similar to: gam help (really a vegan question)

I have a general question about using thin plate splines in the surf and ordisurf routines. My rudimentary knowledge of a gam is that with each predictive variable there is a different smooth for each one and then they are added together with no real interaction term (because they don't handle this well?). Now, If I have two variables that have a high D^2 score and a low GCV score (I am

Dear r-helpers, I just read in an article by Virtanen et al. (2006) where vegetation-environment relationships are studied by fitting smoothed surfaces on an NMDS ordination using GAMs (Wood 2000). The authors describe, that they used R? as goodness-of-fit statistic, which they compare to the R? of fitted vectors. Calculations were carried out using the package vegan (Oksanen). I know that I can

I'm having trouble with the ordisurf function in the vegan package. I have created an ordination plot (cmdscale) of 60 samples based on Bray-Curtis dissimilarities, and would like to overlay various soil edaphic characteristics as possible clues to the clustering I observe in my plot. However, I find that ordisurf creates a surface on the plot that is a perfect, even gradient - and

I'm getting lots of grief from reviewers about figures generated with the envfit function in the Vegan package. Has anyone else struggled to effectively explain this analysis? If so, can you share any helpful tips? The most recent comment I've gotten back: "What this shows is which NMDS axis separates the communities, not the relationship between the edaphic factor and the

Dear r-Community, Step1: I would like to calculate a NMDS (package vegan, function metaMDS) with species data. Step2: Then I want to plot environmental variables over it, using function envfit. The Problem: One of these environmental variables is cos(EXPOSURE). But for flat releves there is no exposure. The value is missing and I can't call it 0 as 0 stands for east and west. Therefore I

I am using the function metaMDS with jaccard distances to ordinate a set of constituent by site matrix. I can post this data if it would be helpful, but it is large to include in an email. I can also provide reproducable code if necessary. I would like to get an R^2 value for the axes of the ordination configuration that I get with metaMDS in the vegan package is there a way to do this- is it

Hello, I am using vegan to do an NMDS plot and I would like to suppress the labels for the loading vectors. Is this possible? Alternatively, how can I avoid overlap? Many thanks for the help. Example code: #perform NMDS using metaMDS() function spe.nmds<-metaMDS(data, distance='bray',k=2 , engine = "isoMDS", autotransform=F, trymax=1000) #calculate the loading (i.e.,

Hi, I've been using R for a while now but I've got a problem with metaMDS (in the vegan package) that I can't quite figure out. I have a set of proportion data (from 0-1, rows sum to 1) that I apply metaMDS to using the command: nMDS.set=metaMDS(sqrt(test.set),distance="euclidean",k=3,zerodist="add",autotransform=FALSE) I am using a squared-chord distance

The design decision in metaMDS says that it uses: Minchin, P.R. (1987) An evaluation of relative robustness of techniques for ecological ordinations. Vegetatio 71, 145-156. This is the paper that I found by the same name. Is this the correct reference? Minchin, Peter R.1987. An Evaluation of the Relative Robustness of Techniques for Ecological Ordination. Vegetatio. Vol. 69, No. 1/3: 89-107.

Hi, I'm trying to use metaMDS with a dissimilarity matrix of angles, not Bray-Curtis, and I wanted to know if there is an in-built function to produce a plot of stress values against dimensions, that could be used to determine the 'true' dimension of the solution. The number of objects is only a lowly 8 so any solution higher than 2-dimensional is not likely to be interpretable,

Dear List, This is Elaine, a postgraduate studying in bird distributions in East Asia. I want to calculate Simpson dissimilarity index, based on a presence/absence matrix of bird species in islands in East Asia. (matrix row: 36 islands/matrix column: species ID) (R package vegan to make NMDS and R package betapart) In most papers using vegan for NMDS and betapart for dissimilarity

I'm pretty new to R and would appreciate some help interpreting the output of a function that was recommended to me. I've used the *envfit *function in the vegan package to plot vectors of four climate variables onto a species matrix ordination. The output indicates that only a single variable (mean_temp) is significant: ***VECTORS NMDS1 NMDS2 r2

Hello, I can draw a basic stress plot for NMDS with the following code in package Vegan. > stressplot(parth.mds, parth.dis) When I try to specify the line and point types, it gives me error message. > stressplot(parth.mds, parth.dis, pch=1, p.col="gray", lwd=2, l.col="red") Error in plot.xy(xy, type, ...) : invalid plot type In the above code, if I removed line type,

Dear all, I'm using R2.8 version, and am trying to do NMDS and calculate other diversity indices in vegan package. The problem is that it works with a small set of data (43 X 23; row by column), but the following error message comes up with a larger data set (43 X 104) (it seems not large to me at all). I made it sure that all data are of numeric type as required. >gh1.H=diversity(gh1)

Dr. Stevens, Hi, my name is Trey Scott, and I'm a grad student of Brian McCarthy's. He referred me to you because of your expertise in handling complex R problems. We were hoping you could help us solve a nagging problem that is prohibiting me from producing graphicl output. Here is a simple mock-up of the matrix I'm using a b c d e f 1i 1 4

Dear R-helpers, I am running metaMDS in the vegan package, which uses isoMDS in MASS, to perform Nonmetric Multidimentional Scaling (NMDS). I have seen some authors report a p-value for the NMDS ordination based on randomization of the dataset. As I understand it this is meant to compare the stress in your dataset to multiple runs of randomized data. I do not see a way to perform such a test in

I've been unable to find a R package that provides the means of performing Clarke & Ainsworth's BIO-ENV procedure or something comparable. Briefly, they describe a method for comparing two separate sample ordinations, one from species data and the second from environmental data. The analysis includes selection of the 'best' subset of environmental variables for explaining

Hi R-experts, I am using the envfit function over an ordination of floristic data. The problem is that every time that I run it changes the results. Sometimes dramatically, selecting variables that the first time were not significant. I do not get what could be the problem or if is normal given the permutations are different. # the NMDS ordination gap_flor_NMDS_chord <- metaMDS(gaps_flor,

Dear All, I am using a Procrustes analysis to compare two NMDS ordinations for the same set of sites. One ordination is based on fish data, the other is based on invertebrate data. Ordinations were derived using metaMDS() from the {vegan} library as follows: fish.mds<-metaMDS(fish.data, distance="bray", k=3, trymax=100, wascores=TRUE, trace=TRUE, zero="add")

Hi, I'm using nmds command (library vegan) to analyze some fishing data. I'd like to plot not only points, but also the names of species and stations in a specified position. I used the command text(nmds$points[,1], nmds $points[,2],labels=row.names(nmds $points),pos=3,cex=0.5) But the labels are sometimes overlapped. Is there any way to use identify, or a similar command, to plot the