similar to: emperical bayes estimates and standard error lme4

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

> To sum up, I can't figure out how MLWin calculates the > standardized residuals. But I understand that this is not a > question for the R list. > Nevertheless, it would help if someone could point me to some > arguments why not to use them and stick to the results > obtainable by ranef(). Hi Dirk: Well, it is interesting that mlWin and lmer generate the same exact

Dear all I have a model that looks like this: m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) I know it is not possible to estimate random effects but one can obtain BLUPs of the conditional modes with re1 <- ranef(m1, postVar=T) And then dotplot(re1) for the examiner and item levels gives me a nice prediction interval. But I would like to have the prediction

Hello! I wonder how can one get upper and lower limits of a prediction interval -- exact values? They are shown on caterpillar plot using ranef() with argument postVar=TRUE, but I would like to know them. A while ago, some discussions were opened on "Confidence Intervals for Random Effect BLUP's", but the answer was never clear: http://www.mail-archive.com/r-help at

I'm running some multi-level binomial models with lme4 and have a question regarding the estimated random effects. Suppose I have nested data e.g. clinic and then patient within clinic. The standard deviations of the random effects at each level are roughly equal in a model for real life data. Attention then turns to examining the individual random effects at each level. I'm extracting

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

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(

Hi All, I am trying to shift from running mixed models in SAS using PROC MIXED to using lme4 package in R. In trying to match the coefficients of R output to that of SAS output, I came across this problem. The dataset I am using is this one: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect034.htm If I run the following code: proc mixed data=rc

Tengo una duda, hay unos modelos mixtos que se resuelven con lme4, no tengo problemas con esto, pero necesito tener el resultado para cada uno de los factores, lo expreso con este ejemplo aunque no es código r creo que se entendería Factores como ser casa y auto, tengo casa 1, casa 2, casa 3, y auto 1, auto2, auto 3. Con lme4 y utilizando ranef obtengo por ejemplo Casa 1

Hola a todos: Les pregunto, desconociendo si es posible de forma fácil. Utilizando la librería lme4, introduzco el modelo, se ejecuta, y obtengo resultado por ejemplo con el comando ranef(modelo). De esta forma entiendo que obtengo la soluciones. Pero me gustaría conocer el PEV (prediction error variance) junto a las soluciónes. Hay algún comando adecuado para este caso (tipo ranef).

Full_Name: Din Chen Version: 2.4.0 OS: Windows XP Submission from: (NULL) (66.17.122.18) I just updated the R.2.4.0. and got the error message for random effect modelling, which was working on R.2.3.1. library(lme4) mmod <- lmer(bright ~ 1+(1|operator), pulp) summary(mmod) Then when I tried to extract the residuals and random effect using: resid(mmodr) ranef(mmodr) I got error message:

When playing around fitting unconditional growth models using R and MlwiN today, I produced two different sets of estimates that I can't reconcile and wondered if anyone here has an idea: The data is two-level repeated measures data with measures nested within child. There are two measures per child. I've fit an unconditional growth model as in Singer and Willet (2003) that allows for

Hi R Users, When I use package lme4 for mixed model analysis, I can't distinguish the significant and insignificant variables from all random independent variables. Here is my data and result: Data: Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9), Variety=rep(rep(c("A1","A2","A3"),each=3),3),

On 05/01/15 12:04, Gabriel Franca wrote: > dear, > > I made the installation of Samba4 in centos6, now I need to back up the structure, tried using the script that samba.org offers more he is giving this error. > > See the error through the bash -x > bash -x samba_backup.sh > + FROMWHERE = / var / lib / samba > + WHERE = / root / backups > + '[' N ''

Hello all, I would like to plot the emperical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y

dear, I made the installation of Samba4 in centos6, now I need to back up the structure, tried using the script that samba.org offers more he is giving this error. See the error through the bash -x bash -x samba_backup.sh + FROMWHERE = / var / lib / samba + WHERE = / root / backups + '[' N '' ']' + '[' -n '' -a -d '' ']' + '[' -n

I am running R.And I want to make parallel computations. When using the snow package under R-2.13.1patched or the parallel package under R 2.14.0dev, R isn't able to create a cluster. When typing: cl <- makeCluster(4) R doesn't respond. It doesn't crash either. specs: OS: 32 bit, Windows XP Prof, SP3. Processor: Intel Core i3 cpu, quad core. My colleague works on exactly the

Dear friends. In Carlin and Louis "Bayes and emperical Bayes methods.." 1996 the classical example of 12 independent tosses of a fair coin producing 9 heads and 3 tails is given. If the situation is seen as a fixed sample of 12, a binomial lieklihood is used, and Carlin et al reports a probability of 0.075. Using sum(dbinom(9:12,12,.5)) I obtain 0.073 Likewise, if the experiment is

> > If one compares the random effect estimates, in fact, one sees that > > they are in the correct proportion, with the expected signs. They are > > just approximately eight orders of magnitude too small. Is this a bug? > > BLUPs are essentially shrinkage estimates, where shrinkage is > determined with magnitude of variance. Lower variance more > shrinkage towards

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!