Reputation: 13833
For the sake of example, consider a basic regression model in R:
form1 <- Petal.Length ~ Sepal.Length + Sepal.Width
fit1 <- lm(form1, iris)
(My apologies to any botanists who post here.)
In order to add quadratic and interaction terms, I know of three approaches:
Entering terms one at a time:
form2 <- . ~ Sepal.Length*Sepal.Width + I(Sepal.Length^2) + I(Sepal.Width^2)
fit2 <- update(fit1, form2)
This doesn't scale beyond small formulas and you can't program with it.
String manipulation:
vars <- attr(terms(form1), "term.labels")
squared_terms <- sprintf("I(%s^2)", vars)
inter_terms <- combn(vars, 2, paste, collapse = "*")
form2 <- reformulate(c(inter_terms, squared_terms), ".")
This scales, but it's not really programmable because the functions themselves need to be hard-coded.
Manipulate the data directly
library(lazyeval)
library(dplyr)
square <- function (v) interp(~ I(v1^2), v1 = as.name(v))
inter <- function(v) interp(~ v1*v2, v1 = as.name(v[1]), v2 = as.name(v[2]))
vars <- attr(terms(form1), "term.labels")
squared_terms <- lapply(vars, square) %>%
set_names(paste0(vars, " ^2"))
inter_terms <- combn(vars, 2, inter, simplify = FALSE) %>%
set_names(combn(vars, 2, paste, collapse = " x "))
fit2 <- model.frame(fit1) %>%
mutate_(.dots = squared_terms) %>%
mutate_(.dots = inter_terms) %>%
lm(Petal.Length ~ ., data = .)
This is fairly scalable, and programmable up to variable naming. But it's also kind of crazy because it defeats the purpose of using a formula
in the first place.
I wish I could do something like this:
library(lazyeval)
library(dplyr)
square <- function (v) interp(~ I(v1^2), v1 = as.name(v))
inter <- function(v) interp(~ v1*v2, v1 = as.name(v[1]), v2 = as.name(v[2]))
squared_terms <- apply.formula(form1, squared_terms)
inter_terms <- combn.formula(form1, 2, inter)
fit2 <- form1 %>%
append.formula(squared_terms) %>%
append.formula(inter_terms) %>%
update(fit1, .)
Abuse of dplyr
aside, there are two killer features here:
Feature 1 is obtainable with Method 3, and Feature 2 is obtainable with Method 2. Is there a Method 4 -- the middle way -- that obtains both at the same time?
Upvotes: 5
Views: 643
Reputation: 206197
OK, there are a lot of moving pieces here, but here are some helper functions that to very specific things
extract_rhs_symbols <- function(x) {
as.list(attr(delete.response(terms(x)), "variables"))[-1]
}
symbols_to_formula <- function(x) {
as.call(list(quote(`~`), x))
}
sum_symbols <- function(...) {
Reduce(function(a,b) bquote(.(a)+.(b)), do.call(`c`, list(...), quote=T))
}
square_terms <- function(x) {
symbols_to_formula(sum_symbols(sapply(extract_rhs_symbols(x), function(x) bquote(I(.(x)^2)))))
}
interact_rhs<-function(x) {
x[[length(as.list(x))]] <- bquote((.(x[[length(as.list(x))]]))^2)
x
}
add_rhs_dot <- function(x) {
x[[length(as.list(x))]] <- sum_symbols(quote(.), x[[length(as.list(x))]])
x
}
add_terms<-function(f, x) {
update(f, add_rhs_dot(x))
}
all of these basically manipulate formulas as calls.
So if you have a formula like
my.formula <- Petal.Length ~ Sepal.Length + Sepal.Width + Other
You can add squared terms with
add_terms(my.formula, square_terms(my.formula))
you can get all the right-hand interactions with
interact_rhs(my.formula)
or do both with
add_terms(interact_rhs(my.formula), square_terms(my.formula))
which gives
Petal.Length ~ Sepal.Length + Sepal.Width + Other + I(Sepal.Length^2) +
I(Sepal.Width^2) + I(Other^2) + Sepal.Length:Sepal.Width +
Sepal.Length:Other + Sepal.Width:Other
I haven't throughly tested this so there are likely to be cases where this breaks down, but it should work in most simple cases.
Upvotes: 11