Nested for loop in R - r

Question

I wrote the following code, and I need to repeat this for 100 times, and I know I need to user another for loop, but I don't know how to do it. Here is the code:
mean <- c(5,5,10,10,5,5,5)
x <- NULL
u <- NULL
delta1 <- NULL
w1 <- NULL
for (i in 1:7 ) {
x[i] <- rexp(1, rate = mean[i])
u[i] <- (1/1.2)*runif(1, min=0, max=1)
y1 <- min(x,u)
if (y1 == min(x)) {
delta1 <- 1
}
else {
delta1 <- 0
}
if (delta1 == 0)
{
w1 <- NULL
}
else {
if(y1== x[[1]])
{
w1 <- "x1"
}
}
}
output <- cbind(delta1,w1)
output
I want the final output to be 100 rows* 3 columns matrix representing run number, delta1, and w1.
Any thought will be truly appreciated.

Answer 1

Here's what I gather you're trying to achieve from your code:
Given two vectors drawn from different distributions (Exponential and Uniform)
Find out which distribution the smallest number comes from
Repeat this 100 times.
Theres a couple of problems with your code if you want to achieve this, so here's a cleaned up example:
rates <- c(5, 5, 10, 10, 5, 5, 5) # 'mean' is an inbuilt function
# Initialise the output data frame:
output <- data.frame(number=rep(0, 100), delta1=rep(1, 100), w1=rep("x1", 100))
for (i in 1:100) {
# Generating u doesn't require a for loop. Additionally, can bring in
# the (1/1.2) out the front.
u <- runif(7, min=0, max=5/6)
# Generating x doesn't need a loop either. It's better to use apply functions
# when you can!
x <- sapply(rates, function(x) { rexp(1, rate=x) })
y1 <- min(x, u)
# Now we can store the output
output[i, "number"] <- y1
# Two things here:
# 1) use all.equal instead of == to compare floating point numbers
# 2) We initialised the data frame to assume they always came from x.
# So we only need to overwrite it where it comes from u.
if (isTRUE(all.equal(y1, min(u)))) {
output[i, "delta1"] <- 0
output[i, "w1"] <- NA # Can't use NULL in a character vector.
}
}
output

Answer 2

Here's an alternative, more efficient approach with replicate:
Mean <- c(5, 5, 10, 10, 5, 5, 5)
n <- 100 # number of runs
res <- t(replicate(n, {
x <- rexp(n = length(Mean), rate = Mean)
u <- runif(n = length(Mean), min = 0, max = 1/1.2)
mx <- min(x)
delta1 <- mx <= min(u)
w1 <- delta1 & mx == x[1]
c(delta1, w1)
}))
output <- data.frame(run = seq.int(n), delta1 = as.integer(res[ , 1]),
w1 = c(NA, "x1")[res[ , 2] + 1])
The result:
head(output)
# run delta1 w1
# 1 1 1 <NA>
# 2 2 1 <NA>
# 3 3 1 <NA>
# 4 4 1 x1
# 5 5 1 <NA>
# 6 6 0 <NA>