search for: lmer2

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of the standard error? If anybody could offer me a suggestion I...

...been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models. Generalized linear mixed models will be added "soon". Furthermore there is no mcmcsamp method for a model fit by lmer2. This deficiency will also be rectified "soon". Once I have all the capabilities and methods currently avail...

...been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models. Generalized linear mixed models will be added "soon". Furthermore there is no mcmcsamp method for a model fit by lmer2. This deficiency will also be rectified "soon". Once I have all the capabilities and methods currently avail...

> (fm1 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) Error in as.double(start) : Calloc could not allocate (888475968 of 4) memory ************************* > sessionInfo() R version 2.4.1 (2006-12-18) powerpc-apple-darwin8.8.0 locale: C attached base packages: [1] "grid" "dat...

Hello R-users: Could you please tell me how can I extract the "df (degree of freedom)" and "estfun (estimating functions)" for the following lmer (or lmer2) model? wtd.mixed<-lmer(ddimer~race+steroid+psi+sofa+apache + (1|subject), method="ML", data=final, cluster="id", weights=w) I tried the following codes: - for the degree of freedom (erorr message in the red): >so <- summary(wtd.mixed) > so$df[1:6] NULL Warn...

I seem to come to this problem alot, and I can find my way out of it with a loop, but I wish, and wonder if there is a better way. Here's an example (lmer1-5 are a series of lmer objects): bs=data.frame(bic=BIC(lmer1,lmer2,lmer3,lmer4,lmer5)$BIC) rownames(bs)=c('lmer1','lmer2','lmer3','lmer4','lmer5') best=rownames(bs)[bs==min(bs)] > best [1] "lmer5" This tells me that lmer5 is the model with the lowest BIC. I want to start working with lmer5 as the best model...

...a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious model with pdDiag replacing pdLogChol I cannot reproduce with lmer. The obvious choice for me would be my model lmer2, but this is yielding different result. Somebody any idea? Thanks, Joris I am using R version 2.1.0 for Linux and the most recent downloads of Matrix and nlme #dataset from McLean, Sanders and Stroup F <- factor(c(1,1,1,1,1,1,2,2,2,2,2,2)) R <- factor(c(1,1,2,2,3,3,1,1,2,2,3,3)) s <-...

Hello EVERYONE, I need an URGENT help from you please! How can I see the "estfun" (empirical estimating function) and "df" (degree of freedom) from the following mixed-model please? (fm1 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) Many thanks in advance for your kind help. Sattar ____________________________________________________________________________________ ews, photos & more. [[alternative HTML version deleted]]

...) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <- lmer2(read ~ SES*age +(1| schlid:idl) +(1| schlid), hamburg, control=list(gradient = FALSE, niterEM = 0)) where my data is "hamburg" and "idl" and "schlid" are the student and school ids, respectively. (1) Is this the specification I want to estimate to obtain those effects...

Running the Chapter 4 examples in Pinheiro & Bates' "Mixed-Effects Models in S and S-PLUS" (2000), I get a message that the default optimizer doesn't converge, but using "optim" for the optimizer results in convergence: > > library(nlme) > > fm1IGF.lis <- lmList(IGF) > > fm1IGF.lme <- lme(fm1IGF.lis) > Error in lme.formula(fixed =

...ch parameter, n.eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor (at convergence, Rhat=1). **************************************** But when I try to do this with my own data I get an error: **************************************** > display(e7.lmer2) lmer(formula = baLO ~ I(baRatio - 0.985) + delta + (1 + I(baRatio - 0.985) + delta | subject), data = e7) coef.est coef.se (Intercept) -0.19 0.06 I(baRatio - 0.985) -4.95 0.74 delta 0.41 0.06 Error terms: Groups Name Std.Dev....

Hello R list, I am hoping to conduct a logistic regression with repeated measures, and would love an actual "code run through" for such an analysis. I found only one related post on this list, but a full answer was never provided. I understand that the routine lmer (or lmer2) in the lme4 package is often recommended in such a case, but actually implementing it is where I've hit a wall. In a nutshell, the experiment involved presenting females from two groups (treatment, control) with an opportunity to mate with a virgin male every 6 hours for 48 hours. Every femal...

