)
+m1 <- lmer(selected_prob ~ num_segments * show_points + trial + (1|ID), df)
 summary(m1)
 car::Anova(m1,'3')
 
@@ -52,38 +61,10 @@ car::Anova(m2,'3')
 
 
 
-ggplot(df,aes(trial, logUrel,color = num_segments)) + geom_smooth() + facet_wrap(~ID)
-
-
-ggplot(df,aes(trial,v_l,color = selected_segment)) + geom_smooth() + facet_wrap(~ID)
-
-
-
-ggplot(df, aes(x = trial, y = selected_prob, color = as.factor(num_segments))) + geom_smooth(method = "gam") + facet_wrap(ID~show_points,ncol = 2)
-
-# get lags
-df = df %>% arrange(ID, block_num, trial) %>% group_by(ID, block_num) %>% 
-  mutate(
-    choice1 = selected_segment==1,
-    choice2 = selected_segment==2,
-    choice3 = selected_segment==3,
-    choice4 = selected_segment==4,
-    choice5 = selected_segment==5,
-    choice6 = selected_segment==6,
-    choice7 = selected_segment==7,
-    choice8 = selected_segment==8
-    # least_sampled = which(c(cum1,cum2,cum3,cum4))
-      ) %>% ungroup()
-df = df %>% arrange(ID, trial)
-
-# ggplot(s2, aes(x = trial, y = selected_prob, color = as.factor(num_segments))) + geom_smooth(method = "gam") + facet_wrap(~show_points)
-# ggplot(s4, aes(x = trial, y = selected_prob, color = as.factor(num_segments))) + geom_smooth(method = "gam") + facet_wrap(~show_points)
-
-ggplot(df, aes(x = trial, y = selected_prob, color = as.factor(num_segments))) + geom_smooth(method = "gam") + facet_wrap(~show_points)
+ggplot(df,aes(trial, u_l,color = num_segments)) + geom_smooth(method = 'gam') + facet_wrap(~ID)
+ggplot(df,aes(trial, -u_l,color = num_segments, lty = show_points)) + geom_smooth(method = 'gam')
 
+ggplot(ff,aes(forced_sampling,samphx_lag,color = show_points, shape = show_points)) + geom_jitter() + facet_wrap(~num_segments)
 
-ggplot(s1[s1$forced_choice==0,], aes(x = selected_segment, color = as.factor(even_uneven))) + geom_histogram(position = "identity") + facet_wrap(~num_segments)
-ggplot(s2[s2$forced_choice==0,], aes(x = selected_segment, color = as.factor(even_uneven))) + geom_histogram(position = "identity") + facet_wrap(~num_segments)
-ggplot(s4[s4$forced_choice==0,], aes(x = selected_segment, color = as.factor(even_uneven))) + geom_histogram(position = "identity") + facet_wrap(~num_segments)
+ggplot(ff,aes(even_uneven,rewhx_lag,color = show_points, shape = show_points)) + geom_jitter() + facet_wrap(~num_segments)
 
-ggplot(df[df$forced_choice==0,], aes(x = selected_segment, color = as.factor(even_uneven))) + geom_histogram(position = "identity") + facet_wrap(~num_segments)