r,statistics,correlation,vegan

You want the anova() method that vegan provides for cca(), the function that does CCA in the package, if you want to test effects in a current model. See ?anova.cca for details and perhaps the by = "margin" option to test marginal terms. To do stepwise selection you have two...

See str(x) and you will find it: x$distances with(x, boxplot(distances~group)) hth...

orihull ignores groups with a single observation and thus doesn't populate the group centroids object with the centre of the convex hull. You could argue it should; I'll need to take this up with Jari and see if we can fix this. To solve the problem, you have to add...

The functions you are doing the actual plotting with have an argument select (at least text.cca() and points.cca(). select takes either a logical vector of length i indicating whether the ith thing should be plotted, or the (numeric) indices of the things to plot. The example would then become: ##...

Is this what you want? Load your data snippet: txt <- '"Group.1" "S.obs" "se.obs" "S.chao1" "se.chao1" "Cliona celata complex" 499.7143 59.32867 850.6860 65.16366 "Cliona viridis" 285.5000 51.68736 462.5465 45.57289 "Dysidea fragilis" 358.6667 61.03096 701.7499 73.82693 "Phorbas fictitius" 525.9167 24.66763 853.3261 57.73494' dat <- read.table(text = txt, header = TRUE) and...

There is no reproducible example. However, your usage is wrong. Function rarecurve needs input data of counts: it samples individuals from each sampling unit (row), and therefore you must have data on individuals. The error is caused by the use of wisconsin(lac.com.data): after that all rowSums(lac.com.data) will be 1, and...

This is guessing since there is nothing reproducible in your question. However, I can trigger similar error if I use univariate responses: adonis is intended for multivariate responses, and may not work with univariate responses. The adonis help page can be read with ?adonis, and it says that the left-hand-side...

From the technical point of view, this looks very much like t-test with unequal variances. Check the formula, and plug in your data. The R function t.test() expects raw data, but if you already have means and se's, it is easy to calculate statistics by hand. I have no idea...