[R] Calculating conditional mean of large series of experiments

[Anthony28]

I need to repeat an experiment 1000 times. Each experiment involves randomly
selecting one ball each from two separate bags. Each bag contains 10 balls,
numbered 1, 2, 3, ... , 10. So the probability of selecting any one pair of
balls is equal to all others.

For each experiment, what I need to do is assign a variable A which
represents the maximum number out of the two balls selected; and a variable
B which represents the minimum number out of the two balls. So if one
experiment yielded (3, 9), then A = 9, B = 3.

I ultimately require the mean value of A given a particular value of B. As
I'm a total novice when it comes to R (as you'll see below), all I have got
so far is this:

output <- matrix(nrow=2, ncol=1000)

#I'm attempting to output a vector with 2 rows and 1000 columns. Each column
represents the outcome of one experiment. I don't even know if what I've
written above is allowed.

for (i in 1:1000){
ball1 <- sample(1:10,1)
ball2 <- sample(1:10,1)
A <- max(ball1, ball2)
B <- min(ball1, ball2)
output[i] <- matrix(nrow=2, ncol=1000, data=c(A, B))
}

# This gives me the message that "number of items to replace is not a
multiple of replacement length". I would really appreciate it if someone
could tell me how to do what I intended above properly. Also if anyone has
time, I would like to know how to filter down the (2 x 1000) matrix to
contain only those elements with a particular B-value so that I can then
calculate the conditional means of the A-values.


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