similar to: cross-classified random factors in lme without blocking

Dear all, I am trying to fit a model with crossed random effects using lme. In this experiment, I have been measuring oxygen consumption (mlmin) in bird nestlings, originating from three different treatments (treat), in a respirometer with 7 different channels (ch). I have also measured body mass (mass) for these birds. id nest treat year mlmin mass ch hack 1EP51711 17

Classification: UNCLASSIFIED Caveats: NONE R users, This is a summary for the responses I got from Sandra Dorai-Raj and Simon Blomberg followed by my question. Sandra suggested using lm instead of lme for a model without a random effect, and Simon suggested anova.lme instead of anova.lm for comparing two models with and without a random effect. For the anova test, both anova.lme and anova

Hi all - R2.0.1, OS X Perhaps while there is some discussion of lme going on..... I am trying to execute a glmm using glmmPQL from the MASS libray, using the example data set from McCullagh and Nelder's (1989, p442) table 14.4 (it happens to be the glmm example for GENSTAT as well). The data are binary, representing mating success (1,0) for crosses between males and females from two

Hello all, In lme4 if you want to model two non-nested random effects you code it like this: mod1 <- lmer(y~x + (1|randomvar1) + (1|randomvar2)) How would you go about to model something similar in nlme? In my database I have two variables for which I have repeated measures, lets call them "individual" and "year". But none of the "individuals" were measured in

Hello, I have an unbalanced mixed model design with two fixed effects "site" (2 levels) and "timeOfDay" (4 levels) and two random effects "day" (3 consecutive days) and "trap" (6 unique traps, 3 per site). The dependent variable is the body length ("BL") of insect larvae from 7 to 29 individuals per trap (104 individuals in total). To account

I'm trying to do some smoothing with lme and am having some difficulty bringing commands over from S-PLUS to R. I have the following setup (modified from Ngo and Wand, 2004): set.seed(1) x <- runif(200) y <- sin(3*pi*x) + rnorm(200)*.4 ## library(splines) z <- ns(x, 4) The following runs without error on S-PLUS f <- lme(y ~ 1, random = pdIdent(~ -1 + z)) But in R I get

Dear All, I have a problem running this program on R. Z is a matrix of spline which is random > fit<-lme(anc~X,random=pdIdent(~Z)) Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups What I have done wrong?

Full_Name: Simon Frost Version: 1.0.0 OS: Windows Submission from: (NULL) (129.215.60.79) Hi everyone, I've just installed R for Windows - I've previously been using it on Linux - and I've run into a problem when using lme. The lme routine throws up an error in the getGroups.data.frame command, complaining about an error in the formula: Error in getGroups.data.frame(dataMix,

I am trying to fit lme using R 2.1.1 under Windows 2k. I am getting the following message noted below. Any suggestions that would help me correct my error would be greatly appreciated. Thanks, John > fit1lme<-lme(Velocity~time,data=gate) Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics

Hi, I have a question about the function lme() in R. I have a 2*2*3 layout with some missing data (labelled as *). These 3 factors are labelled as A,B,C, the response is Score. The layout is as follows:- A B C Score 1 1 1 5 1 1 2 * 1

I have an ANOVA model with 2 factors "Environment" and "Site", "Diameter" is the response variable. Site should be nested within Environment. Site is also a random factor while Environment is fixed. I can do this analysis using the "aov" function by using these commands: >model<-aov(Diam~Env+Error(Env%in%Site),data=environ) >summary(model)

Hi everone, I have a question on using lme on a mixed effects model. The linear mixed model is in the form of: y = bX +Zu + e where "X" and "Z" are the matrices, "b" is the coefficient vector of fixed effects, "u" is the coefficient vector of random effects, and e is an error vector. I would like to use "lme" function to fit the model and

Hello, I want to perform an lme on a database with this structure: ID Sequence Temperature Tumour Error 1 5 0 1 8.721872e-08 1 5 0 2 8.695348e-08 1 5 0 3 2.019604e-13 1 5 37 1

Hi all, I have collected response time data from 178 participants ('sub') for each combination of 4 within-Ss factors ('con','int','tone','cue'). Additionally, I have recorded the gender of each participant, so this forms a between-Ss factor ('gender'). Normally this would be analyzed using aov:

Classification: UNCLASSIFIED Caveats: NONE Dear R users, I'm interested in finding a random effect of the Block in the data shown below, but 'lme' does not work without the random effect. I'm not sure how to group the data without continuous value which is shown in the error message at the bottom line. If I use 'aov' with Error(Block), is there a test method comparing

Dear list, the following should fit the model log(PEPC.Wert)=fKTemp+fHerk+interaction(fKTemp,fHerk)+fMub+error, where fKTemp, fHerk are fixed effect factors and fMub is a random effects factor nested in fHerk (values are different fopr different values of fHerk). > logpepcr1 <- lme(log(PEPC.Wert) ~ fKTemp*fHerk, random= ~ 1 | fMub, na.action=na.omit) The following should be without the

I need to specify a model similar to this lme.formula(fixed = sqrt(lbPerAc) ~ y + season + y:season, data = cy, random = ~y | observer/set, correlation = corARMA(q = 6)) except that observer and set are actually crossed instead of nested. observer and set are factors y and lbPerAc are numeric If you know how to do it or have suggestions for reading I will be grateful. eal ps I have

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Hi helpeRs, I've been working through some examples in Pinhiero & Bates( 2000) trying to understand how to translate to the new Lme4 syntax but without much luck. Below is what I think I should do, but either the answers don't come out the same or I get errors. In the Oxide problems I'm particularly interested in obtaining the levels coeficients but this options no longer seems

I've posted the following to R-help on May 15. It has reproducible R code for real data -- and a real (academic, i.e unpaid) consultion background. I'd be glad for some insight here, mainly not for myself. In the mean time, we've learned that it is to be expected for anova(*, "marginal") to be contrast dependent, but still are glad for advice if you have experience. Thank