similar to: glmmPQL - predict (..., type= 'response')?

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

Dear R-mixed-effects-modelers, I could not answer this questions with the book by Pinheiro & Bates and did not find anything appropriate in the archives, either ... We are preparing a short lecture on degrees of freedom and would like to show lme's as an example as we often need to work with these. I have a problem in understanding how many dfs are needed if random terms are used for

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 ~ >

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.

Dear all, I am doing some data handling and tabualtions and then I am using glmmPQL to fit a binomial hierarchical model (which I assign to a new object). If I do a save.image () or a q ('yes') after this, I get the following warning messages: Warning messages: 1: namespaces may not be available when loading 2: names in persistent strings are currently ignored Sometimes R seems to

I have just realised that I sent this to Per only. For those interested on the list: -----Original Message----- From: Gygax Lorenz FAT Sent: Tuesday, September 14, 2004 4:35 PM To: 'Per Tor??ng' Subject: RE: [R] glmmPQL and random factors Hi Per, > glmmPQL(Fruit.set~Treat1*Treat2+offset(log10(No.flowers)), > random=~1|Plot, family=poisson, data=...) > > Plot is supposed

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

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

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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 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

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

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

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

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, 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 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

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

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;