For Scott, combinations of vectors that are survey answers.

vector-combinations.R

# Change to your data
data <- data.frame(resp=1:5,
                   w11=sample(1:5, 5, replace=TRUE), 
                   w12=sample(1:5, 5, replace=TRUE), 
                   w13=sample(1:5, 5, replace=TRUE))
data$LoySum <- apply(data[, c("w11", "w12", "w13")], 1, mean)                   
                   

# Names of the variables we want to look at
q.names <- c("w11", "w12", "w13")

# List of the k for combinations you want to try
items <- c(2, 3)  # c(2:6)

# Actual code
measures <- data.frame(resp=data$resp)
for (k in items) {
  combinations <- combn(q.names, k)
  cat(paste0("Calculating ", ncol(combinations), " combinations with k=", k, "\n"))
  name.list <- NULL
  for (i in 1:ncol(combinations)) {
    name <- paste(combinations[, i], collapse=".")
    name.list <- c(name.list, name)
    assign(name, apply(data[, combinations[, i]], 1, mean))
  }
  obj.list <- lapply(name.list, get)
  names(obj.list) <- name.list
  res <- do.call(cbind, obj.list)
  measures <- cbind(measures, res)
}

#measures

# Find correlations with LoySum
cor2 <- function(item) {
  res <- cor(data[, "LoySum"], item)
}

# Combine pairwise correlations with k (number of items that were combined)
cors <- unlist(lapply(measures[, 2:ncol(measures)], cor2))
k <- unlist(lapply(strsplit(names(cors), "[.]"), length))
t(rbind(cors, k))

# Plot
plot(k, cors)