similar to: label plotting on nmds diagram

Hi, I have used Vegan to construct an NMDS ordination plot. I plotted sites of three forest types with the site number in it. My reviewer has asked me to use different symbols for each of the forest types. Can anyone send me how I can do this in R in simple steps. I have used the options like ordiplot, sel and pl syntaxes that are not working for the question that I asked for. Best, Sinu --

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.,

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data

Hi! Thanks for providing great help in R-related statistics. Now, however I'm stuck. I'm not a statistics person but I was recommended to use R to perform a nmds plot and PerMANOVA of my dataset. Sample(treatment) in the columns and species (OTU) in the rows. I have 4 treatments (Ambient Temperature, Ambient temperature+Low pH, High temperature, High temperature+low pH), and I have 16

If you submit these lines, you end up with variable "vare.dis". I want to export vare.dis to csv. Stuck I am. library(vegan,logical.return = TRUE) #return=true verifies package is available library(MASS,logical.return=TRUE) #return=true verifies package is available data(varespec) #varespec is an example data file in the vegan package vare.dis <- vegdist(varespec)

Dear All, I was wondering whether anyone might be able to provide some advice with an nMDS / R problem. I?m trying to run nMDS on a dataset that contains many missing values and was wondering how I can account for the missing values when running nMDS? It seems as though the data are being grouped depending on where the zero values appear. Any suggestions greatly appreciated. Thank you very much

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

Hi Im currently trying to plot my NMDS data together with fitted variables (envfit funct) on an ordination plot. The plot function shows two displays="sites" and "sp". I was wondering how to plot it so that the sites come up as different points for different sites but the species come up as actual names? It looks a little busy at the moment with everything in. Sya -- View

Hi all, I used to connect internet via a proxy. Before update packages I wrote in R Sys.putenv("http_proxy"="http://proxy3.redegov.sp.gov.br:80/") Nevertheless the way the connection is done has changed. For example, in the browser the proxy is not indicated and I have to give an username and a password to have access to internet. I read the FAQ and the help for

Hi, I am currently working on some data and feel that NMDS would return an excellent result. With my current data set however I have been experiencing some problems and cannot carry out metaMDS. I have tried with a few smaller data sets which I created for practice sake and this has worked fine. I think it is the set up of my data set that is causing me trouble. I have 18 columns and 18 rows,

I have a data matrix that contains site data for paired infested and control plots. For example: ab ac ad af ag ah 1 .024 x x x x x 2 .9 x x x x x 3 1.5 x x x x x 4 2.3 x x x x x 5 1.0 x x x x x 1c .75 x x x x x 2c 2.1 x x x x x 3c 1.7 x x x x x 4c 10.2 x x x x

hello, subsequently to a NMDS analysis (performed with metaMDS or isoMDS) is it possible to rotate the axis through a varimax-rotation? Thanks in advance. Bernd Panassiti

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

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

Hello, I'm trying plot points in an NMDS according to a factor with two levels: fig<-ordiplot(canod.sol, type="none",cex.axis=0.9,cex.lab=0.1,pty="m",tck=-0.01) ordisymbol(fig, y = hab, factor = "habitat", rainbow = T,col = env, legend = F) This gets me part of the way - It produces a plot with blue triangles for the first factor level and red circles

Dear list fellows, I want to study time series and use R to analyse time series of fishing data from several species (landings and cpue) investigating the correlation between them and with environmental factors (water temperature, wind, etc.). Searching at Amazon I found three books with examples in R: Time Series Analysis: With Applications in R by Jonathan D. Cryer and Jonathan D. Cryer

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,

I'm not sure how you are incorporating time period into your data structure. Typically we are looking at plots or assemblages as the rows and taxa as the columns. Time period adds a third dimension that could be added as blocks of rows. For example, depending on the resolution of your data, one approach would be to have up to 8 rows for each locality: Loc1.1000, Loc1.2000, . . . Loc1.8000. If

Hi; I am working with the similarity matrix below and I would like to plot a two-dimensional MDS solution such as each point in the plot has a label. This is what I did: data <- read.table('c:/multivariate/mds/colour.txt',header=FALSE) similarity <- as.dist(data) distance <- 1-similarity result.nmds <- nmds(distance) plot(result.nmds) (nmds and plot.nmds as defined at

Hi, I am looking for the best multidimensional configuration for my data (47*47 distance matrix). I ve tried classical metric (cmdscale) and non metric MDS (isoMDS, nmds) but it is now difficult to choose the best solution because of the uncertainties in the definitions of the "stress" function. So, same problem, several questions : 1. Statistical consideration : With