Accumulation curves in R (not in Vegan)

I’d like to create accumulation curves, specifically metric accumulation curves, using bootstrapping and for loops. I’m interested in sampling (with replacement) the total number of plots in my example dataset, starting at 1 and working up to the total number (n=1 … max n). Each will be sampled 1000 times.

I don’t believe a package, such as Vegan, will help with this, since I’m not looking for species accumulation curves but instead need to calculate metrics based on abundance data and a plant species’ coefficient of conservatism (please correct me if I’m wrong about this!).

My example dataset is a matrix, with plots, plant species names, abundance values, and c-values (coefficients of conservatism):

https://docs.google.com/spreadsheets/d/1v-93sV4ANUXpObVbtixTo2ZQjiOKemvQ_cfubZPq9L4/edit?usp=sharing

For each of the 1000 iterations for each nth sample, I need to build a matrix that will hold the 1000 iteration results that has species name, abundance, and the c-value and then eliminate any duplicate species from that sample. For each iteration, I must then calculate vegetation metrics. Its important that I don’t calculate the metric for the entire 1000 iterations, but for each individual iteration.

I will repeat for n+1 until max n. At the end, ideally, I will then input those results into a matrix of my final results, with rows being n … max n, and 1000 columns with calculated metrics for each of those 1000 iterations. I will then average across iterations, and then create an accumulation curve of my desired metric from those averages.

The code that I thought was useful is included below, with a different example data set, including the metrics that I’m interested in calculating.

https://docs.google.com/spreadsheets/d/1GcH2aq3qZzgTv2YkN-uMpnShblgsuKxAPYKH_mLbbh8/edit?usp=sharing

d<-Example2
d<-data.matrix(d)

MEANC<-function(x){
  mean(x, na.rm=TRUE)
}


FQI<-function(x){
  mean(x, na.rm=TRUE)*sqrt(sum(!is.na(x)))
}

RICH<-function(x){
  totalsprich<-sum(x)
  sum(x!=0, na.rm=TRUE)
}

shannon <- function(x){
  totalCov <- sum(x, na.rm=TRUE)
  (sum(x / totalCov * log(x / totalCov), na.rm=TRUE)) * -1

}

#for this particular example, the only two functions (metrics) that will work will be RICH and shannon

nrep<-1000
totalQuads<-nrow(d)

bootResultSD<-data.frame(matrix(nrow=nrep, ncol=totalQuads) )

bootResultMean<-data.frame(matrix(nrow=nrep, ncol=totalQuads)  )

for(j in 1:totalQuads){
  for(i in 1:nrep){

    bootIndex<-sample(1:totalQuads, j, replace=FALSE)

    bootSample<-d[bootIndex, na.rm=TRUE, drop=FALSE]

    VALUES<-apply(bootSample, 1, shannon)
    bootResultSD[i, j]<-sd(VALUES, na.rm=TRUE)
    bootResultMean[i, j]<-mean(VALUES, na.rm=TRUE)
  }
}

VALUES
bootResultSD
bootResultMean

meanDATA <- apply(bootResultMean, 2, mean, na.rm=TRUE)
meanDATASD <- apply(bootResultSD[-1], 2, mean, na.rm=TRUE)

The issue with what I’ve created from before is that it is calculating metrics on a per plot basis, instead of accumulating plots and re-calculating metrics based on each cumulative sample. Here is what I’ve come up with so far based off my code from above, but I don’t think this is what I need:

for(j in 1:totalQuads){
  for(i in 1:nrep){

    bootIndex<-sample(1:totalQuads, 10, replace=TRUE) 

    bootSample<-d[bootIndex, na.rm=TRUE, drop=FALSE]

    booted<-bootSample[!duplicated(bootSample[,2]),]

    bootResultSD[i, j]<-sd(booted, na.rm=TRUE)
    bootResultMean[i, j]<-mean(booted, na.rm=TRUE)

  }
}

I’m at a loss for how to proceed past this point. Thanks in advance!