[R] how to create model matrix of second order terms

[Daniel Nordlund]

Depending on what your ultimate goal is, you could use something like

want <- model.matrix(  ~ (a + b + c + d)^2 , data=your_data)

This will create a matrix with the appropriate main effects and first 
order interactions.  If you just want to run a simple regression, you 
could do it directly

lm(y ~ (a + b + c + d)^2 , data=your_data)

but the formula (a + b + c + d)^2 isn't accepted by all packages that do 
some form of regression.


Hope this is helpful,

Dan


On 3/23/2025 10:47 AM, Stephen Bond via R-help wrote:
> I am sending to this forum as stackoverflow has devolved into sth
> pretty bad.
> Below code shows how to get what I want in a clumsy way.
>
> cols <- letters[1:4]
> a1 <- outer(cols,cols,paste0)
> b1 <- a1[!lower.tri(a1)]
>
> X <- matrix(rnorm(80),ncol=4)
> colnames(X) <- cols
> X <- as.data.frame(X)
> XX <- matrix(0,nrow=nrow(X),ncol=length(b1))
> colnames(XX) <- b1
>
> for (k in 1:length(b1)){
>      XX[,k] <- X[,substr(b1[k],1,1)]*X[,substr(b1[k],2,2)]
> }
>
>
>
> Is there a way to get that using a formula or some neat trick? The
> above will not work for factors, so I will need to create the factor
> crossings using formula a*b*c and then cross with the numerics, which
> is even more clumsy.
> Thanks everybody
>
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-- 
Daniel Nordlund
Port Townsend, WA
(425) 273-5256