similar to: random effects with lmer() and lme(), three random factors

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 <-

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 <-

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

Hi R helpers, I'm trying to fit a rather complex model to some simulated data using lme and am not getting the correct results. It seems there might be some identifiability issues that could possibly be dealt with by specifying starting parameters - but I can't see how to do this. I'm comparing results from R to those got when using GenStat... The raw data are available on the

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

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

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

Douglas Bates wrote: > > The GLMM function in the lme4 package allows you to specify crossed > random effects within the random argument without the need for the > pdBlocked and pdIdent constructions. Simply ensure that your grouping > factors are defined in such a way that each distinct group has a > different level in the grouping factor (this is usually not a problem

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")) >

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

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

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

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

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

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

---------- 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

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not

Hello, I'm very sorry for my repeated question, which i asked 2 weeks ago, namely: i'm interested in possibly simple random-part specification in the call of GLMM(...) (from lme4-package) i have a random blocked structure (i.e. ~var.a1+var.a2+var.a3, ~var.b1+var.b2,~var.c1+var.c2+var.c3+var.c4), and each one part of it i would like to model as Identity-structure matrix. So i had,