Inner functions pulls call from outer function and causes error - r

Question

I'm using a function from the library leaps within another function. The last two rows of the leaps function in question goes:
rval$call <- sys.call(sys.parent())
rval
This apparently causes the call to the outer function to be passed to rval$call. And the actual call to the regsubsets function is needed as an argument later on.
Below an example to illustrate:
library(leaps)
#Create some sample data to perform a regression on
inda <- rnorm(100)
indb <- rnorm(100)
dep <- 2 + 0.1*inda + 0.2*indb + rnorm(100, sd = 0.3)
dfk <- data.frame(dep=dep, inda = inda, indb = indb)
#Create some arbitrary outer function
test <- function(dependent, data){
best.fit <- regsubsets(as.formula(paste0(dependent, " ~ .")), data = data, nvmax = 2)
return(best.fit)
}
#Call outer function
best <- test("dep", dfk)
best$call #Returns "test("dep", dfk)"
So best$call will contain the call to the outer function (test), and not the call to the inner (regsubsets) function. As it's not really an option to change the inner function, is there any way of avoiding this problem?
EDIT:
One way around the problem could be something like this:
test <- function(dependent, data){
thecall <- 'regsubsets(as.formula(paste0(dependent, " ~ .")), data = data, nvmax = 2)'
best.fit <- eval(parse(text = thecall))
#best.fit$call <- [some transformation of thecall
return(best.fit)
}
EDIT2:
The reason I need to access what's inside $call is that it's needed in a predict function that I copied from Introduction to statitical learning:
predict.regsubsets <- function(regsubset_model, newdata, id, ...){
form <- as.formula(regsubset_model$call[[2]])
mat <- model.matrix(form, newdata)
coefi <- coef(regsubset_model, id = id)
xvars <- names(coefi)
mat[, xvars] %*% coefi
}
In the second line it uses $call

Answer 1

I’m still not entirely clear on how this is going to be used but in the case of your test function, you could write the following code:
test = function (dependent, data) {
regsubsets_call = bquote(regsubsets(.(as.formula(paste0(dependent, " ~ ."))),
data = .(substitute(data)), nvmax = 2))
best_fit = eval(regsubsets_call)
best_fit$call = regsubsets_call
best_fit
}
However, the result may not work with downstream functions the package provides (though, realistically, it probably will; I’m guessing summary.regsubsets only uses it to print the call).
What’s going on here?
bquote constructs an unevaluated R expression; it’s similar to quote but it allows you to interpolate values (similar to substitute). substitute(data) means that, rather than putting the actual data.frame into the call (which would lead to a very unwieldy output, it puts the variable name (or expression) the user passed to test. So if the user called it as test('mpg', mtcars), then the resulting expression would be
regsubsets(mpg ~ ., data = mtcars, nvmax = 2)
The resulting call object is then (a) evaluated via eval, and (b) stored in the resulting $call.
Incidentally, the formula can (and, as far as I’m concerned, should) be constructed in the same way; no need to parse a string:
as.formula(bquote(.(as.name(dependent)) ~ .))
Taken together, the whole expression would then become:
formula = as.formula(bquote(.(as.name(dependent)) ~ .))
regsubsets_call = bquote(regsubsets(.(formula), data = .(substitute(data)), nvmax = 2))