Às 15:28 de 24/03/2025, Stephen Bond via R-help escreveu:
Folks,
I appreciate your effort, but maybe I was not explicit enough, so let
me try again.
The current setup for formulas does not allow for I(x^2) terms as
explained in the MASS book at the end of Section 6.2 the x:x
interaction is treated as x.
So I need to write my own code, which is clumsy unless you can refer me
to a package that already exists or give me an idea how to improve my
code. Also, writing out terms is not feasible when there are 1000
variables, so the code needs to deal with taking a wide data frame or
matrix with column names for convenience.
Let me know your ideas :-)
On Mon, 2025-03-24 at 02:43 -0700, Bert Gunter wrote:
Full disclosure: I did not attempt to decipher your code.
But ~(A+B +C)^2 - (A + B + C)
gives all 2nd order interactions whether the terms are factors or
numeric.
~I(A^2) + I(B^2) gives quadratics in A and B, which must be numeric,
not factors, of course
You can combine these as necessary to get a formula expression for
just 2nd order terms. Wrapping this in model.matrix() should then
give you the model matrix using "treatment" contrasts for the
contrasts involving factors (you can change the contrast types using
the 'contrasts.arg' argument of model.matrix())
1. Does this help?
2. Do check this to make sure I'm correct
Cheers,
Bert
"An educated person is one who can entertain new ideas, entertain
others, and entertain herself."
On Mon, Mar 24, 2025 at 12:22 AM Stephen Bond via R-help
<r-help@r-project.org> 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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Hello,
Are you looking for ?poly to generate all 1st and 2nd order combinations?
In the outputs below, dimnames[[2]] tell which term corresponds to which
column.
So 1.0.0.0 is 'a' only and 2.0.0.0 is 'a^2'.
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)]
}
# XX
# model.matrix( ~ (a + b + c + d)^2 , data=X)
# model.matrix( ~ (a + b + c + d)^2 - (a + b + c +d), data=X)
# Is this what you want?
# Ugly colnames
m <- model.matrix( ~ poly(a, b, c, d, degree = 2L) , data = X)
dimnames(m)
#> [[1]]
#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13"
"14" "15"
#> [16] "16" "17" "18" "19" "20"
#>
#> [[2]]
#> [1] "(Intercept)" "poly(a, b, c, d, degree =
2)1.0.0.0"
#> [3] "poly(a, b, c, d, degree = 2)2.0.0.0" "poly(a, b, c, d, degree =
2)0.1.0.0"
#> [5] "poly(a, b, c, d, degree = 2)1.1.0.0" "poly(a, b, c, d, degree =
2)0.2.0.0"
#> [7] "poly(a, b, c, d, degree = 2)0.0.1.0" "poly(a, b, c, d, degree =
2)1.0.1.0"
#> [9] "poly(a, b, c, d, degree = 2)0.1.1.0" "poly(a, b, c, d, degree =
2)0.0.2.0"
#> [11] "poly(a, b, c, d, degree = 2)0.0.0.1" "poly(a, b, c, d, degree =
2)1.0.0.1"
#> [13] "poly(a, b, c, d, degree = 2)0.1.0.1" "poly(a, b, c, d, degree =
2)0.0.1.1"
#> [15] "poly(a, b, c, d, degree = 2)0.0.0.2"
# These colnames are nicer
p <- with(X, poly(a, b, c, d, degree = 2L))
attributes(p)
#> $dim
#> [1] 20 14
#>
#> $dimnames
#> $dimnames[[1]]
#> NULL
#>
#> $dimnames[[2]]
#> 2 3 4 5 7 10 11
13
#> "1.0.0.0" "2.0.0.0" "0.1.0.0" "1.1.0.0" "0.2.0.0" "0.0.1.0" "1.0.1.0"
"0.1.1.0"
#> 19 28 29 31 37 55
#> "0.0.2.0" "0.0.0.1" "1.0.0.1" "0.1.0.1" "0.0.1.1" "0.0.0.2"
#>
#>
#> $degree
#> [1] 1 2 1 2 2 1 2 2 2 1 2 2 2 2
#>
#> $coefs
#> $coefs[[1]]
#> $coefs[[1]]$alpha
#> [1] 0.1134201 0.4901752
#>
#> $coefs[[1]]$norm2
#> [1] 1.00000 20.00000 34.11147 140.62467
#>
#>
#> $coefs[[2]]
#> $coefs[[2]]$alpha
#> [1] 0.1356218 1.0041956
#>
#> $coefs[[2]]$norm2
#> [1] 1.00000 20.00000 21.53021 39.73742
#>
#>
#> $coefs[[3]]
#> $coefs[[3]]$alpha
#> [1] 0.2533081 0.1689801
#>
#> $coefs[[3]]$norm2
#> [1] 1.00000 20.00000 19.08499 19.83333
#>
#>
#> $coefs[[4]]
#> $coefs[[4]]$alpha
#> [1] -0.03597259 -0.87409789
#>
#> $coefs[[4]]$norm2
#> [1] 1.00000 20.00000 24.68273 73.41893
#>
#>
#>
#> $class
#> [1] "poly" "matrix"
dimnames(p)
#> [[1]]
#> NULL
#>
#> [[2]]
#> 2 3 4 5 7 10 11
13
#> "1.0.0.0" "2.0.0.0" "0.1.0.0" "1.1.0.0" "0.2.0.0" "0.0.1.0" "1.0.1.0"
"0.1.1.0"
#> 19 28 29 31 37 55
#> "0.0.2.0" "0.0.0.1" "1.0.0.1" "0.1.0.1" "0.0.1.1" "0.0.0.2"
Hope this helps,
Rui Barradas
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