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# Monte Carlo Simulation
# Taken from "Eonometrics in R" by Grant V. Farnsworth
#
# The following block of code creates a randomly distributed data X with
# 25 members. It then creates a y vector that is conditionally distributed
# as
# y = 2 + 3x + e
# It then does a regression of x on y and stores the slope coefficient.
# The generation of y and calculation of the slope coefficient are
# repeated 600 times. The mean and sample variance of the slope coefficient
# are then calculated. A comparison of the sample variance of the estimated
# coefficient with the analytic solution for the variance of the slope
# coefficient is then possible.
init_func <- function() {
set.seed(123) # comment this out if you don't want deterministic results
x <<- rnorm(25, mean=2, sd=1)
}
comp_func <- function(...) {
y <- rnorm(25, mean=(3*x+2), sd=1)
beta <- lm(y~x)
beta$coef[2]
}
if (TRUE) {
cat('Parallel version\n')
library(nws)
s <- sleigh(workerCount=3)
eachWorker(s, init_func)
eo = list(chunkSize = 200)
temp_results <- eachElem(s, comp_func, list(1:600), eo=eo)
A = unlist(temp_results)
Abar <- mean(A)
varA <- var(A)
cat(Abar, varA, '\n')
}
if (TRUE) {
cat('Sequential version\n')
init_func()
A <- vector(length=600)
for (i in 1:600) {
A[i] <- comp_func(i)
}
Abar <- mean(A)
varA <- var(A)
cat(Abar, varA, '\n')
}
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