similar to: Mixed effect model in R

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, a colleague of mine uses R on his Mac and he has quite a nice feature: When he starts writing a part of the function like "plot(" the programming is showing him at the bottom the kind of arguments you can input to the function. He told me that he has not installed any further stuff than the base R program. Is this a feature you only have in the MAC version? I really like it. I

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

Hi experts, I have just about started to use R (after using SAS for more than 5 years) and still finding my way...I have been trying to replicate PROC MIXED results in LMER but noticed that the estimates are coming different. My SAS code is as follows (trying to randomise X2 and Intercept): PROC MIXED DATA = <DATASET NAME> NAMELEN=100 METHOD=REML MAXITER=1000; CLASS GEOGRAPHY; MODEL y

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is:

Dear R People: Sorry for the off-topic. Could someone recommend a good reference for using the EM algorithm on mixed models, please? I've been looking and there are so many of them. Perhaps someone here can narrow things down a bit. Thanks in advance, Sincerely, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

Hello, I am running (non-balanced) mixed models (using library lme4) such as : model1<-glmmPQL(Y~grouping variable+ covariate, random=~1|yr/month, data=dat, family= gaussian or poisson) and besides the usual output in terms of the statistical significance of the fitted coefficients, I'd like to obtain the adjusted means and standard deviations (as one obtains after fitting a lm model).

Hi Everyone, I posted a similar question about a week ago, but haven't gotten any replies -- I'm afraid that's because my previous question was too vague. Let me try again with a more specific question, and I hope someone can help. NOTE, I know I should be using the newer lme4 package, I just haven't had a chance to update my version of R yet, so the question below relates

Dear all, I have an experimental setup in which a random variable is nested within a fixed variable; however I have troubles specifying the correct LMM with lme. I have searched the lists but haven't been able to find an example like my setup, which I unfortunately need to get this stuff right. Pinheiro & Bates is great but I still can't figure out how to do it. My

Hi All, I am making the switch to R and uncertain which of the several packages for mixed models is appropriate for my analysis. I am waiting for Pinheiro and Bates' book to arrive via inter-library loan, but it will be a week or more before it arrives. I am trying to fit a generalized linear mixed model of survival data (successes/trials) as a function of several categorical fixed and

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I'm new to R and am trying to extract the factors of a dataframe using numeric indices (e.g. df[1]) that are input to a function definition instead of the other types of references (e.g. df$out). df[1] is a list(?) whose class is "dataframe". These indexed lists can be printed successfuly but are not agreeable to the plot() and lm() functions shown below as are their df$out

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

Anybody know where I can get the Pinheiro/Bates book? I can't find a bookstore w/ stock and the publisher says they don't know when they'll have it again. Thanks. -Frank

Dear R-Pros, I have count data in which 6-10 points are nested within 17 larger units (which are nested within 3 larger-still units) plus a number of covariates. It could be nested ANCOVA but for poisson family. Could someone kindly advise me 1) if lmer is the correct function for the analysis? and 2) what synax expresses the error structure? I have consulted the package HTML documentation but

Hello,   Could someone help me please and to tell how to get the probability from empirical DENSITY (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “dnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical DENSITY function (which does not correspond to any typical

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,