similar to: Ordination plot option missing from PCA dialog

Hello to all, I'm new to R so I have a lot of problems with it, but I'll only ask the main one. I have clustered an environmental matrix with 2 different methods, and I'd like to plot them in a PCA and a db-RDA. I mean, I want see these clusters in the plots like points of differents colours, together with the rest information of the plot, but I don't know how to do this.

Hi Listers, I am trying to reflect a PCA biplot in the x-axis (i.e. PC1) but am not having much success. In theory I believe all I need to do is multiply the site and species scores for the PC1 by -1, which would effectively flip the biplot. I am creating a blank plot using the plot command and accessing the results from a call to rda. I then use the calls to scores to obtain separate site and

Hi all, I think I may be confused by different people/programs using the word rotation differently. Does prcomp not perform rotations by default? If I understand it correctly retx=TRUE returns ordinated data, that I can plot for individual samples (prcomp()$x: which is the scaled and centered (rotated?) data multiplied by loadings). What does it mean that the data is rotated from the

Hi all, I have site data with plant species cover and am looking for trends. I'm kind of new to this, but have done lots of reading and can't find an answer. I tried decorana (I know it's been replaced by ca.) and see a trend, but I'm not sure what it means. Is there a way to get the loadings/eigenvectors of the axes (like in PCA)? Is there a way to do this with rda() too? How

Dear friends, I am beginning to use R software in my academic research and I'm having some problems regarding the use of PCA. I have a table with 24445 rows and 9 columns, and I used the function prcomp() to do the analysis. Working with an example?: x<-read.table("test.txt", header=T) row.names(x)<-x[,1] x<-x[,-1] require(stats) pca<-prcomp(x, scale=T) names(pca) ##

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

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

Hi, I could use a hand solving a fairly straightforward ordination plot problem: I am conducing NMDS on some community data for roughly 300 localities and 650 species. I have a community matrix, a species attribute matrix, and an environmental attributes matrix. After running metaMDS {vegan} on the community matrix, I can successfully use either of the other two matrices to draw hulls around

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

I am severely rusty re. multivariate / ordination analysis, having done my last work 40 years ago (in plant ecology). I am interested in exploring applications of multivariate analytic approaches to data from the history of motion picture films. I'd very much appreciate any pointers as to possibly appropriate proceedures. The individual "samples" may be individual films and the

Hello, R-Help - I am trying to plot the results of an ordination from package vegan. The tricky part for me right now is getting the colors of the ellipses denoting the 95% confidence intervals of the group centroids to match the colors of the points for those same groups. >From and earlier post, I saw the code to make a plot of the ordination using different colors for my different groups.

Hi All, Can anyone point me a hint (package) how to draw a 3D plot using the first 3 components from PCA? Thanks a lot, FD [[alternative HTML version deleted]]

?for doesn't return anything help.search("for") doesn't return anything- Is the for loop so prevelant in computer programing that the documentation is implicit or is R paradigm to discourage the use of the for loop. I will post data probably tonight, but here is my problem. I have preformed an MDS on a set of data. I have the scores of the four axes that are the optimal

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 list, I am trying to predict species volume from bioclimatic data, I have various sites and I have a data frame with species volume and the corresponding bioclimatic data for each site. I read on a discussion forum that you can use ordination to predict species abundance (in my case volume) from 'new' climate data for sites where you do not know the abundance. Unfortunately I

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

Can anyone point me in the right direction of figuring out what downweight() is doing? I am using vegan to perform CCA on diatom assemblage data. I have a lot of rare species, so I want to reduce the influence of rare species in my CCA. I have read that some authors reduce rare species by only including species with an abundance of at least 1% in at least one sample (other authors use 5% as a

Hello. Does anyone know of an existing function that takes a list in the form of: Plot1 Species1 Abundance1 Plot1 Species2 Abundance2 Plot2 Species1 Abundance1 Plot2 Species3 Abundance3 . . . PlotN SpeciesN AbundanceN and translates into a matrix in the form of Species1 Species2.... SpeciesN Plot1 Abundance1 Abundance2... AbundanceN Plot2 Abundance1

Hi all, I' m trying to figure out if it is appropriate to do a PCA having only categorical data (not ordinal). I have only find the following quote: One method to find such relationships is to select appropriate variables and to view the data using a method like Principle Components Analysis (PCA) [4]. This approach gives us a clear picture of the data using KL-plot of the PCA. However, the

Hello, supposing that I have two or three clear categories for my data, lets say pet preferece across fish, cat, dog. Lets say most people rate their preference as being mostly one of the categories. I want to do pca on the data to see three 'groups' of people, one group for fish, one for cat and one for dog. I would like to see the odd person who likes both or all three in the