similar to: error using glmmML()

I'm trying to use the glmmML package on a Windows machine. When I try to install the package, I get the message: > {pkg <- select.list(sort(.packages(all.available = TRUE))) + if(nchar(pkg)) library(pkg, character.only=TRUE)} Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am not

Hi, I have a spatial data set with many observations (~50,000) and would like to keep as much data as possible. There is spatial dependence, so I am attempting a mixed model in R with a spherical variogram defining the correlation as a function of distance between points. I have tried nlme, lme, glmmML, and glmmPQL. In all case the matrix needed (seems to be (N^2)/2 - N) is too large for my

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is

Hi all, I am trying to create images in R suitable for journal publication. I'd like to make an image that is 3.6" wide and 5.08" length, and 1200 dpi. When I create a tiff file that is 800 dpi and 4x4", it works (although doesn't look good). But when I create a tiff that is the size and dpi I want, it doesn't work. I get the following error: Error in tiff(filename =

Trying to find a quick/slick/easily interpretable way to collapse a data set. Suppose I have a data set that looks like this: h <- structure(list(INDEX = structure(1:6, .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"), TICKS = c(0, 0, 0, 0, 0, 3 ), BROOD = structure(c(1L, 1L, 2L, 3L, 3L, 3L), .Label =

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

I have uploaded a new version (0.30-2) of glmmML to CRAN today. This is a rather extensive upgrade, mostly internal. Adaptive Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the integrals in the log likelihood function. The user can choose the number of points (default is 16), I _think_ that choosing 1 point will result in a Laplace approximation. The integrals in the score and

I have uploaded a new version (0.30-2) of glmmML to CRAN today. This is a rather extensive upgrade, mostly internal. Adaptive Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the integrals in the log likelihood function. The user can choose the number of points (default is 16), I _think_ that choosing 1 point will result in a Laplace approximation. The integrals in the score and

Hello, everyone. I'm hoping to prevent myself from doing a lot of pointing and clicking in Excel. I have a dataframe of bird nest check observations, in which I know the date of the first check, the date of the second check (both currently in Julian date format), the status of the nest at the second check (alive or failed), and the date that the nest hatched (i.e. changed from Incubation

Hello! I am using glmmML for a logitic regression with random effect. I use the posterior.mode as an estimate for the random effects. These can be very different from the estimates obtained using SAS , NLMIXED in the random with out= option. (all the fixed and standard error of random effect estimators are almost identical) Can someone explain to me why is that. The codes I use: R:

A new version of 'glmmML' (0.28-4) is uploaded to CRAN. The most important new feature is the possibility to get a p-value for the test of the hypothesis that the variance of the random effects is zero, on the wishlist of many R users these days! Note two things: (i) glmmML only treats random intercepts for binomial and poisson models, (ii) the p-value is calculated thru bootstrapping

A new version of 'glmmML' (0.28-4) is uploaded to CRAN. The most important new feature is the possibility to get a p-value for the test of the hypothesis that the variance of the random effects is zero, on the wishlist of many R users these days! Note two things: (i) glmmML only treats random intercepts for binomial and poisson models, (ii) the p-value is calculated thru bootstrapping

Hi: I have the following dataset in R( thanks Gabor for your help) but now the problem is that all the dashes are converted to questions marks ("?") I?am trying to get the dashes back using: mydf <- data.frame(lapply(mydf,function(x) replace(mydf,"?"(x),"-"))) but isn't working. I also tried: mydf <- replace(mydf,mydf=="?","-)")

Hi: I?need?help with a dataframe(see?pic attached). is a mix of dates and text. I want to create a table either?using latex function from hmisc or xtable. I already know how to do this but the problem is getting the dataframe into R. I don't have a reproducible example?but I am hoping that the pic attachment will make it to you.?If someone is interested in helping with this task I could send

I'm having a little trouble with trying to install newer versions of Starcraft (updates or Brood War) on a win-free Wine installation. I can run the installer off the original Starcraft CD, and it puts all of the files in the right place, adds a couple registry entries, and then chokes on creating the entries in the Start Menu. Still, I can kill the process at that point, and I have a usable