similar to: calculate length of gradient ?

Is there an R function that does canonical correspondence analysis. Can it be done using the VR function corresp()? If not, how hard it be to write R code to do it? I am a population biologist with long but patchy programming experience in C, Smalltalk, Java and other languages. Thanks, Patrick Foley patfoley at csus.edu

Hi, I want to include a vector representing the sites - environmental data correlation in a PCA. I currently use prcomp (no scaling) to perform the PCA, and envfit to retrieve the coordinates of the environmental data vector. However, the vector length is different from the one obtained in CAnoco when performing a species - environmental biplot (scaling -2). How can I scale the vector in order to

Dear all at the R-project help list. I have run into a problem when it comes to getting values for "total inertia" and "R-squared" for my DCA using "decorana" in the package "vegan". I have tried the "goodness" function, but the reply indicates that it does not work with "decorana" class objects. In Canoco one gets the Total inertia in

Hello, I am trying to obtain f-values for response (independent) variables from a CCA performed in vegan package, to see which ones of them have significative influence in my dependent variables (like the manual selection in canoco), but I can't find any function (or package) that do such a thing. The dependents variables are species data, and the independents are ambiental data. Than you.

Dear members, I am performing multivariate analysis on marine benthic populations using R. At first glance I found ca and VEGANO packages to be the suitable for the task, but neither has incorporated Detrended Canonical Correspondence Analysis (DCCA), which is just the method I want to apply on my data. I've looked for alternative packages containing the method, but my suspicion is that

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

Hola a tod en s, estoy intentando averiguar el número de modas en una distribución. Para ello utilizo diptest. Mi duda es que no acabo de entender cuando la información suministrada por los test suponen la existencia o no de unimodalidad/multimodalidad. Una parte de la salidad de diptest es la que pego a continuación (el resto esta en el fichero adjunto con las distribuciones kernels y las

Hi everyone, I?m a student and relatively new to R so apologies in advance if this question seems stupid or obvious to you. I have collected a dataset with about 60 species of diatoms (count data from 19 different sample sites) and environmental variables for each site (salinity, pH, etc.). It?s all in the same dataset but distinct in R through the functions below diat <- diatom [, 1:60]

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

Dear All, A method for the estimation is univariate unimodal densities (with unknown mode) is described in "Statistical Inference under Order Restrictions" by Barlow et al.. Would anyone know whether there is an R-implementation (preferably with reference) for the estimation of univariate discrete unimodal densities (with unknown mode)? Thanks in advance for your help. Kind

Hi R users, I have two data matrix, one with community data and another with environmental data. Prior to preform the CCA, I have used PCA to select some environmental variables and to avoid redundance information. The result is that I have 4 environmental variables and my community data matrix where, following bibliography, I have eliminated rare species. All variables were log-transformed (x+1)

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

R-ecologists: Anyone wanting to create a Krebs package for R can do so using the C-source code avalaible at: ftp://gause.biology.ualberta.ca/pub/jbrzusto/krebs/source.zip Barry J. Cooke Current mailing address: Ph.D. Candidate 3971 NW 23 Circle Environmental Biology and Ecology Gainesville, Florida, USA Department of Biological Sciences 32605

I think i mentioned this in private communication a while back, but in case anyone else cares: I have a version of canonical correspondence analysis (CCA) coded up in R. I based it on Ter Braak and Prentice (1988) Adv. Ecol. Res. 18:271-317, Ter Braak (1986) Ecol. 67(5):1167-1179, and Ter Braak (1995) Section 5.9 in Jongman, Ter Braak, and van Tongeren "Data Analysis in

After some investigation I found out that constrained ordination is working fine under R version 6.5.0 and 6.5.1 but not under any of newer versions. Is it posible to repair/make something in for example newest version of R 7.0 lito enble correct using of constrained ordination under BiodiversityRGUI environment? [[alternative HTML version deleted]]

Dear all, I would like to calculate a cca (package vegan) with species and environmental data. One of these environmental variables is cos(EXPOSURE). The problem: 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. The cca does not run with missing values. What can I do to make vegan cca ignoring these missing values? Thanks a lot,

Dear All, Does anybody know if there is a functionality in R to break histograms that show a clear bi-modal (or multi-modal) distribution into a series of unimodal histograms that added up result in the original histogram? I was thinking of using QQ-plots (for which tools are available in R), and then observing the number of times the observed quantiles cross the 1:1 line, but this only gives an

Dear R-help, We are conducting a distance-based redundancy analysis using capscale and then testing for statistical significance for six terms in the model for the constrained ordination using anova.cca in the vegan package. The significance test is sequential, i.e., testing for significance of a term only after accounting for all preceding terms. Could someone please provide us with either the

Dear List, I am trying to plot the result of cca using matplot but failed. Pls kindly help and thanks. Elaine The error message was error in xy.coords(x, y, xlabel, ylabel, log = log) : (list) object cannot be coerced to type 'double' code rm(list=ls()) library(vegan) library(MASS) # input richness birdrich

He everybody, Well I have a biplot CCA-like origined from plot.cca (vegan package). I need to rotate on y axis the lines and symbols of constrained and sites representation. If I do that on an image editor, I rotate everything, including titles, axes labels and positions. I just need to rotate the inner par and keep the variables names (constrained) and symbols in the new positions but with the