similar to: Class and object problem

Hi all, does anyone know how to automatically update starting values in R? I' m fitting multiple nonlinear models and would like to know how I can update starting values without having to type them in. thank all --- On Fri, 12/26/08, r-help-request@r-project.org <r-help-request@r-project.org> wrote: From: r-help-request@r-project.org <r-help-request@r-project.org> Subject:

check the following code: # settings n <- 100 # number of sample units p <- 10 # number of repeated measurements N <- n * p # total number of measurements t.max <- 3 # parameter values betas <- c(0.5, 0.4, -0.5, -0.8) # fixed effects (check also 'X' below) sigma.b <- 2 # random effects variance # id, treatment & time id <- rep(1:n, each = p) treat <- rep(0:1,

Hi Uwe and all, Error message was: error in normalizePath(path) : path[1]: no such file to load Many thanks, Odette On Fri, Jan 23, 2009 at 1:22 AM, Uwe Ligges <ligges@statistik.tu-dortmund.de > wrote: > > > Odette Gaston wrote: > >> Hi folks, >> >> I am currently having the problem with using R 2.8.1 that I cannot install >> some of packages from

Odette Gaston wrote: > Hi folks, > > I am currently having the problem with using R 2.8.1 that I cannot install > some of packages from CRAN or local drive and somebody may be able to help > me. > ex) faraway package and lme4 package. I have downloaded them in my hard > drive as local, but still R was unable to find the package (message showed > up as no such file). I

Odette > Wondering how can I generate "Akaike weight" with R? I know the description, > but is there any function to generate by R on the web-site or R library? > I am using GLM or GLMM (family=binomial), so would be appreciated if you > help me. You could have a look at this. http://bm2.genes.nig.ac.jp/RGM2/R_current/library/aod/man/summary.aic.html Which is in the OAD

Hi folks, I am currently having the problem with using R 2.8.1 that I cannot install some of packages from CRAN or local drive and somebody may be able to help me. ex) faraway package and lme4 package. I have downloaded them in my hard drive as local, but still R was unable to find the package (message showed up as no such file). I could download most packages, but not all what I want. I showed

Dear all, Is it possible to generate AIC or something equivalent for nonlinear model estimated based on maximum log likelihood l in R? I used nls based on least squares to estimate, and therefore I cannot assess the quality of models with AIC. nlme seems good for only mixed models and mine is not mixed models. res <- nls(y ~ d*(x)^3+a*(x)^2+b*x+c, start=list(a=2, b=1,c=1,d=1), data=d) If

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'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

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

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

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

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

I have recently upgraded my Redhat 7.3 server to Fedora FC1 with all related updates. I also upgraded my samba version from 3.0.0 to 3.0.2a. It now seems that my nmbd daemon will not start & subsequently I cannot log in from any Windows client as the netbios server name PDC is not found I think. Before this problem started, I noticed that in order for my DNS ping requests from Windows clients

Dear R-list, I thought that I would let some of you know of a free R package, glmm.ADMB, that can handle mixed models for overdispersed and zero-inflated count data (negativebinomial and poisson). It was built using AD Model Builder software (Otter Research) for random effects modeling and is available (for free and runs in R) at: http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html I

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

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