Comparing original value with bootstrap

Question

I need to compare my value coming from a matrix bootstrap, with the original unique value, but I don't know exactly how to do this for my data. Here I have the example data

in order I need do:

• measuring the targeted variable on the original network and save it
• Shuffling my original data (correlation matrix) - randomly (100x)
• create the graph
• measuring the targeted variable for this random network
• Save this data end of the loop

Comparing the distribution of the random targeted variable with the original one saved at the begging. Then if the original data is outside of the 95% of the distribution of the random variable, this mean that it's significative (the random is different of the original variable).

    library(igraph)

###DADOSEXEMPLO##

cor_pequena <- 0.1
matcor[] <- ifelse(matcor < cor_pequena, 0, matcor)
matrixnetwork = graph.adjacency(matcor, mode = "undirected", 
                                weighted = TRUE, 
                                add.colnames = NULL, diag = FALSE)
degree(matrixnetwork)
#> [1] 3 4 3 3 1 2 2

plot(matrixnetwork)

Here I calculate the unique value for the graph, in this case for the measure betw

centr_betw(matrixnetwork, directed = FALSE, normalized = TRUE)$res Here I apply the permutation "change value in vertes" in conjunction with the specific function I want to apply, in this case Betw

set.seed(123)
bootcentr_Betw <- lapply(seq_len(Nperm), function(x){  
randomnet <- rewire(matrixnetwork, with=each_edge(0.5)) #rewire vertices with constant probability
  E(randomnet)$weight <- sample(E(matrixnetwork)$weight) #shuffle initial weights and assign 
 them randomly to edges
 return(centr_betw(randomnet)$res)
 })

Now I need to compare the original value with the bootstrap of the randomized graph.

Below I tried to plot the original unique value against bootstrap, replicating the code I got from elsewhere

# 4. Plote o valor observado em rela??o ? distribui??o de valores aleat?rios
 niveis<-row.names(bootstrength )
for(k in niveis)
{
  if(any(is.na(bootstrength [k,]) == TRUE))
  {
    print(c(k, "metrica tem NA"))
  } else {
    nome.arq<- paste("modularity",k,".png", sep="")
    png(filename= nome.arq, res= 300, height= 15, width=21, unit="cm")
    plot(density(bootstrength [k,]), main="Observed vs. 
 randomized",)
    abline(v=obs[k], col="red", lwd=2, xlab="")
    dev.off()
    print(k)
    nome.arq<- paste("Patefield_Null_mean_sd_",k,".txt", sep="")
    write.table(cbind(mean(bootstrength [k,]),sd(bootstrength [k,])), 
file=paste(nome.arq,sep=""), 
                sep=" ",row.names=TRUE,col.names=FALSE)
  }
}

I confess that I don't know where the file he generated is.

After that I tried to replicate the statistics I found here on the forum, but it didn't work.

#5. Estime o valor P (signific?ncia)
  significance=matrix(nrow=nrow(bootstrength),ncol=7)
  row.names(significance)=row.names(bootstrength)  
  colnames(significance)=c("p (rand <= obs)", "p (rand >= obs)", "p 
  (rand=obs)")

  signif.sup=function(x) sum(x>=x[1])/length(x)
  signif.inf=function(x) sum(x<=x[1])/length(x)
  signif.two=function(x) ifelse(min(x)*2>1,1,min(x)*2)

 significance[,1]=apply(bootstrength,1,signif.inf)
 significance[,2]=apply(bootstrength,1,signif.sup)
 significance[,3]=apply(significance[,-3],1,signif.two)

The first error that appears is when I run

 significancematrix(nrow=nrow(bootstrength),ncol=7)
Error in matrix(nrow = nrow(bootstrength), ncol = 7) : non-numeric matrix extension

Is there an easier way to simply calculate where my original value is in relation to the boostrap, and have a p-value?