# Identify unique pairs within a given study (to keep things manageable) and create list of possible surrogate pairs (e.g. individual 1 from pair 1 and individual 2 from pair 2)
Groups <- as.numeric(as.character(unique(s4$group[s4$StudyNum==4]))) # Lists all pairs

SurrogateList <- expand.grid(a = Groups, b = Groups) # Identify all possible combinations of 2 pairs

SurrogateList = subset(SurrogateList, a != b) # Exclude combinations with identical pairs

surrodf <- tibble()
for (i in 1:nrow(SurrogateList)){  # loop through all combinations
  x <- subset(s4, group==SurrogateList$a[i]) # subset data from the first pair
  y <- subset(s4, group==SurrogateList$a[i]) # subset data from the second pair
  group <- c(800 + ((1:4)*i))                            # create new pair id
  for (co in c("Synchronous","TurnTaking", "Conversation", "MovementCoop", "MovementGuided")){ # loop through conditions
    if (co %in% unique(x$condition) & co %in% unique(y$condition)){      # check that both pairs have the data for that condition
      z1 <- subset(x, condition==co)            # subset only that condtion from first pair
      z2 <- subset(y, condition==co)            # subset only that condtion from second pair
      if (nrow(z1) > nrow(z2)) {    # make sure data have same length in both pairs
        z1<-z1[1:length(z2)]
        }
      if (nrow(z2) > nrow(z1)) {
        z2<-z2[1:length(z1)]
      }
      w1 <- z1 %>% mutate(  # assemble new pair combining the 2 pairs
        HR2 = z2$HR_other,
        Resp2 = z2$Resp_other,
        HR2_lead = z2$HR_self,
        Resp2_lead = z2$Resp_self,
        HR2_change = z2$changeHR_other,
        Resp2_change = z2$changeResp_other)
      }  ### DO SOMETHING TO SAVE THIS  # make sure that you do this!
      surrodf <- rbind(surrodf, w1)
     }}

# Starting from the wide format, create "surrogate" dataset with the data from surrogate pairs

# Make it into long format

# Create models as in chunks above, but adding an interaction with the Real vs. Surrogate variable (exclude shuffled ones for simplicity)