R : Calculating p-value using simulations - r

Question

I wrote this code to run a test statistic on two randomly distributed observations x and y
mean.test <- function(x, y, B=10000,
alternative=c("two.sided","less","greater"))
{
p.value <- 0
alternative <- match.arg(alternative)
s <- replicate(B, (mean(sample(c(x,y), B, replace=TRUE))-mean(sample(c(x,y), B, replace=TRUE))))
t <- mean(x) - mean(y)
p.value <- 2*(1- pnorm(abs(quantile(T,0.01)), mean = 0, sd = 1, lower.tail =
TRUE, log.p = FALSE)) #try to calculate p value
data.name <- deparse(substitute(c(x,y)))
names(t) <- "difference in means"
zero <- 0
names(zero) <- "difference in means"
return(structure(list(statistic = t, p.value = p.value,
method = "mean test", data.name = data.name,
observed = c(x,y), alternative = alternative,
null.value = zero),
class = "htest"))
}
the code uses a Monte-Carlo simulations to generate the distribution function of the test statistic mean(x) - mean(y) and then calculates the p-value, but apparently i miss defined this p-value because for :
> set.seed(0)
> mean.test(rnorm(1000,3,2),rnorm(2000,4,3))
the output should look like:
mean test
data: c(rnorm(1000, 3, 2), rnorm(2000, 4, 3))
difference in means = -1.0967, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
but i got this instead:
mean test
data: c(rnorm(1000, 3, 2), rnorm(2000, 4, 3))
difference in means = -1.0967, p-value = 0.8087
alternative hypothesis: true difference in means is not equal to 0
can someone explain the bug to me ?

Answer 1

As far as I can tell, your code has numerous mistakes and errors in it:
quantile(T, 0.01) - here T == TRUE, so you're calculating the quantile of 1.
The object s is never used.
mean(sample(c(x,y), B, replace=TRUE)) What are you trying to do here? The c() function combines x and y. Sampling makes no sense since you don't know what population they come from
When you calculate the test statistic t, it should depend on the variance (and sample size).