similar to: installing package lme4

Dear R: Am I having trouble downloading the LME4 library. I am using Windows and am using ver 1.7 I have tried the following: 1) Install package from CRAN, but LME4 is not listed 2) Downloaded LME4 from http://cran.us.r-project.org/, however, I cannot open the file when I try install from local drive. I get the following error: Error in file(file, "r") : unable to open connection

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

I am hoping to run a generalised linear mixed effect model but I don't seem to have access to package lme4. library (lme4) Error in library(lme4) : there is no package called ?lme4? I'm unsure of how to go about installing this package and I haven't been able to find much online either. I am using R version 3.4.1 on a Windows 10 laptop. Any help would be appreciated, thanks.

it takes a long time to load the lme4 package.anyone else encounter this problem? > system.time(library(lme4)) 佋佖伻侂佇佽佉仾伒伳伋佁伡伃伆侟仯伜Matrix 佋佖伻侂佇佽佉仾伒伳伋佁伡伃伆侟仯伜lattice [1] 19.90 0.30 25.56 NA NA > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status Patched major 2 minor

Dear List: I was able to run the following in nlme successfully, but the same model and code (same dataset) failed to run in lme4 and gave me the error message below. Any thoughts? lme(math~year, data=egsingle, random=~year|schoolid/childid) Error in lme(formula = math ~ year, data = egsingle, random = structure(list( : Unable to invert singular factor of downdated X'X

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

Hello. I have to fit a mixed model from a repeated measures split-plot experiment in which the response variable is binary. This requires a generalised linear mixed model in which I can specify a binomial distribution. I can’t find the appropriate package in R. I have looked at glmmML, but it doesn’t seem to allow any mixed structure beyond a simple 2-level one. Can anyone point me to the

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

I posted this on R help, and a few others responded indicating they too were able to replicate the error as a function of missing data. I believe this should not be the case and hence and reporting it here. ### Code provided on R-Help by Ivar Herfindal # Simulate data testdata <- cbind.data.frame(gr=3Drep(letters[1:4], each=3D5), = aa=3Drnorm(20), bb=3Drnorm(20)) # Introduce some missingness

Dear R-users, I did some more research and I'm still not sure how to set up an ANCOVA with nestedness. Specifically I'm not sure how to express chicks nested within boxes. I will be getting Pinheiro & Bates (Mixed Effects Models in S and S-Plus) but it will not arrive for another two weeks from our interlibrary loan. The goal is to determine if there are urbanization (purban)

I would appreciate help translating the following lme model to an lmer function. lme(lognrms ~ Group*Rotation*muscle*side*support*arms, random=~1|Subject/Stratum2/rep, data=Data) Many thanks Ross Darnell r.darnell at uq.edu.au

If you are indeed using lme and not lmer then the needed function is VarCorr(). However, 2 recommendations. First, this is a busy list and better emails subject headers get better attention. Second, I would recommend using lmer as it is much faster. However, VarCorr seems to be incompatible with lmer and I do not know of another function to work with lmer. Hence, a better email subject header

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Hello, I’m trying to perform a REML analysis using the lmer() function (lme4 package). Well, it seems to work well, except that I’m not getting any p-value (see example below). Can someone tell me what I did wrong? Thanks for your help, Amélie > library(gdata) > dive <- read.xls("C:/Documents and Settings/Amelie/My Documents/Postdoc/CE 2005-2006/divebydive.xls",

Just out of curiosity, why might this be occuring: > class(x6) [1] "mcmc" > crosscorr.plot(x6) NULL # Replicable code example(lmer) x6 <- mcmcsamp(fm1, n=1000) crosscorr.plot(x6)

Dear all, I try to run the example of lmer and get the following error message. > library(lme4) > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) [[1]] Error in get(x, envir, mode, inherits) : variable "as.dpoMatrix" was not found This error message is similar to what I get with other models. It looks like the mer class has a slightly

FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut some corners compared to identical(): > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) Unit: nanoseconds expr min lq mean median uq max neval identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100 isFALSE(FALSE) 713 761 1133.53 809.5 871.5

Quite possibly, and I?ll look into that. Aside from the work I was doing, however, I wonder if there is a way such that sapply could avoid the overhead of having to call the identical function to determine the conditional path. From: William Dunlap [mailto:wdunlap at tibco.com] Sent: Tuesday, March 13, 2018 12:14 PM To: Doran, Harold <HDoran at air.org> Cc: Martin Morgan <martin.morgan

We have released to CRAN a new version of the (recommended) package Matrix, and of package lme4 yesterday. Anyone who gets the new version of Matrix *MUST* re-install lme4 -- if (s)he is using lme4 at all. Technical details about that further below. The fact that yesterday's version number of Matrix is 0.9995875-2, indicates that Matrix' version is indeed approaching 1.0 (*), and

We have released to CRAN a new version of the (recommended) package Matrix, and of package lme4 yesterday. Anyone who gets the new version of Matrix *MUST* re-install lme4 -- if (s)he is using lme4 at all. Technical details about that further below. The fact that yesterday's version number of Matrix is 0.9995875-2, indicates that Matrix' version is indeed approaching 1.0 (*), and