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I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

I use lmer to fit a mixed effect model.It give some warnings.what does this warnings mean? and what should I do? > (fm2.mlm <- lmer(qd ~ edu + jiankang + peixun +hunyin + cadcj + age + age2 + sex + dangyuan + Comp.1 + Comp.2+trust.cz1 +(trust.cz1|commid), data = individual,na.action = "na.exclude",family="quasibinomial")) Generalized linear mixed model fit using PQL

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Dear all, I use lmer to fit a mixed effect model.It give some warnings. What can I do about this? Here is the function and the warning message: > model.growth.mcas5 <- lmer(response ~ monthElapsed + (monthElapsed|studentID), + data= mcas5, family=binomial(link="logit"), method='ML') Warning messages: 1: nlminb returned message false convergence (8) in:

Hi R-users: New to R and I am trying to run a GLM with random effects. I have 3 replicates ('Replicate) of counts of parasites ('nor.tot.lep') before and after an experiment ('In.Out'). When I run lmer I get the error messages (16 of each) below... > lmer(nor.tot.lep ~ In.Out + (In.Out|Replicate),data=coho, family =tweedie(var.power = 1, + link.power = 1)) Generalized

I have been struggling all day to import a particular function/method combination (ranef(), which extracts the random effects from a mixed model fit) from the nlme package into another package ... so far without success. The NAMESPACE for nlme contains the following lines: export(..., ranef, ...) S3method(ranef, lme) ranef is defined as a standard S3 generic, function (object, ...)

Hi R-Users, I have 3 replicates ('Replicate) of counts of parasites ('nor.tot.lep') before and after an experiment ('In.Out'). I am trying to treat the three replicates as a random effect in order to determine if the main effect (In.Out) significantly influences my dependent variable (nor.tot.lep) after the variance explained by the replicates is accounted for. I have

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

User of the coxme library (mixed effects Cox models) are instructed to use ranef(), fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same methods as nlme and/or lmer. The current behavior is to "depend" on nlme. If I defined the methods myself in coxme, then someone who had both nlme and coxme loaded will suffer from "last loaded wins",

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

Version 2.2 of coxme has been posted to CRAN, Windows versions and mirrors should appear in due course. This is a major update with three features of note: 1. A non-upwardly compatable change: Extractor functions: beta= fixed effects, b=random effects nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2 ------------------------------------------------------ beta

I'm trying to use the lmeSplines package together with lme4. Below is (1) an example of lmeSplines together with nlme (2) an attempt to use lmeSplines with lme4 (3) then a comparison of the random effects from the two different methods. (1) require(lmeSplines) data(smSplineEx1) dat <- smSplineEx1 dat.lo <- loess(y~time, data=dat) plot(dat.lo) dat$all <- rep(1,nrow(dat)) times20

>>>>> Therneau, Terry M , Ph D <therneau at mayo.edu> >>>>> on Sun, 15 Feb 2015 17:31:00 -0600 writes: > I'm testing out a new version of coxme and R CMD check fails with "could not find function > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > line below >

I really hope sombody could help me with the following, I'm having problems accessing the random effect samples following the example on MCMCsamp: (fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000, saveb=TRUE) str(samp0) Formal class 'merMCMC' [package "lme4"] with 9 slots ..@ Gp :

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

I'm testing out a new version of coxme and R CMD check fails with "could not find function ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the line below importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring at some obvious (but

> > I'm testing out a new version of coxme and R CMD check fails with "could not find function > > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > > line below > > > importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) > > > and nlme is declared in the DESCRIPTION

Dear all, I'm curious about how to interpret the results of the following code. The first model is directly from the help page of lmer; the second is the same model but using the Gamma family with log link. The fixed effects make sense, because y = 251.40510 + 10.46729 * Days is about the same as log(y) = 5.53613298 + 0.03502057 * Days but the random effects seem quite

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the