Calculating p-value using R

i want to calculate the p-value of a statistics test using R. I'm aware of the existing function t.test(x) but what i want to do is to determine the p value through monte carlo simulations. Therefore i defined this code:

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)))) # random samples of test statistics
t <- mean(x) - mean(y) #teststatistics t
p.value <- 2 * (1- pnorm(mean(s)))   #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"))
}


When running

> set.seed(0)
> mean.test(rnorm(1000,3,2),rnorm(2000,4,3))


this is supposed to return

     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


this return this though:

        mean test
data:  c(rnorm(1000, 3, 2), rnorm(2000, 4, 3))
difference in means = -1.0967, p-value = 0.9999
alternative hypothesis: true difference in means is not equal to 0


What's the error ?

• A suggestion. Reconsider the definition of p-value. It is the probability of getting data at least as extreme as the observed data, while assuming that the null hypothesis is true. This means you need to define the way in which you are going to determine if the data in each sample* is more extreme than the data observed.... * Note that there are different ways to sample: permutation test, monte carlo, bootstrap. It may be helpful to look at the calculation for hypothesis tests that use bootstrap or monte carlo. – Sal Mangiafico Jul 15 '17 at 19:50
• I have edited and reposted my question, maybe it's more precise now – Manuel Jul 15 '17 at 19:59
• If your question is "how do I rewrite my R code correctly" it's likely to be off topic; you'd do better to discuss what you're doing (it looks like bootstrapping rather than simulation) and your proposed steps without necessarily discussing R (i.e. explain in words/pseudocode). These lines t <- mean(x) - mean(y) #teststatistics t and p.value <- 2 * (1- pnorm(mean(s))) #try to calculate p value ... confuse me. I don't get how this is supposed to give you a p-value, since the sample value (t) doesn't come into the calculation of the p-value. What are you trying to do there? – Glen_b -Reinstate Monica Jul 15 '17 at 22:57
• Here's what I'm thinking. Again, think about the definition of p-value. You have your observed data, Group A, let's say has a mean of 6. And Group B, let's say has a mean of 8. The difference between the means is 2. Then you have the combined group, AB. If the null hypothesis is true, it should be common to pull two samples from AB and that the difference in their means be 2 or greater or –2 or less. So this is what you do in a bunch of trials. If the means from the trial are 2 or greater or –2 or less, this counts as one point for "As extreme". After all your trials, you simply (cont) – Sal Mangiafico Jul 16 '17 at 13:00
• (..cont.) divide the points for "As extreme" by the total number of trials. This is your p value. It is literally the probability of getting data as extreme as the observed data while assuming the null hypothesis is true. That being said, please don't except my explanation as a completely justified statistical test. Please read up on permutation tests and different sampling methods (with replacement, without replacement, bootstrap, monte carlo). Comparing what you are doing to various functions in R that use monte carlo, or the results from permutation tests, may be helpful. – Sal Mangiafico Jul 16 '17 at 13:05

Please see the caveats in my comments to the question. This is just a conceptual example. It is not meant to be presented as a statistically sound test.

The following code will compare the means of two samples, and produce a p-value by resampling. Note that if the samples are normally distributed and there are sufficient replications, that the p-value is very close to that of the t.test function.

The heart of the function is very simple. I added some code to produce a nicer output.

Mean.test = function(X, Y, r=1000, digits=4){
XY = c(X, Y)
Diff = abs(mean(X) - mean(Y))
Count = 0
for(i in 1:r){
S1 = sample(XY, size=length(X), replace=TRUE)
S2 = sample(XY, size=length(Y), replace=TRUE)
Diff.s = abs(mean(S1) - mean(S2))
if(Diff.s >= Diff){Count=Count+1}
}
P.value = Count/r
Z=data.frame(
Statistic= rep("Boogida", 3),
Mean    = rep(NA, 3),
SD      = rep(NA, 3),
n       = rep(NA, 3),
stringsAsFactors=FALSE)

Z[1,] = c("Group 1", signif(mean(X),digits),signif(sd(X),digits),
signif(length(X),digits))
Z[2,] = c("Group 2", signif(mean(Y), digits), signif(sd(Y), digits),
signif(length(Y),digits))
Z[3,] = c("Difference", signif(abs(mean(X) - mean(Y)), digits), NA, NA)

colnames(Z)[1]=""

U = data.frame(
Statistic = rep("Boogida", 1),
p.value    = rep(NA, 1),
stringsAsFactors=FALSE)

colnames(U)[1]=""

U[1,] = c("p-value", signif(P.value, digits))

W = list(Summary=Z,
Result=U)

return(W)
}

A = rnorm(n=19, mean=6, sd=3)
B = rnorm(n=21, mean=8, sd=3)

Mean.test(A, B, r= 10000)

t.test(A, B)