similar to: lme and lmer df's and F-statistics again

Dear R users, I am building a hierarchical model on a large data set. It can take quite some time to finish one fit, I was just wondering whether it is possible to store the fit object (the result) to a file for later (offline) analysis. thanks Julia -- View this message in context: http://www.nabble.com/how-to-store-lme-lmer-fit-result-tp19910951p19910951.html Sent from the R help mailing

Brian Ripley wrote (to somebody asking about "effect sizes"): > ... > Given that, I wonder if you are used to standard terminology. Good point. But I think for many of us there is more behind that. I personally belong to an (apparently fairly large) group of R users who may be enthusiastic, but are statistical laymen due to a lack of formal education in the area. The

Hello all. I'm currently working with mixed models, and have noticed a curious difference between the nlme and lmer packages. While I realize that model selection with mixed models is a tricky issue, the two packages currently produce different AIC scores for the same model, but they systematically differ by 2. In looking at the logLik values for each method, I find that they

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Hi, I use R at home, and am interested in using it at my work company (which is in the Fortune 100). I began the request, and our legal team has given some gruff about the open source license. Not boring you with the details here, but I used some info on gnu.org as a rebuttal, and someone at the company replied that the generalities of GNU GPL may differ from R's specific GNU GPL license,

Kevin They do not exist. This question has come up often. Try the following to see a thread RSiteSearch('lmer p-values') Harold > -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Hai Lin > Sent: Friday, May 05, 2006 12:46 PM > To: r-help at stat.math.ethz.ch > Subject: [R] F-statistic

Hello !! II'm trying to test for my fixed effects using an lmer with quasipoisson errors. Since my lmer model is corrected for overdispersion using this kind of errors, I should use during model simplification in my Anovas *F test *and not *Chi square test* to compare two models. So I write: > anova(model,model2,test="F") but R keeps performing a Chi square instead of the F

I am newbie in R, trying to understand and compare syntax in nlme and lme4. lme() model from the nlme package I am interested in is: lme.m1.1 = lme(Y~A+B+C,random=~1|D/E,data=data,method="ML") (for simplicity reason, I am giving generic names of factors) If I understand well, there are three fixed factors: A, B and C, and two random factors: D and E. In addition to that, E is nested in

windows Vista R 2.10.1 What is the difference (or differences) between lme and lmer? Both appear to perform mixed effects regression analyses. Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone)

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

---------- 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

I'm migrating to lmer() from lme(). I have noticed that AIC values from lmer() are 2 units smaller than those reported by lme(). Could someone please explain why? The issue can be replicated with the first example from Pinheiro & Bates (2000) Mixed-effects models in S and S-plus. library(nlme) Rail2 <- Rail summary(lme(travel~1,data=Rail2,random=~1|Rail)) # AIC = 128.177

Dear community, I'd like to fix a mixed model. I have unbalance data, what should i use: lme in nlme package , or lmer in lme4. Thanks, user at host.com as user at host.com -- View this message in context: http://r.789695.n4.nabble.com/lme-or-lmer-for-unbalance-data-tp4608425.html Sent from the R help mailing list archive at Nabble.com.

Folks: In the following mixed effect model, the "x" and residual variance components are nonidentifiable. Yet lme() (or the equivalent in lmer()) happily produces an answer in which the sum of the variance components is the correct estimate of the single variance parameter. Why? -- i.e. why doesn't lme complain? x <- 1:50 y <- rnorm(50) m1 <- lme( y ~ 1, rand = ~1|x)

Can lme or lmer fit a plain regular fixed effects anova? Ie a model without a random effect, or have there be at least one random effect in order for these functions to work? Trying to run such, (1) without specifying a random effect produces an error, (2) specifying that there is no random effect does not produce the same output as an anova run in lm(); (2b) specifying that there is no

Dear all, I'm not very sure on the use of repeated measures in R, so some advice would be very appreciate. Here is a simple example similar to my real problem (R 2.6.0 for windows): Lets supose I have annual tree production measured in 9 trees during 3 years; the 9 trees are located in 3 different mountains (sites), and each tree receive different annual rainfall (different locations). I would

First of all, I apologize for asking a question that has appeared recurrently in this mailing list. However, I have googled for it, have looked at the mailing list archives, and also looked at Pinheiro & Bates book (although not very thoroughly, I must confess), to no avail. Here is the question: I am trying to obtain with lme or lmer the same exact numerical results (p-values) that I obtain

Hi all, I've searched threads and read up on some ways of doing this but I'm having a hard time to get it to work. Here's my basic problem. I have the following linear mixed model y = Xb+Zu+e where u~N(0,s^2*K) where K is a matrix. I read a thread that basically suggested to decompose Zu into ZPD^(1/2) D^(-1/2)P'u so that (D^(-1/2)P'u)~N(0,s'^2I) but I'm not sure

I wantto run sequential ANOVAs (i.e. type I sums of squares), and trying to getresults including ANOVA tables and associated coefficients for predictive variables(I am using the R 3.4.2 version). I think ANOVA tables look right, but believecoefficients are wrong. Specifically, it looks like that the coefficients arefrom ANOVA with ?marginal? (type III sums of squares). I have tried both lme

Hello, all, I am running some simulations to estimate power for a complicated epidemiological study, and am using lme and lmer to get these estimates. I have to run a few thousand iterations, and once in a great while, an iteration will create fake data such that the model won't converge. I see from Google searches that this is not an uncommon situation. My question: is there a way to