R function with functions as arguments, each with variable arguments

In answer to a question on Cross Validated, I wrote a simple function that used arbitrary quantile functions as its arguments

  #generate a bivariate correlated normal sample
  if (length(rho)==1){
  for (t in 1:length(rho)){

However, both fx and fy may require their own parameters. For instance, when fx=qchisq or when fy=qgamma. As a default solution, in my implementation, I used

fx=function(x) qchisq(x,df=3)


fy=function(x) qgamma(x,scale=.2)

but this is quite time consuming.

For instance,

> rhos=seq(-1,1,.01)
> system.time(trancor<-etacor(rho=rhos,fx=qlnorm,fy=qexp))
utilisateur     système      écoulé 
      0.834       0.001       0.834 


> system.time(trancor<-etacor(rho=rhos,fx=qlnorm,fy=function(x) qchisq(x,df=3)))
utilisateur     système      écoulé 
      8.673       0.006       8.675 


An illustration of my comment above:

etacor1 <- function(rho = 0,
                    nsim = 1e4,
                    fx = qnorm,
                    fy = qnorm,
                    fx.args = formals(fx),
                    fy.args = formals(fy)){
    #generate a bivariate correlated normal sample
    x1 <- rnorm(nsim)
    x2 <- rnorm(nsim)

    fx.arg1 <- names(formals(fx))[1]
    fy.arg1 <- names(formals(fy))[1]

    if (length(rho) == 1){
        y <- pnorm(cbind(x1, rho * x1 + sqrt((1 - rho^2)) * x2))
        fx.args[[fx.arg1]] <- y[,1]
        fy.args[[fy.arg1]] <- y[,2]

    coeur <- rho
    rho2 <- sqrt(1 - rho^2)

    for (t in 1:length(rho)){
        y <- pnorm(cbind(x1,rho[t]*x1+rho2[t]*x2))
        fx.args[[fx.arg1]] <- y[,1]
        fy.args[[fy.arg1]] <- y[,2]
        coeur[t] <- cor(do.call(fx,as.list(fx.args)),


I am displeased with the apparent necessity of as.list. I feel like I should know why that is, but it is escaping me at the moment.

In using this function, it should not be necessary to pass in all arguments, but you do need to make sure any list you pass to fx.args or fy.args is named.

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