Randomization macro - r

Question

I try to measure the phylogenetic signal in two variables, a discrete and a continuous one. To do so, I use the δ-statistic (Borges et al 2018) and the K-statistic (Blomberg 2003), respectively. I have a tree, and two vectors corresponding to my variables. The line of code for these two statistics are the following:
1) delta(vector, tree, lambda0, se, sim, thin, burn)
2) phylosig(tree, vector, method = "K")
I get a single value each time. But I would like to randomize my vectors to test the significance of the orignal values. I would like to do 1000 repetitions and then proceed to a simple test of significance but, as I am a new R user, I have no idea how to do it. I think of something like this:
For δ:
%first repetition
random_vector <- sample(vector)
random_delta <- delta(vector, tree, lambda0, se, sim, thin, burn)
write.xlsx(random_delta, path)
%second repetition
random_vector <- sample(random_vector)
random_delta <- delta(vector, tree, lambda0, se, sim, thin, burn)
write.xlsx(random_delta, path, append = TRUE)
And on, and on, until 1000 δ-statistics stored in a single .xlsx, ready to be used in a test.
For K, I guess it is a bit different since it is not a vector anymore but a table with two columns (species, values):
%first repetition
random_vector <- sample(vector)
names(random_vector) <- tree$tip.label
random_K <- phylosig(tree, vector, method = "K")
write.xlsx(random_K, path)
%second rep
random_vector <- sample(random_vector)
names(random_vector) <- tree$tip.label
random_K <- phylosig(tree, vector, method = "K")
write.xlsx(random_delta, path, append = TRUE)
Etc.
I thought of that, but maybe someone has another idea. Either way, I am in.
I hope I have made myself clear.
EDIT:
Thank you for your answers. Yes, a loop is totally what I need. And yes, write all the values in one vector seems more approriate, I agree with you.
With the δ-statistic, the phylogenetic signal is all the more important in the data as δ is high. But what is high? That is why I want to do 1000 iterations, to calcultate the p-value and demonstrate if the original value is 'exceptional' or not. Same with K, which is comprised between 0 and 1 in the presence of a phylogenetic signal.
Here is a more explicite example:
> library(phytools)
> trait_delta <- c(2,1,3,1,1,3,1,3,2,1,1,2,2,2,2,1,1,3,1,1)
> trait_K <- c(2,1,3,1,1,3,1,3,2,1,1,2,2,2,2,1,1,3,1,1)
> set.seed(25)
> ns <- 20
> tree <- rtree(ns)
> plot(tree)
>
> #delta
> lambda0 <- 0.1
> se <- 0.5
> sim <- 10000
> thin <- 10
> burn <- 100
>
> delta <- delta(trait_delta,tree,lambda0,se,sim,thin,burn)
> rand_values_delta <- c(print(delta))
>
> #to loop 999 times
> rand_trait_delta <- sample(trait_delta)
> rand_delta <- delta(rand_trait_delta,tree,lambda0,se,sim,thin,burn)
> rand_values_delta <- append(rand_values_delta, print(rand_delta), after =
> length(rand_values_delta+1))
>
>
> #K
> names(trait_K) <- tree$tip.label
> K <- phylosig(tree, trait_K, method = "K")
> rand_values_K <- c(K)
>
> #to loop 999 times
> rand_trait_K <- sample(trait_K)
> names(rand_trait_K) <- tree$tip.label
> rand_K <- phylosig(tree, rand_trait_K, method = "K")
> rand_values_K <- append(rand_values_K, rand_K, after =
> length(rand_values_K+1))

Answer 1

I'm still a bit hazy on exactly what you are doing, but hopefully this points you in the right direction:
n <- 10000 # run this many iterations
results <- rep(NA, n) # create an empty vector to store all the values
for (i in 1:n){
# get the random vector for this iteration
random_vector <- sample(vector)
# save the value you output
results[i] <- delta(vector, tree, lambda0, se, sim, thin, burn)
}
# write the single vector to file
write.xlsx(results, path)
This is untested as I don't know what vector or path are, I just used them from your code