similar to: How to Perform CCA in ??!! Help please

Hi All, I am using canonical correspondence analysis to compare a community composition matrix to a matrix of sample spatial relationships and environmental variables. In order to parse out how much variance is explained purely by space (S/E) or the environment (E/S) I am using a conditional (partial) CCA. I want to test significance via Monte Carlo but I can not find a way to do this with a

I performed canonical correspondence analysis (cca) with the example data of vegan, but I'm not able to obtain a table like scores() for the constraining variables. I can see them in the summary() mode, but it would be great to have in a separate table. Any suggestion?, thanx Gianandrea require(vegan) data(varespec) data(varechem) vare.cca<-cca(varespec,varechem) scores(vare.cca)

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

Dear all, I realised a correspondence analysis with function cca() of vegan library. Just like in Okansen (2010) in the example of R help: library(vegan) data(varespec) data(varechem) vare.cca<-cca(varespec~ Al + P + K, varechem) With plot.cca() function I represented the species matrix in the next way: plot(vare.cca,display="species") Being similar to: plot((c(-2,2)),(c(-2,2)),

Hi All I am not clear quite how one could use cca from package vegan and the associated predict.cca to predict species abundance from environmental data (or if this is possible in a generalised way). In other words, can one derive a cca object based on known community data and use that to predict e.g. species abundances in a different number of samples based on environmental data? The help

Dear List, I tried to conduct cca using csv data but failed. The message said that data.frame is required. Please kindly share how to convert a csv-imported file to a data.frame. Thank you. Elaine code rm(list=ls()) spec <-read.csv("c:/migration/M_R_20100718_winterM_spec_vegan.csv",header=T, row.names=1) dim(spec) spec[1,] envi

Hi, I have a couple questions about fitting environmental (land use factors, plant species presence-absence, and soil variables) constraints to my CCA biplot. 1. After successfully plotting species and site scores in my CCA, I have been trying to insert the biplot arrows of the environmental constraints in my data set using the text() function. When I do that, the plot changes completely. Is there

Dear List, I tried to do cca based on species data and environmental variables (formula instead of community data). However, there was an error saying row sums must be >0. I searched the previous related messages but found few solutions. Please kindly help and thank you in advance. code This is vegan 1.17-3 Warning message: package 'vegan' was built under R version 2.10.1

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,

Hii rstudio members I am learning rstudio, For my manuscript I am trying to plot CCA using species and environmental data. But I am getting error like Error in cca.default(sptrans, envtrans) : all row sums must be >0 in the community data matrix *My code is like * library(vegan) library(ggplot2) library(dplyr) rassspec<-read.csv("C:/Users/hp/Desktop/R_data/rassspec.csv",

Hi, I've just started using R and am having some problems with CCA using vegan. I'm looking at abundance p/m2 (hence decimals) vs environmental variables and have been using http://ecology.msu.montana.edu/labdsv/R/labs/lab12/lab12.html to guide me through. My organism data looks like this: Sample "Species_1" "Species_2" "Species_3" etc Sample_1

Hello I am using Jari Oksanen's CCA routine from the Vegan package on some estuary data, following a technique applied in (Anderson, M.J. & Gribble, N.A., 1998, Partitioning the variation among spatial, temporal and environmental components in a multivariate data set, Australian Journal of Ecology 23, 158-167). Some steps in the process require that the dependent matrix be constrained by

Dear List, I used spec and envi for cca in ade4. (both are data.frame) However, there is a message telling that error in if (nf > rank) nf <- rank R missing value in TRUE/FALSE Please kindly help how to modify the code below. Thank you. Elaine code rm(list=ls()) spec <-read.csv("c:/migration/M_R_20100718_winterM_spec_vegan.csv",header=T, row.names=1) dim(spec) spec[1,]

Dear R community, I'm working with PCA and CCA methods, and I have a theoretical question. Why is it necesary to have more temporal values than variables when the CCA O PCA are going to be used? Could you advise to me some any paper about it? Thanks in advance, [[alternative HTML version deleted]]

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]

I am trying to use the cca/rda/capscale functions in vegan to analyse genetic distance data ( provided as a dist object calculated using dist.genpop in package adegenet) with geographic distance partialled out ( provided as a distance object using dist function in veganthis method is attempting to follow the method used by Geffen et al 2004 as suggested by Legendre and . FORTIN (2010). I

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

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.

Hello all, I'm hoping this is a simple problem. I'm trying to do cca of my data. I have my plant data and environmental data as 2 separate files. I have 3 years of data, stacked vertically, within these files. I want to conduct the cca for each year and am trying to create separate year files using the following: cnts94 <- cnts[1:27,] env94 <- env[1:27,] when I run

Hello, I am attempting to use the ANOVA.CCA function with the by="margin" option. The process works fine using the by="terms" option and I note in the Vegan manual that Jari suggests that an error may occur if the anova does not have access to the data on the original constraints. This is the error that I get: Error in dimnames(x) <- dn : length of 'dimnames'