> # load some personality data
> library(psych)
> data(bfi)
> scales <- c("a1", "a2", "a3")
> names(bfi) <- tolower(names(bfi))
> bfi <- bfi[,scales]
> bfi <- na.omit(bfi)
> head(bfi)
      a1 a2 a3
61617  2  4  3
61618  2  4  5
61620  5  4  5
61621  4  4  6
61622  2  3  3
61623  6  6  5
> # calculate overall mean
> bfi$omean <- apply(bfi[,scales], 1, mean)
> # calculate ipsative scores based regression residuals
> bfi$res_a1 <- resid(lm(a1 ~ omean, bfi))
> bfi$res_a2 <- resid(lm(a2 ~ omean, bfi))
> bfi$res_a3 <- resid(lm(a3 ~ omean,bfi))
> # calculate devaiation ipsative scores
> bfi$dev_a1 <- bfi$a1 - bfi$omean
> bfi$dev_a2 <- bfi$a2 - bfi$omean
> bfi$dev_a3 <- bfi$a3 - bfi$omean
> # check that both satisfy ipsatisation requirement
> all(round(apply(bfi[,paste0("res_", scales)], 1, sum), 6) == 0)
[1] TRUE
> all(round(apply(bfi[,paste0("dev_", scales)], 1, sum), 6) == 0)
[1] TRUE
> # Examine similarities between approaches (see diagonal)
> round(cor(bfi[paste0("res_", scales)], bfi[paste0("dev_", scales)]), 3)
       dev_a1 dev_a2 dev_a3
res_a1  0.981 -0.729 -0.711
res_a2 -0.716  0.999  0.065
res_a3 -0.718  0.067  0.971