similar to: HLM - centering level 2 predictor

Hi, I'm using 'geepack' to run Generalized Estimating Equations. I'm aware that I can use anova to compare two models, but would it be possible to test QIC on R? It seems that there were similar questions a couple of years ago, but the question has not been answered yet. I'd appreciate if someone could show me the code! Thank you, Sachi [[alternative HTML version

Hi, I'm analyzing my data using GEE, which looks like below: > interact <- geeglm(L ~ O + A + O:A, + data = data1, id = id, + family = binomial, corstr = "ar1") > summary(interact) Call: geeglm(formula = lateral ~ ontask + attachment + ontask:attachment, family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1") Coefficients:

Hi, I'm running GEE using geepack. I set corstr = "ar1" as below: > m.ar <- geeglm(L ~ O + A, + data = firstgrouptxt, id = id, + family = binomial, corstr = "ar1") > summary(m.ar) Call: geeglm(formula = L ~ O + A, family = binomial, data = firstgrouptxt, id = id, corstr = "ar1") Coefficients:

Hi, I am new to this list. I have a question regarding including both spatial and temporal random factors in lmer. These two are not nested, and an example of model I try to fit is model1<-lmer(Richness~Y+Canopy+Veg_cm+Treatment+(1|Site/Block/Plot)+(1|Year), family=poisson, REML=FALSE), where richness = integer Y & Treatment = factor Canopy & Veg_cm = numerical, continous

Hello, I have been attempting to run a poisson glmm using lme4 for some time now and have had a lot of trouble. I would say 9 times out of 10 I receive the following warning: CHOLMOD warning: %h Error in mer_finalize(ans) : Cholmod error `not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 432 My data are counts of microbe colony forming units (CFUs) collected from

Dear All, I have conducted an experiment in order to examine predation pressure in the surroundings of potential wildlife road-crossing structures. I have documented predation occurrence (binary?) in these structures and calculated several possible explanatory variables describing the spatial heterogeneity in several scales. At the landscape scale I have calculated the percentage of different

Dear R list members, I have a question regarding xyplot. I managed to make a xyplot of all the IDs by using the syntax below: xyplot(PA ~ CRPC + CRPT | ID, data = redinteract) Now, I'd like to make a graph of a specific ID number (e.g., only ID number 301). I thought I could use "subset", but it seems to be not working. Could anyone let me know how I can make a graph of a

Dear R users: I am running lmer(), and I had the following warning message: "In mer_finalize(ans): iteration limit reached without convergence (9) What is the default limit for lmer()? And how do I increase the limit? Many thanks, Julia -- View this message in context: http://www.nabble.com/How-to-increase-iteration-limit-for-lmer%28%29--tp20252974p20252974.html Sent from the R help

Dear R list members, I'd like to make a graph of coefficients of the intercept, variable 1, and variable 2 (and possibly the interaction between variable 1 and variable 2). When I use the lmList function as attached below, it shows a nice coefficient graph. > PACRP.lis <- lmList(PAffect ~ CRPC + CRPT + CINT | ID, redinteract) > coef(PACRP.lis) > PACRPlis.coef <-

Hi, Probably this is a very simple question for all the programmers, but how do you change from 32-bit builds (default) to 64-bit builds? I've been trying to run Anova to compare two models, but I get the following error message: Error: cannot allocate vector of size 1.2 Gb R(3122,0xa0ab44e0) malloc: *** mmap(size=1337688064) failed (error code=12) *** error: can't allocate region ***

I'm having trouble centering multi-line strip text in lattice. As the code below demonstrates bounding box of the text is centered within the strip, but the first line isn't centered in relation to the longer second line. The "adj" argument to par.strip.text doesn't seem to do much. Suggestions? a=data.frame( x=rep(1:10,2) ,y=rep(1:10,2) ,z=rep(c('First Line\nLonger

I'm resending this message because I did not include a subject line in my first posting. Apologies for the inconvenience! Tanja > Hello, > > I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when

Hello! I am trying to run a GLMM using LME4, and keep getting the warning message: "In mer_finalize(ans) : false convergence (8)" I am quite new to R, and in looking into this thus far, it appears that there are a variety of reasons why this might occur, such as needing to standardize some parameters or if all subjects in one combination of parameters all have the same outcome. I also

Using lmer() on my data results in an error. The problem, I think, is my model specification. However, lm() works ok. I recreated this error with a more simple dataset. (See code below.) # word and letter recognition data # two within factors: # word length: 4, 5, 6 letters # letter position: 1-4 (in 4-letter words), 1-5 (in 5-letter words), 1-6 (in 6-letter words) # one dependent variable: #

Hello, I'm trying to create a custom function that "mean-centers" data and can be applied across many columns. Here is an example dataset, which is similar to my dataset: *Location,TimePeriod,Units,AveragePrice* Los Angeles,5/1/11,61,5.42 Los Angeles,5/8/11,49,4.69 Los Angeles,5/15/11,40,5.05 New York,5/1/11,259,6.4 New York,5/8/11,187,5.3 New York,5/15/11,177,5.7

Let me start with an example: par(mfrow=c(2,3)) for (i in 1:5){ x = rnorm(100) y = .5*x + rnorm(100, 0, sqrt(1-.5^2)) plot(x,y) } Note that there's five plots and six spaces for those plots via mfrow, leaving one row empty. Is there a way to have the bottom two plots centered? I think it looks weird to have them left-justified. Thanks in advance for the help! -- Dustin Fife PhD

Dear R community, I have two questions regarding fitting GLMM using maximum likelihood method. The first one arises from trying repeat an analysis in the Breslow and Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random effects, one subject specific, and one observation specific. Thus if we count random effects, there are more parameters than observations. When I try to run the

Hi all, I have a problem when running lmer. In my data set, Agree is a binary(0/1) response. WalkerID and ObsID is the identification number of the subjects. the description of the other variables are as follows: > levels(regdat$Display) [1] "Dynamic" "Static" > levels(regdat$Survey) [1] "HM1_A" "HM1_B" "HM1_C" "HM2_A"

Hello, I am trying to use the lmer function from the lme4 package I have installed today (lme4_0.999375-32.zip; R-2.10.1). According to the information, I should be able to use a generalized linear mixed model. However when I try to fit a model with Gamma distribution of the errors, it gives me the following error model1<-lmer(Cmic~1+(1|FOREST/DEPTH),data=DATOS,family=Gamma) Error en

Hey, I am having trouble with lmer. I am looking at the presence/absence of water shrews against habitat and other factors e.g so I used this: m1<-lmer(Presencebsence~Habitatype*Width+(1|Sitename))summary(m1) But i keep getting this error up Error in mer_finalize(ans) : Downdated X'X is not positive definite, 16.> summary(m1)Error in asMethod(object) : matrix is not symmetric [1,2] What