similar to: Nested Design Coding Question

Hi, I have a data set on germination and plant growth with the following variables: dataset=fm mass (response) sub (fixed effect) moist (fixed effect) pop (fixed effect) mum (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for split-plot design). I want to see if moist or sub interacts with mum for any of the pops, but I am getting an error

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

Dear R-help group, How can I define a lme with 3 factors(a,b,c), where c is nested in b, and a is crossed with b/c? I think that: lme(response ~ ..., data = Data, random = pdBlocked(list(pdIdent(~ a - 1), pdIdent(~ b - 1)))) is one part of the answer and: lme(response~..., data=Data, random=~1|b/c) is the other part of the answer but how can I combine them?? Could anybody please help

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

Hi there, I am reading the 2004 paper "Smoothing with mixed model software" in Journal of Statistical Software, by Ngo and Wand. I tried to run their first example in Section 2.1 using R but I had some problems. Here is the code: library(nlme) fossil <- read.table("fossil.dat",header=T) x <- fossil$age y <- 100000*fossil$strontium.ratio knots <-

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

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

Hi, all, I have a question about random effects model. I am dealing with a three-factor experiment dataset. The response variable y is modeled against three factors: Samples, Operators, and Runs. The experimental design is as follow: 4 samples were randomly chosen from a large pool of test samples. Each of the 4 samples was analyzed by 4 operators, randomly selected from a group of

Hello all: I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand

I have used lme() on data from a between-within subjects experiment. The correct ANOVA table is known because this is a textbook example (Experimental Design by Roger Kirk Chapter 12: Split-Plot Factorial Design). The lme() F-values differ from the known results. Please help me understand why. d<-read.table("kirkspf2.dat",header=TRUE) for(j in 1:4) d[,j] <- factor(d[,j]) ### Make

Moi! I was helping to teach a course on mixed models this week, and we came across a problem with coding more than one random effect in lme when they aren't nested. As an example, suppose we have an experiment where we sample moths from several populations, and place the moths on different trees, and measure a trait (in this case survival of offspring, but that's less important). We

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

1) I have the following data situation: 96 plots 12 varieties 2 time points 2 technical treatments the experiment is arranged as follows: a single plot has two varieties tested on it. if variety A on plot #1 has treatment T1 applied to it, then variety B on plot #1 has treatment T2 applied to it. across the whole experiment variety A is exposed to treatment T1 the same number of times as

I am doing an analysis and would like to use lme() and the multcomp package to do multiple comparisons. My design is a within subjects design with three crossed fixed factors (every participant sees every combination of three fixed factors A,B,C). Of course, I can use aov() to analyze this with an error term (leaving out the obvious bits): y ~ A*B*C+Error(Subject/(A*B*C)) I'd also like

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

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?

---------- Forwarded message ---------- From: Freddy Gamma <freddy.gamma@gmail.com> Date: 2011/1/21 Subject: TRADUCING lmer() syntax into lme() To: r-sig-mixed-models@r-project.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to

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

Hello, I have used nlme to fit a model, the R syntax is like fmla0<-as.formula(paste("~",paste(colnames(ldata[,9:13]),collapse="+"),"-1")) > fmla1<-as.formula(paste("~",paste(colnames(ldata[,14:18]),collapse="+"),"-1")) >

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,