similar to: random effects in mixed model not that 'random'

Hi, First I should note I am relatively new to R so I would appreciate answers that take this into account. I am trying to perform an MDS ordination using the function ?metaMDS? of the ?vegan? package. I want to ordinate species according to a set of functional traits. ?Species? here refers to ?sites? in traditional vegetation analyses while ?traits? here correspond to ?species? in such

I have a host-parasite association matrix in which parasite species are rows and host species columns and cells contain the frequency of interactions. Some parasites are associated with many hosts, and some hosts harbor several parasites, and I want to repeatedly select only one single representative host per "generalized" (multi-host) parasite to create a new matrix in which no hosts

Hi, I should preface this problem with a statement that although I am sure this is a really easy function to write, I have tried and failed to get my head around writing functions in R. I can use R where functions exist to do what I want done, but have found myself completely incapable of writing them myself. The problem is that I have a table with several rows of species and several columns of

I hope you can help with this one. I have two matricies: 1. A species abundance matrix: <http://r.789695.n4.nabble.com/file/n4643979/2species_matrix.jpg> 2. A species trait score matrix: <http://r.789695.n4.nabble.com/file/n4643979/2trait_matrix.jpg> The trait matrix lists trait scores for each species as listed in the species abundance matrix. I would like to create a script

Hi, I am working with a species-by-trait .csv file (columns=traits, rows=species) and get the following warning message when trying to plot results of both metaMDS and pcoa:  "Warning message: In ordiplot(x, choices = choices, type = type, display = display,  :   Species scores not available" I am using a Gower's transformation in both procedures within the metaMDS or pcoa

Hello I have a table describing butterfly range traits. It is composed of three columns as below Species name range size (X) latitude of range midpoint (Y) There are 11 kinds of butterflies. Each has its range size, and the latitude of each range midpoint ranges from 9 to 19. I would like to have the average range size of every degree of latitude. For example, the average range

Dear R user, I have a question on using R to analyze data with repeated measurements. I have 2 species with several strains (12) per species, each of which has been measured twice with for a given trait. No particular covariance, just two measures. Now I want to analyze the data with an ANOVA (aov) considering these repeated measures to get the MSq and SSq for the species and strain level. I

Doea anyone know how to compute components of variance analysis? For example, I have the score of pupils on a test. Each pupil belongs to a school and within each school I may have several classes? How can I estimate the variance of the pupils, schools, classes and the errror variance? Any examples? Any help will be appreciated. Jason Dr. Iasonas Lamprianou Department of Education The

Hello, Does somebody could help me in the computation(formulation) of a hierarchical ANOVA using linear model in R? I'm working in a population biology study of an endangered species. My aim is to see if I have effects of "Density of individuals/m2" on several measured plants fitness traits. As independent variables I have: Humidity (continuous) Nutritive substance (continuous)

Hello I have a table describing butterfly range traits. It is composed of three columns as below Species name range size (X) latitude of range midpoint (Y) There are 11 kinds of butterflies. Each has its range size, and the latitude of each range midpoint ranges from 9 to 19. I would like to have the average range size of every degree of latitude. For example, the average range

Hi Everyone,  Sorry to ask what I think is a basic question but I really haven't found my answer yet in the archives.  I am trying to run a mixed effects model in R using the lme package. My experiment is such that I am interested in the effects of Temperature (2 levels) and Species (3 levels) on Growth. I collected individuals from three populations within each species. Because individuals

Dear List, I have a data frame called trait with roughly 800 species in, each species have 15 columns of information: Species 1 2 3 etc.. a t y h b f j u c r y u etc.. I then have another data frame called com with the composition of species in each region, there are 506 different communities: community species NA1102 a NA1102 c NA0402 b NA0402 c AT1302 a AT1302 b etc.. What

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

Hello all: I am confused about the output from a lm() model with an incomplete design/missing level. I have two categorical predictors and a continuous covariate (day) that I am using to model larval mass (l.mass): leaf.species has three levels - map, syc, and oak cond.time has two levels - 30 and 150. There are no response values for Map-150, so that entire, two-way, level is missing.

Dear List, I have a data frame called trait with roughly 800 species in, each species have 15 columns of information: Species 1 2 3 etc.. a t y h b f j u c r y u etc.. I then have another data frame called com with the composition of species in each region, there are 506 different communities: community species NA1102 a NA1102 c NA0402 b NA0402 c AT1302 a AT1302 b etc.. What

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

Hi R-users I would like to extract the random effects ("1|SITE", "1+SPECIES|SITE" and "BA|SITE") from this model formula: Full_model <- formula (VAR ~ (1|SITE) + (1+SPECIES|SITE) + (BA|SITE) + HEIGHT + COND + NN_DIST) I tried: terms(Full_model) labels(terms(Full_model)) but I could not distinguish between random and fixed effects. thanks

Dear all, I wanted to let you know about four phylogenetic comparative methods (PCM) packages that have become available on (3 on CRAN and 1 on GitHub) recently that hopefully will be interesting to somebody. Three of them go significantly beyond the Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes. 1) There is a new version of mvSLOUCH available. The most important change is that the

Dear all, I wanted to let you know about four phylogenetic comparative methods (PCM) packages that have become available on (3 on CRAN and 1 on GitHub) recently that hopefully will be interesting to somebody. Three of them go significantly beyond the Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes. 1) There is a new version of mvSLOUCH available. The most important change is that the

Hi, I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured several traits on each individual. To test for an effect of either treatment or source as well as their interaction, I used a linear mixed effect model with family as random factor, i.e.