similar to: lme help configuring random effects

Hi  I have a dataframe similar to: >Sample<-c(1,1,1,2,2,2,3,3,3) >Time<-c(1,2,3,1,2,3,1,2,3) >Mass<-c(3,3.1,3.4,4,4.3,4.4,3,3.2,3.5) >mydata<-as.data.frame(cbind(Sample,Time,Mass))   Sample Time Mass 1      1    1  3.0 2      1    2  3.1 3      1    3  3.4 4      2    1  4.0 5      2    2  4.3 6      2    3  4.4 7      3    1  3.0 8      3    2  3.2 9      3    3

Hello: I am using R mainly on windows XP, version 2.5. I?m a biologist, with a medium level statistics background. I have a problem stating a two-way factorial design where both factors are random. I?m using the lmer() function implemented in the Matrix package version 0.99. My design is as follows: Two species were randomly selected from a total of 4 species. This species are present

I?ve used logistic regression to create models to assess the effect of 3 variables on the presence or absence of a species, including the interaction terms between variables and model averaging using MuMI: model.avg The top models (delta<4) include several models with interaction terms and some models without; model weights are quite low for all models (<0.25). My problem is that the models

Dear all, We want to test if the invasiveStatus is predicted by the amount (quant) of animals arriving to a country of a certain species (taxonid). We are using lmer to perform the model. The model is: lmer(invasiveStatus~I(log(quant+1))+I(log(inDegree+1))+(1|taxonid)+(1|country), family=binomial,data=td), where invasiveStatus is a binary variable, quant and inDegree are integer variables, and

Hello, Having measured two populations' characteristics at one particular time[with great precision] with R, I would like to extend this to measuring the same populations starting at t1, and then again at t2, and try to develop a growth model (something like dpop1/dt=r*pop^(...),dpop2/dt=r*pop^(...)). I think the idea is to create a model that will predict the growth of a population(N(mu,

Hi, I'm fitting a relatively simple growth model to some forest plot data. Two species of trees were planted in different mixtures in 10 (nearly-adjacent) plots and measured on four occasions over 10 years. The model is constructed in terms of the diameter increments (per year; DI) in the 3 intervals, in which DI is modelled as a function of mid-interval D and DSQ. The details of the

Dear colleagues, Von Bertalanffy model is commonly adjust to data on fish length (TL) and age (AGE) TL= Linf*(1-exp(-K*(AGE-t0)). Linf, K and t0 are parameters of the model. One main goal of the growth study is the comparison of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly

Hi R-listers, I am new to R and programming. I have a large dataframe composed of two grouping variables (species, population, with populations nested in species) and tens of continuously numeric variables. For each numeric variable, I want to make a boxplot with population as the X axis and the boxes filled according to which species it is belonging to. But, that is a definitely tedious work. I

Hi, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods and though have spent already many hours studying from various books an on-line helps, I was unfortunately not able to find a solution to my problem on my own. Data structure: I studied escape behavior of three species of a prey to a predator. The prey specimens (many) were in a

Hi guys, Got a few days left and I need to model a random effect of species on the body mass (logM) and temperature (K) slopes. This is what i've done so far that works: model1<-lme(logSSP~logM + K,random=~1|species,data=data1) model2<-lme(logSSP~logM + K,random=~K|species,data=data1) model3<-lme(logSSP~logM + K,random=~logM|species,data=data1) The one I now want is:

Dear R-people, I hope asking this is not too cheeky, but I do have a R Problem. I hope that some of you like to play around with R and can help me. Its like this. I have several plant species (A,B,C) and 10 replicates per species. 5 plants per species are damaged, 5 not. I let a caterpillar feed on each plant and measured the growth of the caterpillars on control plants (CR) and on damaged

Hi, I am new to this list as a poster, but a reader for some time. I've using R for several weeks now, and I have a lot of questions about certain procedures. Here I go: I want to test if there are differences in the time spent by pollinators visiting flowers of a given plant species, according to a number of experimental manipulations made on those flowers. All experimental

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.

Hi, I have the following conceptual / interpretative question regarding random effects: A mixed effects model was fit on biological data, with observations coming from different species. There is a clear overall effect of certain predictors (entering the model as fixed effect), but as different species react slightly differently, the predictor also enters the model as random effect and with

If I have a data frame where a species occupies several rows with different phases such as (both col's ar factors): species,phase Populus tremula,1 Populus tremula,2 Populus tremula,3 Calluna vulgaris,1 Calluna vulgaris,2 Betula alba,1 Betula alba,2 Betula alba,3 Primula veris,1 Primula veris,2 and another df where each species only have one row: species,growth_form Populus tremula,tree Acer

R-users - I must preface this question by saying that I'm a relative newbie to both R and mixed-modeling. I'm using lme fit an ANCOVA-like model. My data consist of bone length measurements for a developmental series of two capuchin monkey species. I'm interested in whether the rate of bone length scaling to body mass (i.e., growth) differs between species. My call to lme

Dear R users, I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green). model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4) Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533

Hi everyone, I am trying to analyse my data from a small plant experiment (for a meeting tomorrow afternoon) and am a beginner to R so I apologise if this is a very basic question. I carried out a plant experiment examining plant interactions between two species (A and B) under different watering treatments. I had: - 7 watering treatments (7 different watering frequencies labelled 1-7) - 3

Hi, I am running R2.8.1 under Linux, and I am having trouble using the variance functions in nlme My basic model was something like: model0 <- lme( log(growth) ~ light * species.group , data=data, random=~light|species ) # with 20 odd species divided in 2 groups Following the methods in Pinheiro&Bates I tried to put a variance function in the model: model1 <- update(model0,

Hi. I would like to run a mixed effects model, but there's one aspect of the model that I don't know how to code. My goal is to analyze data from an experiment in which I tested the swimming performance (i.e., Umax) of two species of fish from 4 different lakes (a sample of the data is below). Each fish was tested when it was rested and when it was tired, and each test was