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...effects and their corresponding degrees of freedom: you should see this being handled automatically and correctly by lme e.g. in the output of anova (model.lme) Lorenz - Lorenz Gygax Centre for proper housing of ruminants and pigs Agroscope Reckenholz-T?nikon Research Station ART T?nikon, CH-8356 Ettenhausen / Switzerland

...hy the middle term is not exactly 0? I do not understand the quadratic part at all. Wouldn't that need the be the linear part to the power of 2? Thank you for your thoughts! Lorenz - Lorenz Gygax Swiss Federal Veterinary Office Centre for proper housing of ruminants and pigs T?nikon, CH-8356 Ettenhausen / Switzerland

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

...ters are appreciated. Thanks, Lorenz - Lorenz Gygax, Dr. sc. nat. Tel: +41 (0)52 368 33 84 / lorenz.gygax at fat.admin.ch Tag der offenen T??r, 11./12. Juni 2004: http://www.fat.ch/2004 Center for proper housing of ruminants and pigs Swiss Veterinary Office agroscope FAT T??nikon, CH-8356 Ettenhausen / Switzerland Fax : +41 (0)52 365 11 90 / Tel: +41 (0)52 368 31 31

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

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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

...anyone point me in the right direction on where and how to answer these questions? Many thanks and regards, Lorenz - Lorenz Gygax Tel: +41 (0)52 368 33 84 / lorenz.gygax at fat.admin.ch Center for proper housing of ruminants and pigs Swiss Veterinary Office agroscope FAT T??nikon, CH-8356 Ettenhausen / Switzerland Fax : +41 (0)52 365 11 90 / Tel: +41 (0)52 368 31 31

...reciated. Thanks and regards, Lorenz - Lorenz Gygax, Dr. sc. nat. Tel: +41 (0)52 368 33 84 / lorenz.gygax at fat.admin.ch Tag der offenen T??r, 11./12. Juni 2004: http://www.fat.ch/2004 Center for proper housing of ruminants and pigs Swiss Veterinary Office agroscope FAT T??nikon, CH-8356 Ettenhausen / Switzerland Fax : +41 (0)52 365 11 90 / Tel: +41 (0)52 368 31 31

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

Hi, I am a first user of R. I was hoping I could get some help on some data I need to analyze. The experimental design is a complete randomized design with 2 factors (Source material and Depth). The experimental design was suppose to consist of 4 treatments replicated 3 time, Source 1 and applied at 10 cm and source 2 applied at 20 cm. During the construction of the treatmetns the depths vary

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

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, 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

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:

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

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