## Annotated (##) code to run the simple random sampling null model
## (Model 1) in the R statistical package (http://www.r-project.org/)
##
## Read presence (1) absence (0) matrix of species occurrence, 
## with species in rows and samples in columns 
## (in the current case, samples are islands). 
## One row is reserved for headers. 
newdata <- read.csv("datafile.csv") 
## Create vector of sample totals: total number of species to be drawn. 
SpeciesSample <- colSums(newdata) 
## Display vector of species totals by sample (island). 
SpeciesSample 
## Set the number of iterations: number of times to conduct the 
## sampling. 
Iterations <- 1000 
## Create a vector that stores the number of endemics drawn in 
## iteration. 
HistoResults <- rep(0,Iterations) 
## Create a matrix with rows for the SpeciesSamples, and columns for 
## the mean (1), 95% low (2) and 95% high (3) confidence intervals, and 
## the observed species counts (4). 
Summ <- matrix(0,nrow=length(SpeciesSample),ncol=4) 
## Create a vector of species occurrences. The "1"s represent endemics 
## in the system. This works for abundance, presence-absence and 
## archipelago data in the same way. 
weights <- rowSums(newdata) 
weights 
## Begin iterations to resample each sample (island) in the matrix. 
## ----------------------------------------------------------------## 
for (j in 1:length(SpeciesSample)) { ## loop for each sample. 
## FracEndemic is used for counts, but can rescale for 
## proportions. 
FracEndemic <- 0 
## Sampling without replacement, draw the appropriate 
## number of species, using weights to scale probabilities. 
for (i in 1:Iterations) { ## loop for each iteration. 
RandomDraw <- sample(weights, size=SpeciesSample[j], 
replace=F, prob=weights) 
## Evaluate inequality to tally up the 1's (endemics). 
FracEndemic <- sum(RandomDraw == 1) 
## Arcsine transformation of endemic proportions. 
FracEndemic <- asin(FracEndemic/SpeciesSample[j]) 
## Store result and move to next iteration. 
HistoResults[i] <- FracEndemic 
} Endemism in the Hawaiian biota - 22 
Summ[j,] <- c(mean(HistoResults),quantile(HistoResults, 
c(0.025,0.975)),0) 
} 
## Loop through stored results for each sample and iteration to extract 
## summary data. 
for (j in 1:ncol(newdata)) Summ[j,4] <- 0 
## Extract overall means and confidence intervals. 
endemics <- rowSums(newdata) 
for (j in 1:ncol(newdata)) { 
for (i in 1:nrow(newdata)) { 
if(newdata[i,j] == 1 & (endemics[i] == 1)) 
Summ[j,4] <- Summ[j,4] + 1.0 } } 
##------------------------------------------------------------------## 
## Extract column and row names to print into output matrix. 
newdata <- data.frame(newdata) 
ISLE <- names(newdata) 
SpeciesSample1 <- data.frame(SpeciesSample) 
row.names(SpeciesSample1) <- ISLE 
print(row.names(SpeciesSample1)) 
## Create summary matrix to store results. 
Summ1 <- matrix(data=Summ,nrow=length(ISLE),ncol=4, dimnames= 
list(ISLE, c("exp_asinprop", "a95low", "a95high", "endemics"))) 
## Display summary matrix. 
Summ1 
## Export summary table to comma delimited file. 
write.csv(Summ1, file = "C:/directory/file.csv") 
## end ##