Hello, R-Community! This is the first time writing to this group and indeed the first time using a mailing list, so please bear with me if I’ve done something wrong.
I have a large species x site matrix (89 x 4831) that I want to ordinate using metaMDS in the Vegan (2.0-5) package in R (2.15.2). If I run this data frame using the Jaccard index in two or more dimensions (k>1), the first run (run=0) has a relatively low stress value and the other 20 runs are much higher and have very low deviation. However, k=1 seems to work fine. Furthermore, a stress/scree plot reveals a pyramid-like shape, where the k=1 lowest stress value is low, increases rapidly for k=2 then decreases slowly as k increases. Dimensions Stress 1 0.1382185 2 0.1939509 3 0.1695375 4 0.155221 5 0.1406408 6 0.1294149 I’ve tried this with a small iteration of this data and this issue arises at k>2 rather than at k>1 as it is here. Anyway, this is the input and output: library(vegan) library(MASS) PSU <- read.table("PSU.txt", header = TRUE, sep = "") PSU.sp <- PSU[, 22:110] PSU.NMDS <- metaMDS(PSU.sp, k=4, zerodist = "add", distance = "jaccard") Square root transformation Wisconsin double standardization Zero dissimilarities changed into 0.0006657301 Run 0 stress 0.155221 Run 1 stress 0.2548103 Run 2 stress 0.255434 Run 3 stress 0.2551382 … (Up to run 20 where run 1 through run 20 have all very similar stress values.) Call: metaMDS(comm = PSU.sp, distance = "jaccard", k = 4, zerodist = "add") global Multidimensional Scaling using monoMDS Data: wisconsin(sqrt(PSU.sp)) Distance: jaccard Dimensions: 4 Stress: 0.155221 Stress type 1, weak ties No convergent solutions - best solution after 20 tries Scaling: centring, PC rotation, halfchange scaling Species: expanded scores based on ‘wisconsin(sqrt(PSU.sp))’ Now, again, with k=1 this does not happen – the solution looks like any other regular NMDS run. There are no blank values in the data as they are all numbers between 0 and 100 corresponding to % cover, and every row and column sum is greater than 0. There are many sites with the same species configurations, hence the zerodist, but omitting this makes no difference to the problem at hand. The NMDS works fine if I use a subset of the data, but I have not subsetted and tested all of it. Other metric (Euclidean) and nonmetric (Bray) dissimilarity indices result in the same effect. I’ve chosen k=4 here because of the (marginal) elbow in the stress plot, but the data itself actually looks pretty good at any k value. Even though the output is reasonable, I am concerned that hitting the best solution by a large amount on the first run means something is messing up, and this concern is amplified by the strange pyramid shaped stress plot. Because metaMDS uses random starts, I don't see how this output is possible. I've scoured the help files and archives of this list and I am really now at a loss to explain this. Thank you in advance for your time and consideration! Ewan _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology