similar to: adonis(), design

I am trying to apply this technique (M.J Anderson 2001) to a dataset of aquatic insect abundances. There is a sample in the unrestored and restored segement of a stream for every time period. I would like to compare the centroids of the distance matrices for the treatments up (unrestored) and dn (restored) to see if there is a difference in insect communities between the treatments. I will not

Hi, I'm trying to use adonis on a subset of data from a dataframe. The actual data is in columns 5:118, and the first four columns are various factors. There are 3 levels of the factor Habitat, and I want to examine differences among only two of them. So I started with: > CoastNear = subset(gel_data, Habitat != "I") The resulting data.frame has three levels for Habitat, but

Hi, forgive me if someone has already posted about this but I have had a look and cannot find the answer, also I am very new to R and been getting the grips with this. I have been trying to use Adonis to find out if there are significant difference between groups on data that I have analyses with NMDS, and have been struggling with getting this to work and understanding what is going on. I am

I am trying to apply a permuation-based MANOVA (Anderson 2001) to a set of morphological data from three ecomorphs of fish reared under two different conditions and measured at two points during ontogeny. I will supply a distance matrix based on Procrustes distances calculated outside of vegan. I have not found an example of a design such as this for adonis. However, I have designed my factors

Hello all, I used google and looked at the documentation to find out why the ADONIS function is called adonis (in the vegan package). I am writing a document and would like to include a abbreviation list (similar to "ANOSIM = Analysis of Similarities"). regards, Steve

Hi, I was wondering if someone can tell me what is the difference between "strata" argument (function "adonis" in "vegan" package) and using random effects in PERMANOVA+ add-on package to PRIMER6 when doing permutational MANOVA-s? Is the way permutations are done the same? Thank you very much in advance, Vesna -- View this message in context:

Dear all, I am trying to get a PERMANOVA with quite large data set. I am reading a lot about this question, but I do not get the answer about it. Although I know that the R function is adonis () (vegan package), it does not work: adonis(Pha.env~SPha, data=Pha, permutations=10) The error message: Error: cannot allocate vector of size 334.2 Mb In addition: Warning messages: 1: In vegdist(lhs,

Hi, Somebody knows how to make contrasts if i'm using the function adonis? Thanks.

Hi, Using Vegan package I was wondering if there's a way to use a distance matrix as an input for adonis (or any of the other similar hypothesis testing functions) instead of the usual species by sample table. Working in the field of microbial ecology, what I'm trying to do is to overcome the problem of having to use discrete units such as species or OTUs, which are problematic in

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

Dear All, I'd like to perform adonis (from the vegan package) rather than amova (in ade4) on some haplotype data, as I have crossed factors. Is there a simple way to tweak the source to allow weights (haplotype frequencies) in a similar way to amova? Best Simon

Dear vegan team, Vegan development happens now completely in github. R-Forge repository is no more in sync with github. I tried to commit all github changes to R-Forge, but a week ago I got a conflict in file and I haven't had time to resolve that conflict. You can follow vegan development and vegan discussion also without signing to github. The system seems to be completely open and does not

Hi All, I have a question which does not pertain directly to the use of R but comes from my use of R! I have data which can be described as 3-dimensional e.g. (x,y,z), with no negative component. The suggested way to analyze this data is via multivariate techniques or by calculating what amounts to a levene's test on the data and then an ANOVA on the three components if the first test is

Hello, I want to perform a permanova where the first factor called Treatment has four levels. The second factor involves sampling the same research plots for four consecutive years, hence the repeated measurements. I have been able to use the adonis function from the package vegan to run this analysis. code below: TC.perMANOVA.adonis<-adonis(TC.PerMANOVA ~ Treatment*Year,

Hello, I would like to use a permanova to analyze my repeated measures, multivariate data set using the package vegan (adonis function). I have several explanatory variables and many clinical biochemistry/hematological response variables (all continuous, non-normally distributed). My explanatory variables are: ID - 29 levels - individual subjects Treatment - 2 levels Sex - 2 levels Time -

Hi everyone! I have data consisting of several response variables and several explanatory variables. I wish to do a permanova on this using the vegan library and the adonis() function. However, my data had several missing values in it. In order to 'fix' this I used the mice() function from the mice library to make 5 imputations for all the missing values. To do analysis on the 5 datasets

Dear List, Let's say I have a list whose components are 2 matrices (as exemplified in the "mylist" object below). I'd like to create a list with components being 4 matrices based on an logical index vector. is there a way to simplify what I'm doing to obtain the results in "mylist2"? I'd like something that would work on an arbitrary number of elements in

Hello R-user, sorry for this very off-topic question. But I shall present R to my dept. (pro's and con's and what it can do). The pro's and con's are easy but not what R can do (additional to the "normal" statistics). I looked through the packages, but the enormous amount of packages makes it very difficult for me to decide which one is worth mentioning. I used

Hi everyone, I have 2 environments (2 different R sessions) as described below: Session 1: Name of the environment: "CrlmmInfo" Objects in the environment: index1: logical index - length 238304 index2: logical index - length 238304 priors: list of 4 - (matrix 6x6, 2 vectors of length 6, vector of length 2) - all num params: list of 4: centers [238304 x 3 x

Hi everyone, I have 2 environments (2 different R sessions) as described below: Session 1: Name of the environment: "CrlmmInfo" Objects in the environment: index1: logical index - length 238304 index2: logical index - length 238304 priors: list of 4 - (matrix 6x6, 2 vectors of length 6, vector of length 2) - all num params: list of 4: centers [238304 x 3 x