similar to: Random seed problem in MCMC coupling of chains

And a last post from Paul Gilbert. Thanks to all! This disscusion was really beneficial for me! -----Original Message----- From: Paul Gilbert [mailto:pgilbert at bank-banque-canada.ca] Sent: sre 2005-06-08 21:01 To: Gorjanc Gregor Subject: Re: [R] Random seed problem in MCMC coupling of chains Gorjanc Gregor wrote: > Thanks to Paul and Gabor for additional tips/examples. Actually, I find

Folks, I am finding problems with using "dse": > library(dse1) Loading required package: tframe Error: c("package '%s' required by '%s' could not be found", "setRNG", "dse1") > library(dse2) Loading required package: setRNG Error: package 'setRNG' could not be loaded In addition: Warning message: there is no package called

Hi All, 1) Is it possible to set the options such that R opens a new script editor every time I start the R and 2) specify the size of windows. Thanks for the suggestion and Best regards, Krishna [[alternative HTML version deleted]]

I would like to request help with the installation of dse in raqua in mac os x 10.3. I get the following error message after the messages indicating that parts were successfully installed. I would be most grateful for a solution. ----------------------------------------- * Installing *source* package 'setRNG' ... ** R ** inst ** help >>> Building/Updating help pages for

Most ELF shared libraries can be sliced in two ways. One is following the information in the program headers (e_phoff). The other is following the information in the section headers (e_shoff). Regular relocatable objects only have the section header. At runtime, the dynamic linker only uses the program headers. In fact, the section headers is optional. When given a shared library, how should

In Solaris with R-1.7.0beta from 2003-04-01 I still have a problem with "R CMD check" after I build a package: * creating setRNG-manual.tex ... OK * checking setRNG-manual.tex ... ERROR Could not create DVI version. This typically indicates Rd problems. I can manually gererate the dvi from setRNG-manual.tex, so I don't think my latex is defective as I speculated last week. The file

Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.

I don't know the details of pls (in the pls.pcr package, I assume), but if you use validation="CV", that says you want to use CV to select the best number of components. Then why would you specify ncomp as well? Andy > From: ryszard.czerminski at pharma.novartis.com > > When I try to use ncomp parameter in pls procedure I get > following error: > > >

Hi all, I am currently doing a consistency analysis using an lmer model and trying to use parametric bootstrapping for the confidence intervals. My model is like this: model<-lmer(y~A+B+(1|C/D)+(1|E),binomial) where E is the individual level for consistency analysis, A-D are other fixed and random effects that I have to control for. Following Nakagawa and Scheilzeth I can work out the

Hello, I'm working on a problem using nested for-loops and I don't know if it's a problem with the order of the loops or something within the loop so any help with the problem would be appreciated. To briefly set up the problem. I have 259 trees (from 11 different species, of unequal count for each species) of which I am trying to predict biomass. For each tree species I have 10000

Hello, I would appreciate *any* ideas on this problem. I'm the maintainer of a package ("subselect"), which on CRAN's Daily Package Checks is OK on all flavours of R, except r-devel Linux x86_64, where there is a "memory not mapped" segfault with the very first example that is tried out (output below). Additionally, a user with an AMD64 machine has just reported a

Hi, this is related to a question just raised on Bioconductor where one function sets the random seed internally but never resets it, which results in enforced down streams random samples being deterministic. What is the best way to reset the random seed when you use set.seed() within a function? Is it still to re-assign '.Random.seed' in the global environment as following example

Dear R-help list readers, I am fitting mixed models with the lme function of the nlme package. If I get convergence depends on how the method (ML/REM) and which (and how much) parameters will depend randomly on the cluster-variable. How get the bist fit without convergence? I set the parameters msVerbose and returnObject to TRUE: lmeControl(maxIter=50000, msMaxIter=200, tolerance=1e-4,

Hello, My problem is, that I have to build hundreds of GARCH models to obtain volatility forecasts. I would like to run a loop, that would build those forecasts for me. There is no problem, with writing only the results of the forecasts, but I'd like to have stored results of the models in some file, that I could check later, what are the models like, to be able to compare if I should use

I would like to build a model in R to simulate the seed dispersal by one plant. The plant produced 5 seeds and the probability of falling inside the eight closest space was 0.8 and in the next space 0.2 and in the rest space 0: 0 0 0 0 0 0 0.2 0.2 0.2 0.2 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.8 1 0.8 0.2 0 0.2 0.8 0.8 0.8 0.2 0 0.2 0.2 0.2 0.2 0.2 0 0 0 0 0 0 0

I am having problems with the package dse. I just installed R 2.2.1 and reinstalled all packages. I am running Windows XP Pro with all updates. Below there are two examples of error messages generated when trying to execute some simple programs. The code was taken directly from the package documentation. Any help on this will be greatly appreciated. Merry Christmas Fernando

I've only been using R on and off for 9 months and started using the glasso package for sparse covariance estimation. I know the concept is to shrink some of the elements of the covariance matrix to zero. However, say I have a dataset that I know has some underlying "baseline" covariance/correlation (say, a value of 0.3), how can I change or incorporate that into to the

Hello. I've developed an algorithm in R which I need to package. The implementation uses S4 objects and it's divided in 5 files. Everything is working fine when I load the files into the R console but when I try to make a package I get an error that I don't quite understand. Here's what I do: *1.* in R console, I do and get: > package.skeleton(name='remora') Creating

Hi - I am using R version 2.13.0. I have run several GLMMs using the glmmPQL function to model the proportion of fish caught in one net to the total caught in both nets by length. I started with a polynomial regression full model with three length terms: l, l^2, and l^3 (l=length). The length terms and intercept were the fixed effects and the random effect was a paired haul (n=18).

Today I struggled for hours to understand some unexpected package test results. It turned out that this is because package "parallel", buried deep in my dependencies, calls runif() during it's initialization and in this way changes the random number sequence. This seems to be a part of a more general question--which kind of functions can we trust if we want to preserve random