similar to: weighted-ML estimates

I am having the same problem as one Rebecca Sela(see bellow). On 21/12/2007 12:07 AM, Rebecca Sela wrote: >* I am trying to optimize a likelihood function using NLMINB. After running without a problem for quite a few iterations (enough that my intermediate output extends further than I can scroll back), it tries a vector of parameter values NaN. This has happened with multiple Monte Carlo

Hi, I am using lmer to analyze habitat selection in wolverines using the following model: (me.fit.of <- lmer(USED~1+STEP+ALT+ALT2+relM+relM:ALT+(1|ID)+(1|ID:TRKPT2),data=vdata, control=list(usePQL=TRUE),family=poisson,method="Laplace")) Here, the habitat selection is calaculated using a so-called discrete choice model where each used location has a certain number of alternatives

Hi, I'm new to calling C++/C programs from R and am having some trouble getting started. Following Sigal Blay (Simon Fraser University)'s instructions, I have a .c file called "useC1.c": /* useC1.c */ void useC(int *i) { i[0] = 11; } This produces a .o file : "useC1.o" in a specified directory. I then open R, set the proper directory and:

Hello, I have been asked to help perform a meta-analysis with individual- and aggregate-level data. I found a nice article on this, and the idea is easy to understand: use a mixed effects models, but for the studies where there is only aggregate level data, force the variance to be that which was observed. Unfortunately, I am struggling to see how to implement this in R. I am familiar with

Dear All, I would be very grateful for your help concerning the following question: Below mentioned programme is available on net to generate longitudinal data. Usually we get almost same parameter estimates as used to generate the data. The problem here is I am not able to get it for data used here, despite increasing sample size and number of simulations. Is it normal to expect this type of

Hi, I asked this on mixed model mailing list, but that list is not very active, so I'd like to try the general R mailing list. Sorry if anyone receives the double post. Hi, I have a dataset of animals receiving some eye treatments. There are 8 treatments, each animal's right and left eye was measured with some scores (ranging from 0 to 7) 4 times after treatment. So there are

Dear all, First, I would like to thank you for your immense work. My question is about a frequent topic which I am not able to solve - even after hours of search in the mailing lisy. I would like to analyse random-effects (and fixed-effects)models of longitudinal / panel data with sampling weights. I have an unbalanced panel of different individuals in 5 years and income data as well as their

Hi- I want to fit a model with crossed random effects and heteroskedastic level-1 errors where inferences about fixed effects are of primary interest. The dimension of the random effects is making the model computationally prohibitive using lme() where I could model the heteroskedasticity with the "weights" argument. I am aware that the weights argument to lmer() cannot be used to

R Users, Emine Bayman sent this out earlier and we do not think it went through. Appologies if it did. We want to fit GLMM with lmer with binomial data and a one-way random effects model (overall mean is a fixed effect and there are random effects for each binomial). We are using the Laplace method. We are simulating multiple data sets and use the Laplace method with control = list

Hi all, I am trying to run a linear mixed effect models in lmer() from the lme4 package using the weights option. I am using the R version 2.7.2 (2008-08-25) and lmer version in lme4_0.999375-26, which I think it is the latest version! I am getting and error message when I add the option "weights" in the lmer function. This is the error message I get "Error en

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

Hello! I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible. For

I originally sent this to Doug Bates but have received no reply yet so I thought I would expand to a wider source. I've been trying to estimate linear mixed effect models in lmer() from the lme4 package using the weights option. The help and code for lmer() suggest to me that this is implemented but I can't seem to get it to do anything with weights = , no error message reported it

There are two ways to view weights. One is to treat them as case weights, i.e., a weight of 3 means that there were actually three identical observations in the primary data, which were collapsed to a single observation in the data frame to save space. This is the assumption of survfit. (Most readers of this list will be too young to remember when computer memory was so small that we had to

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 Help, I'm trying to calculate a weighted correlation matrix from a data frame with 6 columns (variables) and 297 observations extracted from the regression. The last column is a weight column which I want to apply. $ model :'data.frame': 297 obs. of 6 variables: ..$ VAR1 : num [1:297] 5.21 9.82 8.08 0.33 8.7 6.82 3.94 4 0 5 ... ..$ VAR2 : num [1:297]

Hi, I'm running wine-1.0.1, OpenBUGS 3.0.3, R 2.9.0, and R2WinBUGS on a Redhat Enterprise Linux machine. Following various peoples' suggestions... This works perfectly (yay!): wine Z:/opt/OpenBUGS/winbugs.exe Within R, however, I get this: (setup the example from ?bugs, then....) R> schools.sim <- bugs(data, inits, parameters, model.file, n.chains=3,

Hello all I'm running the lme4 package on my binomial data, and I'm happy with the model and the resultant plot. However, I'd like to plot my table data, which has: two IVs, and one DV. You can see an example below, where 'attractive' = question (IV), male = condition(IV/predictor) and no/yes = answer (dv). I'm using the table to investigate what questions act differently

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

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