this is my dataset:
Variable_1 <- c(3, 5, 6, 9, 12)
Variable_2 <- c(5, 5, 7, 6, 10)
Variable_3 <- c(0, 1, 3, 3, 5)
Variable_4 <- c(6, 7, 5, 10, 10)
X <- data.frame(Variable_1, Variable_2, Variable_3, Variable_4)
I want to calculate the pairwise correlation of these variables, with interaction with a new variable (x). However, when I do this with my codes, the following error appears:
with(data.frame(x = rnorm(5)),
cor(model.matrix(~ . * x, X)))
> 1: In terms.formula(object, data = data) :
'varlist' has changed (from nvar=4) to new 5 after EncodeVars() -- should no longer happen!
> 2: In terms.formula(formula, data = data) :
'varlist' has changed (from nvar=4) to new 5 after EncodeVars() -- should no longer happen!
> 3: In cor(model.matrix(~. * x, X)) : standard deviation is zero
Why does it happen? And how can I fix this?
>Solution :
If you add x to the X data instead of using with in a separate data frame, that solves the errors 1 and 2. Warning 3 about the SD being 0 is because the model formula you have includes an intercept term, we can use + 0 or - 1 in the formula to not include an intercept.
cbind(X, x = rnorm(5)) |>
model.matrix(object = ~ . * x - 1) |>
cor()
# Variable_1 Variable_2 Variable_3 Variable_4 x Variable_1:x
# Variable_1 1.00000000 0.8524929 0.94311913 0.82929841 -0.6392038 0.03777884
# Variable_2 0.85249292 1.0000000 0.91532579 0.48178766 -0.6351861 -0.31367519
# Variable_3 0.94311913 0.9153258 1.00000000 0.60163630 -0.6868033 -0.04758412
# Variable_4 0.82929841 0.4817877 0.60163630 1.00000000 -0.3359712 0.29232862
# x -0.63920377 -0.6351861 -0.68680333 -0.33597124 1.0000000 0.56683001
# Variable_1:x 0.03777884 -0.3136752 -0.04758412 0.29232862 0.5668300 1.00000000
# Variable_2:x -0.53418365 -0.5306942 -0.54239657 -0.30656762 0.9766116 0.66241818
# Variable_3:x 0.45817294 0.1534868 0.51245819 0.35902826 -0.1084688 0.70426363
# Variable_4:x -0.33930292 -0.5650112 -0.47714884 0.06483919 0.8915007 0.83829795
# Variable_2:x Variable_3:x Variable_4:x
# Variable_1 -0.53418365 0.45817294 -0.33930292
# Variable_2 -0.53069420 0.15348678 -0.56501121
# Variable_3 -0.54239657 0.51245819 -0.47714884
# Variable_4 -0.30656762 0.35902826 0.06483919
# x 0.97661158 -0.10846875 0.89150066
# Variable_1:x 0.66241818 0.70426363 0.83829795
# Variable_2:x 1.00000000 0.06749953 0.89434134
# Variable_3:x 0.06749953 1.00000000 0.20684320
# Variable_4:x 0.89434134 0.20684320 1.00000000