Nicholas,
Your approach looks perfectly OK. However, rarefied sampling is normally done
*without* replacement.
It looks to me that a similar analysis could be done with vegan::specaccum
function. However, that uses sampling without replacement.
About your specific questions: you can use quantile() function in base R to get
desired confidence intervals. The CIs should be given as argument probs to
quantile(). For quantile function you should return a vector of richness values
instead of a table. You can get similar with table output as well, but you may
need to write that yourself.
It is excepted that the CI gets narrower as the subsample size increases. In
classic rarefaction sampling without replacement, there is only one "subsample"
of the original sample size and therefore there is no variance but you get the
observed data. As the subsampled proportion approaches 1, the number of
possible samples decreases and so does the variance of subsamples. With
replacement this may not happen as some units are there twice or more and some
are missing. Subsample of original size with replacement should contain 63.2%
of your original units. This also means that because you duplicate (or
multiplicate) some sampling units and omit some, you will systematically
underestimate richness when you subsample with replacement. This is why we
normally use subsampling without replacement. However, the larger subsamples
tend to become more similar, in particular with data set like you described.
This is necessary and correct.
This is the most annoying thing in CIs in rarefaction to me. The rarefaction
variation *only* describes the randomness of subsampling from a fixed sample.
It does not describe the variance of that fixed sample, or the real variance of
your observed richness. With real variability I mean the variance you observe
if you replicate your sampling in Nature and get completely new, independent
samples from the same real community. I am afraid people use those rarefaction
CIs and variances as measures of sample variability which is really misleading
-- they actually describe only the artifactual variance of subsampling from
fixed samples, when these fixed samples are regarded as error-free and to have
zero variance of species richness. Using these variances to infer differences
in species richness in Nature is wrong and misleading. I have tried to spell
out that warning in vegan manual pages, but people seem to ignore those parts
of the text.
cheers, Jari Oksanen
On 28/05/2014, at 20:05 PM, Nicholas J. Negovetich wrote:
> I have a question related to rarefaction of our samples. Unlike all of the
> examples of which I'm aware, I'm not working with 'sites', per se. Instead,
> I'm working with the parasites of snails. Snails are infected with 1
> parasite species, and each pond/stream can hold several parasite species.
> The difficulty comes when comparing parasite richness between sites (Pond A
> vs Pond B) and sampling effort (# snails collected) differs. Rarefaction
> provides a means to standardize effort on the lowest sample size so that we
> can test if parasite richness differs between sites.
>
> I'm familiar with the vegan package and how it performs rarefaction. But, I
> can't perform this type of analysis because (1.) 'uninfected' snails would be
> considered empty patches, and (2.) max richness will be 1 (only one parasite
> species can (usually) infect a snail). To counter this problem, I've tried
> to rarefy my samples using randomization methods. The script is below. In
> summary, I sampled (with replacement) my dataset and calculated the number of
> parasite species from each sample. I repeated this 100 times and calculated
> the mean richness for a given sample size. I did this for sample sizes 1-50
> (smallest sample size is 50 individuals).
>
> I have a few questions related this this analysis.
> 1. Does this appear correct?
>
> 2. How can I generate CI using this method? I normally calculate the 2.5%
> and 97.5% quantiles when performing randomization, but the nature of our data
> does not lend itself well to quantile calculations. Could I assume that my
> bootstrapped means follow a normal distribution? If not, then what would be
> the best method for generating confidence intervals?
>
> 3. This last one is the primary reason for this post. When performing this
> analysis on my real datasets, I've noticed a peculiar thing. In one sample,
> my variance of the bootstrapped samples converges to zero as sample size
> approaches the true sample size (this is for my sample of the smallest sample
> size). Relatedly, I've noticed some (but not all) of my CI narrowing as my
> sample size approaches the actual size. This suggests that I'm not doing
> something correctly, but I'm not sure how to correct it (or if I even need to
> correct it). The alternative for me is to scrap the rarefaction and perform
> an alternative analysis, such as randomizing the sites and comparing the
> actual difference in richness between sites to a null distribution of
> randomized differences. Thoughts?
>
> NN
>
>
> #Script below
> #Two sites: Site A = 100 snails; Site B = 50 snails
> #0 = uninfected; 1-4 = species 1-4
>
> A <- c(rep(0,56),rep(1,25),rep(2,15),rep(3,3),rep(4,1))
> B <- c(rep(0,29),rep(1,12),rep(2,7),rep(3,2))
>
> #Function to generate a random sample with replacement and calculate species
> richness
> bsS <- function(x,n=100){
> rs <- sample(x,size=n,replace=T)
> S <- sum(table(rs)[-1]>0)
> return(S)
> }
>
> #Function to rarefy my dataset; utilized bsS function
> #For sample sizes of 1:n, generate 'reps' number of bootstrapped richness
> values (bsS)
> #and save the mean richness for each sample.
> rrfy <- function(x,n=10,reps=100){
> smax <- sum(table(x)[-1]>0)
> s <- c()
> std <- c()
> low <- c()
> high <- c()
> for (i in 1:n){
> rs <- c()
> for(j in 1:reps){
> rs[j] <- bsS(x,i)
> }
> s[i] <- mean(rs)
> std[i] <- sd(rs)
> low[i] <- mean(rs)-1.96*(sd(rs)/sqrt(length(rs))) #I don't think this is
> correct
> high[i] <- mean(rs)+1.96*(sd(rs)/sqrt(length(rs))) #I don't think this is
> correct
> }
> plot(s~c(1:n),type='l',ylim=c(0,smax+1))
> lines(low~c(1:n),lty=2,col='red')
> lines(high~c(1:n),lty=2,col='red')
> return(data.frame(x=c(1:n),s,stdev=std))
> }
> rA <- rrfy(A,n=50)
> rB <- rrfy(B,n=50)
>
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