similar to: CCA with Vegan - Plot problem

Dear R users, How to find all single minima within each next part of analyzed vector (table) Select all minima (mass_value=min & mass_value<2) (many) in vector(table), BUT first put mask on table in order to select within one window mask (5 elements) only one local minimum, and next to search within the next time window mask the second minimum (only one local along second mask)

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)

Hello, I am hoping someone can help me with the following multivariate issue: I have a model consisting of about 50 covariates. I would like to reduce this to about 5 covariate for the reduced model by combining cofactors that are strongly correlated. Is there a package or function that would help me with this in R? I appreciate any suggestions. Thanks, Ian

Hi, I need to find the z-score of the data present in a speardsheet. The values needs to be calculated for each gene across the samples (refer the example). And, it should be a simple thing, but I am unable to do it right now ! The example re the structure of the spreadsheet is - # Example: MyFile <- read.csv( text= "Names,'Sample_1','Sample_2','Sample_3'

We need to keep the discussion on the list. When I run your code, there are several problems. strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName ="strain", header = T, row.names = 1) str(strain.data) # lists 9 columns at the end with all NAs strain.data1 <- (strain.data, sqrt.dist = TRUE) # this is not a valid R line. I get Error: unexpected ',' in

Hello, I have a data set that looks like this: name G-ID test_id g-id g 1 00077464 C_068131 C_068131 OC_068131 - 2 00051728 C_044461 C_044461 OC_044461 - 3 00058738 C_050343 C_050343 OC_050343 - 4 00059239 C_050649 C_050649 OC_050649 - 5 00001761 C_000909 C_000909 OC_000909 - 6 00005119 C_002752 C_002752 OC_002752 - locs

Hello Sir I am getting problem in plotting in CCA . Could you please help me? I wrote the below command but I don't know why it is taking only first 5 env data rather than all 9. > strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName="strain", header = T, row.names = 1) > env.data <- read.xlsx("Dee rhiz.xlsx", sheetName="env", header = T,

Dear fellows, The two questions are on sm.density.compare(). I compare kernel density estimates of two arrays of data. I'd like to scale up y-axis so that I can show better the differences in y values. English is not my first language so I'll try to explain it. I would like to stretch y-axis a bit longer but not to change the range of y values. How can I do this? Second, I'm

Dear useRs, I have a slightly complicated data structure and am stuck trying to extract what I need. I'm pasting an example of this data below. In some cases, there are duplicates in the "gene_id" column because there are two different "sample 1" values for a given "sample 2" value. Where these duplicates exist, I need to average the corresponding

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

I am using VEGAN ordistep function for stepwise model selection. By default the Pin and Pout values are set to .05 and .1 Is it appropriate to use a family wise correction ( such as bonferroni or one of the alternatives) to adjust these values where there are several (5-10), potentially correlated variables in the model selection process? -- Nevil Amos Molecular Ecology Research Group

Dear List, I am running constrained correspondence analysis for abundance data of 7 birds. However, I would like to check which bird prefers which environment gradient by showing the species with different colors of the dots in cca plot (package vegan). Please kindly help and thank you Elaine [[alternative HTML version deleted]]

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

I am a beginner with R and have managed to use vegan to run a CCA on my data. I am evaluating the habitat relationships of select species in riparian areas around creeks before and after logging across three sampling intervals (pre-logging, two-years post-logging,. and 10 years post-logging). I sampled from 18 sites of 3 treatments (n = 6 sites for each treatment). When I plot my

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

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)

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 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]]

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 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