rarecurve() plotted with Standard Error

Question

Does rarecurve() (vegan) accept standard error for plotting?

If so, how can I plot such a curve?

I am following a classical script for this, with the BCI dataset:

S <- specnumber(BCI)
(raremax <- min(rowSums(BCI)))
Srare <- rarefy(BCI, raremax)
plot(S, Srare, xlab = "Observed No. of Species", ylab = "Rarefied No. of Species")
abline(0, 1)
rarecurve(BCI, step = 20, sample = raremax, col = "blue", cex = 0.6)

Statistically speaking, facilitating a function as this one would be helpful to most vegan users.

Thank you! André

That plot is already very cluttered. How would putting in error band be helpful? — IRTFM, Jun 05 '15 at 00:09
Well, first of all, statistically speaking it is somewhat important to show how much variation you find within each sample. In other not-so clustered datasets it may become more feasible to plot. — André Soares, Jun 05 '15 at 09:19

score 1 · Answer 1 · answered Jun 09 '15 at 07:20

rarecurve does not give you SE. The reason is obvious and already given to you: there is enough clutter without extra curves. If you really want to do this, you must do it manually. That is not too complicated, because rarefy function accepts a vector sample sizes and gives you all the numbers you need. The following draws a basic plot using one site of Barro Colorado data set:

library(vegan)
data(BCI)
sum(BCI[1,]) # site 1, 448 tree stems
N <- seq(2, 448, by=8)
S <- rarefy(BCI[1,], N, se = TRUE)
plot(N, S[1,], type="l", lwd=3)
lines(N, S[1,] + 2*S[2,]) ## 2*SE is good enough for 95% CI
lines(N, S[1,] - 2*S[2,])

Statistically speaking, this gives you only the error caused by the subsampling process assuming that the observed data have no random variation. To me this makes little sense, and I find the rarefaction SE's misleading and meaningless. That does not stop me providing them in vegan.

rarecurve() plotted with Standard Error

1 Answers1