similar to: Strange Estimates from lmer and glmmPQL

I am trying to install the R package "Rmpi" which needs libmpi. I've installed openmpi and lam in Centos 5.2: [root at rab45-1 /]# rpm -qv openmpi openmpi-1.2.5-5.el5 openmpi-1.2.5-5.el5 [root at rab45-1 /]# rpm -qv lam lam-7.1.2-14.el5 lam-7.1.2-14.el5 But I get the following error message when trying to install Rmpi: /usr/bin/ld: skipping incompatible /usr/lib/lam/lib/libmpi.so

I'm using FC4 and R 2.3.1 to fit a mixed effects logistic regression. The response is 0/1 and both the response and the age are the same for each pair of observations for each subject (some observations are not paired). For example: id response age 1 0 30 1 0 30 2 1 55 2 1 55 3 0 37 4 1 52 5 0 39 5 0 39 etc. I get the

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, and 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML",

I have been looking into both of these approaches to conducting a GLMM, and want to make sure I understand model specification in each. In particular - after looking at Bates' Rnews article and searching through the help archives, I am unclear on the specification of nested factors in lmer. Do the following statements specify the same mode within each approach? m1 = glmmPQL(RICH ~ ZONE,

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

I can't find a way to retrieve z values and p-values from the output from lmer in the lme4 package. How is this done? Rick B.

Hello, I'm running a simulation study of a multilevel model with binary response using the binomial probit link. It is a random intercept and random slope model. GLMMPQL and lmer fail to converge on a *significant* portion of the *generated* datasets, while MlWin gives reasonable estimates on those datasets. This is unacceptable. Does anyone has similar experiences? Regards, Roel de

>On Wed, 3 Aug 2005, Bernd Weiss wrote: > >> I am trying to replicate some multilevel models with binary outcomes >> using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. > >That's not going to happen as they are not using the same criteria. the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by

Dear R HELP, ABOUT glmmPQL and the anova command. Here is an example of a repeated-measures ANOVA focussing on the way starling masses vary according to (i) roost situation and (ii) time (two time points only). library(nlme);library(MASS)

Hi, My name is Jos?? Mar??a G??mez, and I am pretty new in R. Thus, I apologize deeply if my questions are extremmely na??ve.I have checked several available books and URL's, without finding any answer. I'm trying to fit Generalized Linear Mixed Models via PQL. Below I provide the structure of my data set. Year and Plot are random variables. Fate is the binomial dependent. I have severe

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Dear all, I am trying to replicate some multilevel models with binary outcomes using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. The data can be found at <http://www.uni-koeln.de/~ahf34/xerop.dta>. The relevant Stata output can be found at <http://www.uni- koeln.de/~ahf34/stataoutput.txt>. First, you will find the unconditional model,

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Hello, I am using the R function glmmPQL to fit a logistic GLMM, with weights. I am finding that I get fairly different parameter estimates in glmmPQL from fitting the full dataset (with no "weight" statement) and an equivalent, shorter dataset with the weights statement. I am using the weights statement in the 'glmmPQL' function exactly as in the 'glm' function. I

Hi, I am trying to fit a model with lmer in R and proc glimmix in SAS. I have simplified my code but I am surprised to see I get different results from the two softwares. My R code is : lmer(y~age_cat + (1|cat),data=fic,family=binomial(link = "logit"), NaGQ=1) My SAS code is : ods output Glimmix.Glimmix.ParameterEstimates=t_estimates; proc glimmix data=tab_psi method=laplace;

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

Is there a way (simple or not) to get the marginal prediction from lme (in nlme) and/or lmer (in lme4)? Rick B.

Hello R users- I am new to R, and tried searching the archives and literature for an answer to this - please be patient if I missed something obvious. I am fitting a logistic regression model, and would like to include variance functions (specifically the varIdent function). I cannot figure out how to do this either in glmmPQL (or something similar) for the model with random effects, or in glm

Dear all, I am experiencing problems with glmmmPQL. I am trying to analyze binomial data with some spatial autocorrelation. Here is my code and some of the outputs > colnames(d.glmm) [1] "BV" "Longitude" "Latitude" "nb_pc_02" "nb_expr_02" [6] "pc_02" "nb_pc_07" "nb_expr_07"