similar to: binomia data and mixed model

Hi again. Perhaps a simple question this time.... I am analysing data with a dependent variable of insect counts, a fixed effect of site and two random effects, day, which is the same set of 10 days for each site, and then transect, which is nested within site (5 each). I am trying to fit the cross classified model using GLMM in lme4. I have, for potential use, created a second coding

Does someone out there have an example of R-code for a split-split plot ANOVA using aov or another function? The design is not balanced. I never set up one in R before and it would be nice to see an example before I tackle a very complex design I have to model. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

I am trying to perform a model 3 ANOVA for a 2 factor (say factor A and factor B) anova in which factor A is fixed and factor B is random. Therefore, the error term for the test of factor A should be the A:B interaction term and the error terms for B and A:B should be the model residual (within) term. I have tried to work out how to specify such error strata using aov, however, I have had

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

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Hello. I am fitting a two-level mixed model which assumes equality of variance in the lowest-level residuals across groups. The call is: fit3<-lme(CLnNAR~CLnRGR,data=meta.analysis, + na.action="na.omit",random=~1+CLnRGR|study.code) I want to test the assumption of equality of variances across groups at the lowest level. Can someone tell me how to do this? I know that one

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

This graph -> http://www.math.hope.edu/~tanis/dallas/images/disth36.gif is an example I found at http://www.math.hope.edu/~tanis/dallas/disth1.html created by Maple. Does anybody know how to create something similar in R? I have a feeling it could be possible using scatterplot3d (perhaps with type=h, the fourth example in help('scatterplot3d')?), but I cannot figure it out. Thanks in

Hi, All; When I generate a "special" paper postscript image larger than "a4" or "letter" using R, I can only see one-page portion of all image, of course. What will be the simple solution for this? Is there any way I can set the bounding box information on the image? Or any other suggestions? Thanks in advance; Tae-Hoon Chung

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

Hi there Please excuse this elementary question, but I have been fumbling with this for hours and can't seem to get it right. I have a nested anova, with random factor "lakefac" nested within factor "fishfac" (fixed), with an additional fixed factor "Habfac". If I consider everything as fixed effects, it's addmittedly not the correct model, but I can at

Hello all! Consider a dataset with a grouping structure, Group (factor) Several treatments, Treat (factor) Some sort of yield, Yield (numeric) Something, possibly important, measured for each group; GroupCov (numeric) To look for fixed effects from Treat on Yield, a first attempt could be: m1 <- lm(Yield ~ Treat) which gives, in a symmetric situation, the same estimated fixed effects as:

Hi I try to implement a simple 2-factorial repeated-measure anova in the lme framework and would be grateful for a short feedback -my dependent var is a reaction-time (rt), -as dependent var I have -the age-group (0/1) the subject belongs to (so this is a between-subject factor), and -two WITHIN experimental conditions, one (angle) having 5, the other 3 (hands) factor-levels;

I have a large dataset where each Subject answered seven similar Items, which are binary yes/no questions. So I've always used Subject and Item random effects in my models, fit with lmer(), e.g.: model<-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1| Item_ID),data,binomial) But I recently realized something. Most of the variables that I've tested as fixed effects are properties

Hi ! We study the effect of several variables on fruit set for 44 individuals (plants). For each individual, we have the number of fruits, the number of flowers and a value for each variable. Here is our first model in R : y <- cbind(indnbfruits,indnbflowers); model1 <-glm(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4+I (freq8_4^2), quasibinomial); - We have

Dear All, I wonder if anyone can please offer any advice on a model including 2 fixed effects and 1 random effect, as well as a covariate? The experimental design is as follows: I have a two by two factor design, where the two factors, Age (A) and Group size (G), both have 2 levels (old or young, and 1 or 3 respectively), and I am interested in the effect of these factors upon a continuous

Dear R users, I have not found anything on this in the archives. Does anyone know whehther the parameter use= is not functioning in cor or enlighten me what it is supposed to do? My R version is "R version 1.8.1, 2003-11-21" on Windows 2000. I am hoping to be able to update to 1.9.1 as soon as it has appeared (we are not allowed here to install software on our own and thus I am trying

Hello, I have the following design, counts were collected at different transects, different depths and different sites at different times. Time is continuous and assumed to be random, all the others are categorical fixed where transect is nested within depth which is nested within site. I would like an expert opinion about the following code where intercept is modeled as random (I am not sure