similar to: Logistic regression with repeated measures

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,

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of

Hi, I am attempting to plot survival curves estimated by cox proportional hazards regression model. The formula for the model is this: F.cox.weight <- coxph(Surv(Lifespan, Status) ~ MS + Weight + Laid + MS:Laid + Weight:Laid, data = LongF) MS = Mating status (mated/virgin) Weight = adult female weight, continuous covariate Laid = number of eggs laid by each female, continuous covariate I

> (fm1 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) Error in as.double(start) : Calloc could not allocate (888475968 of 4) memory ************************* > sessionInfo() R version 2.4.1 (2006-12-18) powerpc-apple-darwin8.8.0 locale: C attached base packages: [1] "grid" "datasets" "stats" "graphics" "grDevices"

I seem to come to this problem alot, and I can find my way out of it with a loop, but I wish, and wonder if there is a better way. Here's an example (lmer1-5 are a series of lmer objects): bs=data.frame(bic=BIC(lmer1,lmer2,lmer3,lmer4,lmer5)$BIC) rownames(bs)=c('lmer1','lmer2','lmer3','lmer4','lmer5') best=rownames(bs)[bs==min(bs)] > best [1]

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Dear R-users, the problem I'm facing today is to write a multicolor main title in a plot. I would like to have 3/4 colors to help people in the audience to visually realize what is what in a 4x2 plot array On top of each plot I would like to have two lines. The 1st line will indicate what is plotted The 2nd line will indicate a a reference Graphically speaking I would like a result similar

Hello EVERYONE, I need an URGENT help from you please! How can I see the "estfun" (empirical estimating function) and "df" (degree of freedom) from the following mixed-model please? (fm1 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) Many thanks in advance for your kind help. Sattar

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Hello , I am writing because I would need some advice on the following question. I am working on paternity in a monogamous bird species and I am performing analyses to check whether the probability for a male to be cuckolded (binary variable) depends on his body size, the body size of his female, the degree of genetic relatedness to his female and nest density around his own nest (all continuous

Dear r-helpers, I can run the examples on the mcsamp help page. For example: **************************************** > M1 <- lmer (y1 ~ x + (1|group)) > (M1.sim <- mcsamp (M1)) fit using lmer, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.(Intercept)

Hello everybody, I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error:"In aov (......) Error() model is singular" The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response

Here is a clean version. I did this with nls and it works (see below), but I need to do it with optim. Keun-Hyung # optim vol<-rep(c(0.03, 0.5, 2, 4, 8, 16, 32), 3) time<-rep(c(2,4,8),each=7) p.mated<-c(0.47, 0.48, 0.43, 0.43, 0.26, 0.23, NA, 0.68, 0.62, 0.64, 0.58, 0.53, 0.47, 0.24, 0.8, 0.79, 0.71, 0.56, 0.74, 0.8, 0.47) eury<-data.frame(vol=vol, time=time, p.mated=p.mated)

Hi, I am new to this list as a poster, but a reader for some time. I've using R for several weeks now, and I have a lot of questions about certain procedures. Here I go: I want to test if there are differences in the time spent by pollinators visiting flowers of a given plant species, according to a number of experimental manipulations made on those flowers. All experimental

Hello list, I've been reading through the archives and it seems as though, as of right now, there is no way to specify the correlation structure in lmer. I was wondering if anyone knows if this is going to be implemented? I'm using mixed-effects models within a tree structure, so I make a lot of calls to lme to get the resulting deviance, and lmer2 is almost 5 times faster than lme

Hello all, I have two questions regarding nested anova. I've attached a simple example, where a male is mated with 4 females, and the intensity of the colour of the eyes is measured in two offspring from each female. 1) As I understand it, residuals in a nested anova are the difference between an observed value and the predicted value, which is equal to the sample mean for the cell that

Hi, I think this is probably (& hopefully) very easy but I the ideas I have tried don't work. Simply, I have 2 very large lists in my workspace which take an awfully long time to make, each containing the information from 12500+ lmer models. I would like to now save these lists as files (txt, csv... whatever suits best) on to my computer/usb. Then I would like to be able to reload them in

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,