Scoping issue when using doParallel - r

Question

I am trying to estimate multiple nonparametric models using the doParallel package. My problem though seems to be related to the np package.
Take a look at this reproducible example:
library(np)
library(doParallel)
df <- data.frame(Y = runif(100, 0, 10), X = rnorm(100))
models <- list(as.formula(Y ~ X))
npestimate <- function(m, data) {
LCLS <- npregbw(m, data = data, regtype = "lc", bwmethod = "cv.ls")
LLLS <- npregbw(m, data = data, regtype = "ll", bwmethod = "cv.ls")
# sigt <- npsigtest(LCLS, boot.method = "wild", boot.type = "I")
return(list(LCLS = LCLS, LLLS = LLLS))
}
cl <- makeCluster(length(models))
registerDoParallel(cl)
results <- foreach(m = models, .packages = "np", .verbose = T) %dopar%
npestimate(m, data = df)
stopCluster(cl)
As you can see I created a function called npestimate() in order to compute different stuff for each model. I commented out one line where I want to run significance tests using npsigtest. Usually, npsigtest gets the data used by looking in the environment where npregbw was called.
But this does not work here. I am not sure why but npsigtest just cannot find the data that was used in the two lines of code right above.
The data is automatically exported to the nodes, so using .export in foreach is redundant.
Any suggestions how to make this work?

Answer 1

npsigtest copies pretty much the approach used within lm and functions for lm objects. It thus has the same potential scoping pitfalls. The issue is the environment associated with the formula:
environment(models[[1]])
#<environment: R_GlobalEnv>
It's easy to fix:
npestimate <- function(m, data) {
environment(m) <- environment()
LCLS <- npregbw(m, data = data, regtype = "lc", bwmethod = "cv.ls")
LLLS <- npregbw(m, data = data, regtype = "ll", bwmethod = "cv.ls")
sigt <- npsigtest(LCLS, boot.method = "wild", boot.type = "I")
return(list(LCLS = LCLS, LLLS = LLLS))
}
I actually often prefer eval(bquote()) constructs because of such issues.