inst/doc/monte-carlo.R

In robust2sls: Outlier Robust Two-Stage Least Squares Inference and Testing

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----include = FALSE, echo = FALSE--------------------------------------------
Sys.setenv(LANGUAGE="en")

## ----eval = FALSE-------------------------------------------------------------
# utils::vignette("overview", package = "robust2sls")

## -----------------------------------------------------------------------------
library(robust2sls)
p <- generate_param(dx1 = 3, dx2 = 2, dz2 = 3, intercept = TRUE, seed = 10)

## -----------------------------------------------------------------------------
library(parallel)
library(parallelly)
library(doParallel)
library(doFuture)

ncores <- 2
cl <- makeClusterPSOCK(ncores)
f <- y ~ x2 + x3 + x4 + x5 | x2 + x3 + z4 + z5 + z6

tryCatch({
  # export libraries to all workers in the cluster
  invisible(clusterCall(cl = cl, function(x) .libPaths(x), .libPaths()))

  registerDoParallel(cl)
  sim1 <- mc_grid(M = 100, n = c(100, 1000), seed = 42, parameters = p,
                  formula = f, ref_dist = "normal",
                  sign_level = 0.05, initial_est = "robustified", iterations = 0,
                  shuffle = FALSE, shuffle_seed = 42, split = 0.5)

  # Next, use doFuture for the parallel loop. Both implementations should
  # yield the same result.
  registerDoFuture()
  plan(cluster, workers = cl)
  sim2 <- mc_grid(M = 100, n = c(100, 1000), seed = 42, parameters = p,
                  formula = f, ref_dist = "normal",
                  sign_level = 0.05, initial_est = "robustified", iterations = 0,
                  shuffle = FALSE, shuffle_seed = 42, split = 0.5)

  # check identical results
  identical(sim1, sim2)
}, finally = {
  plan(sequential)
  stopCluster(cl)
})

## -----------------------------------------------------------------------------
library(doFuture)
registerDoFuture()
plan(sequential)
sim3 <- mc_grid(M = 100, n = c(100, 1000), seed = 42, parameters = p, 
               formula = f, ref_dist = "normal", 
               sign_level = 0.05, initial_est = "robustified", iterations = 0,
               shuffle = FALSE, shuffle_seed = 42, split = 0.5)

# check identical results
identical(sim1, sim3)