Passing the weights argument to a regression function inside an R function - r

Question

I am trying to write an R function to run a weighted (optional) regressions, and I am having difficulties getting the weight variable to work.
Here is a simplified version of the function.
HC <- function(data, FUN, formula, tau = 0.5, weights = NULL){
if(is.null(weights)){
est <- FUN(data = data, formula = formula, tau = tau)
intercept = est$coef[["(Intercept)"]]
zeroWorker <- exp(intercept)
}
else {
est <- FUN(data = data, formula = formula, tau = tau, weights = weights)
intercept = est$coef[["(Intercept)"]]
zeroWorker <- exp(intercept)
}
return(zeroWorker)
}
The function works perfectly if I do not use the weights argument.
mod1 <- HC(data = mydata, formula = lin.model, tau = 0.2,
FUN = rq)
But, throws an error message when I use the weights argument.
mod2 <- HC(data = mydata, formula = lin.model, tau = 0.2,
FUN = rq, weights = weig)
I google the problem, and this post seems to be the closest to my problem, but I could still not get it to work. R : Pass argument to glm inside an R function.
Any help will be appreciated.
My problem can be replicated with:
library("quantreg")
data(engel)
mydata <- engel
mydata$weig <- with(mydata, log(sqrt(income))) # Create a fictive weigth variable
lin.model <- foodexp~income
mod1 <- HC(data = mydata, formula = lin.model, tau = 0.2,
FUN = rq) # This works perfectly
mod2 <- HC(data = mydata, formula = lin.model, tau = 0.2,
FUN = rq, weights = weig) # throws an error.
Error in HC(data = mydata, formula = lin.model, tau = 0.2, FUN = rq, weights = weig) :
object 'weig' not found

Answer 1

You have two problems. The error you're encountering is because you're trying to use the weigh variable without referencing it as coming from the mydata dataset. Try using mydata$weig. This will solve your first error, but you then get the actual one related to using the weights argument, which is:
Error in model.frame.default(formula = formula, data = data, weights = substitute(weights), :
invalid type (symbol) for variable '(weights)'
The solution is to add the variable specified in HC's weights argument to the dataframe before passing it to FUN:
HC <- function(data, FUN, formula, tau = 0.5, weights = NULL){
data$.weights <- weights
if(is.null(weights)){
est <- FUN(data = data, formula = formula, tau = tau)
} else {
est <- FUN(data = data, formula = formula, tau = tau, weights = .weights)
}
intercept = est$coef[["(Intercept)"]]
zeroWorker <- exp(intercept)
return(zeroWorker)
}
Then everything works:
mod2 <- HC(data = mydata, formula = lin.model, tau = 0.2, FUN = rq, weights = mydata$weig)
mod2
# [1] 4.697659e+47