How to create a more efficient simulation loop for Monte Carlo in R

Question

The purpose of this exercise is to create a population distribution of nutrient intake values. There were repeated measures in the earlier data, these have been removed so each

Answer 1

Here is an approach that addresses the 2 biggest speed issues:

1. Instead of looping over observations(i), we compute them all at once.
2. Instead of looping over MC replications (j), we use replicate, which is a simplified apply meant for this purpose.

First we load the dataset and define a function for what you were doing.

Male.Distrib = read.table('MaleDistrib.txt', check.names=F)

getMC <- function(df, Lambda.Value=0.4, Male.Resid.Var=12.1029420429778) {
  u2        <- df$stddev_u2 * rnorm(nrow(df), mean = 0, sd = 1)
  mc_bca    <- df$FixedEff + u2
  temp      <- Lambda.Value*mc_bca+1
  ginv_a    <- temp^(1/Lambda.Value)
  d2ginv_a  <- max(0,(1-Lambda.Value)*temp^(1/Lambda.Value-2))
  mc_amount <- ginv_a + d2ginv_a * Male.Resid.Var / 2
  mc_amount
}

Then we replicate it a bunch of times.

> replicate(10, getMC(Male.Distrib))
         [,1]      [,2]     [,3]     [,4]      [,5]     [,6]     [,7]     [,8]     [,9]    [,10]
[1,] 36.72374 44.491777 55.19637 23.53442 23.260609 49.56022 31.90657 25.26383 25.31197 20.58857
[2,] 29.56115 18.593496 57.84550 22.01581 22.906528 22.15470 29.38923 51.38825 13.45865 21.47531
[3,] 61.27075 10.140378 75.64172 28.10286  9.652907 49.25729 23.82104 31.77349 16.24840 78.02267
[4,] 49.42798 22.326136 33.87446 14.00084 25.107143 25.75241 30.20490 33.14770 62.86563 27.33652
[5,] 53.45546  9.673162 22.66676 38.76392 30.786100 23.42267 28.40211 35.95015 43.75506 58.83676
[6,] 34.72440 23.786004 63.57919  8.08238 12.636745 34.11844 14.88339 21.93766 44.53451 51.12331

Then you can reformat, add IDs, etc., but this is the idea for the main computational part. Good luck!