similar to: Robust estimation of variance components for a nested design

Has there been any follow up to this question? I have found myself wondering the same thing: How then does SAS fit a beta distributed GLMM? It also fits the negative binomial distribution. Both of these would be useful in glmer/lmer if they aren't 'illegal' as Brian suggested. Especially as SAS indicates a favorable delta BIC of over 1000 when I fit the beta to my data (could be the

There has recently been some discussion on the list about AD Model builder and the suitability of R for constructing the types of models used in fisheries management. https://stat.ethz.ch/pipermail/r-help/2006-January/086841.html https://stat.ethz.ch/pipermail/r-help/2006-January/086858.html I think that many R users understimate the numerical challenges that some of the typical

R-help, I'm trying to estimate some parameters using the Maximum Likehood method. The model describes fish growth using a sigmoidal-type of curve: fn_w <- function(params) { Winf <- params[1] k <- params[2] t0 <- params[3] b <- params[4] sigma <- params[5] what <- Winf * (1-exp(- k *(tt - t0)))^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

Dear R People: When looking at the previous postings regarding TRAMO-SEATS, I am somewhat puzzled. Is it true that we CANNOT replicate TRAMO-SEATS because of licensing or ownership issues, please? If not, would anyone be interested in an R version of it, please? Thanks, Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

Hello, I am using odesolve to simulate a group of people moving through time and transmitting infections to one another. In Matlab, there is a NonNegative option which tells the Matlab solver to keep the vector elements of the ODE solution non-negative at all times. What is the right way to do this in R? Thanks, Jeremy P.S., Below is a simplified version of the code I use to try to do this,

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

?text says "'adj' allows _adj_ustment of the text with respect to '(x,y)'. Values of 0, 0.5, and 1 specify left/bottom, middle and right/top, respectively." But it looks like 0, 1 specify top, bottom respectively in the y direction. plot(1:4) text(2,2, "adj=c(0,0)", adj=c(0,0)) text(2,2, "adj=c(0,1)", adj=c(0,1), col=2) #the red

I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R: Error in lme.formula(fixed=zz + arm.long,random=~1 |

Hello, I am running R 2.6.1 on windows xp I am trying to fit a cox proportional hazard model with a shared Gaussian frailty term using coxme My model is specified as: nofit1<-coxme(Surv(Age,cen1new)~ Sex+bo2+bo3,random=~1|isl,data=mydat) With x1-x3 being dummy variables, and isl being the community level variable with 4 levels. Does anyone know if there is a way to get the standard error

Small data sets (6-12 values, or a similarly small number of groups) which don't look nice and symmetric are quite common in my field (analytical chemistry and biological variants thereof), and often contain outliers or at least stragglers that I cannot simply discard. One of the things I occasionally do when I want to see what different assumptions do to my confidence intervals is to run a

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am

I'd like to fit linear models on very large datasets. My data frames are about 2000000 rows x 200 columns of doubles and I am using an 64 bit build of R. I've googled about this extensively and went over the "R Data Import/Export" guide. My primary issue is although my data represented in ascii form is 4Gb in size (therefore much smaller considered in binary), R consumes about

Hello, I'm fairly new to R. I'm a chemist, not a programmer so please bear with me. I have a large data.frame that I want to break down (subset) into smaller data.frames for analysis. I would like to give the data.frames descriptive names which I have stored in a character vector. My original thought was that I want the subsets to show up as individual objects, but haveing them stored

Hi Folks... No code to troubleshoot here. I need some suggestions about the right terminology to use in further searching, and any suggestions about R pkgs that might be appropriate. I am in the planning stages of a project in which IR, NMR and other spectra (I'm a chemist) would be collected on various samples, and individual samples would be followed over time. The spectra will be feature

Hi All, I've been trawling through the documentation and listserv archives on this topic -- but as yet have not found a solution. I'm sure this is pretty simple with R, but I cannot work out how without resorting to ugly nested loops. As far as I can tell, grep, match, and %in% are not the correct tools. Question: given these vectors -- patrn <- c(1,2,3,4) exmpl <-

Hello R Users, I am interested in using R to generate quantitative structure-activity relationships (QSARs) for small molecules given a set of molecular descriptors (the X's) and biological data (Y's) (usually tab-delimited data files). Has anyone done this using R ? Would you be willing to share your R scripts (or ideas) for doing this with me ? I am particularly interested in

Conservative robust estimation methods do not appear to be currently available in the standard mixed model methods for R, where by conservative robust estimation I mean methods which work almost as well as the methods based on assumptions of normality when the assumption of normality *IS* satisfied. We are considering adding such a conservative robust estimation option for the random effects to

Hi the list, I use rgl to produce a 3D graph. I would like to "show" this graph to some collaborator. Is there a way to save it and send it to someone else? Christophe Genolini

I often use simple nested random-effect models for interlaboratory data. The variance components are important things to know. Is there an R function or package that gets variance components from lm objects? Or can someone point me to a method of doing so?