...n care of. If I try to add a Operator:Fixture interaction (as done in Montgomery) I also do not get agreement with my manual calculations (which are the same as in the Montgomery book). Can I recreate the standard anova table broken into the correct strata using lmer? Thanks, Elizabeth > ass.lmer2<-lmer(Time~Layout*Fixture+(1|Operator),data=ass) > anova(ass.lmer2) Analysis of Variance Table Df Sum Sq Mean Sq F value Layout 1 1.153 1.153 0.3406 Fixture 2 82.792 41.396 12.2319 Layout:Fixture 2 19.042 9.521 2.8133 > summary(ass.lmer2) Linear mix...

...where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <- lmer2(read ~ SES*age +(1| schlid:idl) +(1| schlid), hamburg, control=list(gradient = FALSE, niterEM = 0)) where my data is "hamburg" and "idl" and "schlid" are the student and school ids, respectively. (1) Is this the specification I want to estimate to obtain those effect...

...uping factor data1 <- data.frame(counts, visit, zone, region) rm (counts, visit, zone, region) # a quick look bwplot(I(sqrt(counts))~as.factor(visit)|region, data=data1) # two lmer models lmer1 <- lmer (counts~visit+visit:zone + (1|region), data=data1, family=poisson(link="log")) lmer2 <- lmer (counts~visit+visit:zone + (visit+visit:zone|region), data=data1, family=poisson(link="log")) mcmc1 <- mcmcsamp(lmer1, n=10000) # works mcmc2 <- mcmcsamp(lmer2, n=10000) # doesn't work [[alternative HTML version deleted]]

...and it seems as though, as of right now, there is no way to specify the correlation structure in lmer. I was wondering if anyone knows if this is going to be implemented? I'm using mixed-effects models within a tree structure, so I make a lot of calls to lme to get the resulting deviance, and lmer2 is almost 5 times faster than lme on my test data, so if it may be implemented later I'd be willing to wait, otherwise I might look into trying to hack it myself. Sam Stewart

...not affect subsequent code in my script . Then I can just silence the original script line which makes the list and just have a couple of lines which save and load each one to the same object name as the original lists. So the commands I need help for should able to *save the lists *rg.lmer and rg.lmer2 separately And then *load the lists as lists, /not a df/,* in to another workspace Possibly looking towards sink() but not sure and couldn't figure out how this works... Thanks in advance Rob -- View this message in context: http://r.789695.n4.nabble.com/Save-reload-list-objects-tp4639397...

...ing command (in a .Rnw file) latex(anova(e7.lmer3, e7.lmer4), file = 'e7lmer34.tex', rowname = c ('nonlinear', 'linear'), longtable = FALSE, dcolumn = T, booktabs = T, table.env = F) produces the following output after running Sweave: % latex.default(anova(e7.lmer1, e7.lmer2), file = "e7lmer12.tex", rowname = c("nonlinear", "linear"), longtable = FALSE, dcolumn = T, booktabs = T, table.env = F) % \newcolumntype{.}{D{.}{.}{-1}} \begin{center} \begin{tabular}{l.......}\toprule \multicolumn{1}{l}{anova}& \multicolumn{1}{c}{D...

...ituations. Do I include the random intercept or not? I also have a related question. When I used to create a mixed-effects regression model using lmer and included e.g., an interaction in the fixed-effects structure, I would test if the inclusion of this interaction was warranted using anova(lmer1,lmer2). It then would show me that I invested 1 additional df and the resulting (possibly significant) improvement in fit of my model. This approach does not seem to work when using gam. In this case an apparent investment of 1 degree of freedom for the interaction, might result in an actual decrease of...

...urse.hours.per.day Aims To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients). To determine if nurse.hours.per.day affects rate. To determine if rates vary between hospitals. There is first order autoregression in the data. I have attempted to use the lmer function (and lmer2) with correlation structure and weights: test1 <-lmer(opsn1~timepoint+as.factor(hospital)+ skillmixpc + nursehrsperpatday +(timepoint|hospital) +offset(log(totalpats)),family=poisson, data=opsn.totals) test2 <-lmer(opsn1~timepoint+as.factor(hospital)+ skillmixpc + nursehrsperpatday +(...