similar to: glmulti runs indefinitely when using genetic algorithm with lme4

Dear all, I have a question about the glmulti package. I want to include some variables in all models. To that end I applied the wrapper function as shown in the examples (http://www.inside-r.org/packages/cran/glmulti/docs/glmulti). To include the variable "Geslacht" in all models: > glm.redefined = function(formula, data, always="", ...)

Dear All, wonder if you have some thoughts on running the with() function (and perhaps including the pool() function to get the results?) in glmulti? In other words, how to run glmulti with a data set that is produced by mice()? publicly available code: data <- airquality data[4:10,3] <- rep(NA,7) data[1:5,4] <- NA data <- data[-c(5,6)] library(mice) library(glmulti) the following

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

Hello, I'm using the "glmulti" package to run models of all the possible combinations of my variables. However, I am only interested in a few interactions between my variables. I have tried the equivalent of: mod1<-lm(y~a+b+c+a:b) glmulti(mod1, level=1) mod2<-lm(y~a+b+c+a:b) glmulti(mod2, level=2) and glmulti("y", c("a", "b", "c"),

Dear R, The glmulti package no longer loads through the library() command, apparently because of a problem with rJava. I have today reinstalled R from scratch (updated to v2.14.0) and reinstalled all packages from scratch and updated them all too. The problem is the same as I found on v2.13.2. See session below for the error. I tried install.packages(rJava) as advised by the error report but it

Dear all, I had a R segmentation fault, and then invoked debug mode and ran step by step. When I reached "terms(Y~X1*X2*...*X16)", I would then have "segmentation" fault. However, if I just ran this under regular "R interactive" mode, it would be fine though taking long time. My questions are: 1. Is there a known limit of terms for a formula? 2. Why does the

Dear all, I had a R segmentation fault, and then invoked debug mode and ran step by step. When I reached "terms(Y~X1*X2*...*X16)", I would then have "segmentation" fault. However, if I just ran this under regular "R interactive" mode, it would be fine though taking long time. My questions are: 1. Is there a known limit of terms for a formula? 2. Why does the

All, I am working on a multiple-regression meta-analysis and have too many alternative models to fit by hand. I am using the "metafor" package in R, which generates AIC scores among other metrics. I'm using a simple formula to define these models. For example, rma(Effect_size,variance, mods=~Myco_type + N.type +total, method="ML")->mod where Effect_size is the

my understanding of `eval' behaviour is obviously incomplete. my question: usually `eval(expr)' and `eval(expr, envir=parent.frame())' should be identical. why does the last `eval' (yielding `r3') in this code _not_ evaluate in the local environment of function `f' but rather in the global environment (if `f' is called from there)?

I initially thought, this should better be posted to r-devel but alas! no response. so I try it here. sory for the lengthy explanation but it seems unavoidable. to quickly see the problem simply copy the litte example below and execute f(n=5) which crashes. called with n != 5 (and of course n>3 since there are 3 parameters in the model...) everything is as it should be. in detail:

Dear list, I am trying to do an automated model selection of a glmm (function glmer; package: lme4) containing a large number of predictors. As far as i understand, glmulti is able to devide the process into chuncks and proceed by parallel processing on on multiple cores. Unfortunately this does not seem to work and i could not really fid any advice on the matter on other forums. Specifically i

Hello, I'm using the "glmulti" package to run models of all the possible combinations of my variables. However, I am only interested in a few interactions between them. I have tried the equivalent of: 1) mod1<-lm(y~a+b+c+a:b) glmulti(mod1, level=1) 2) mod2<-lm(y~a+b+c+a:b) glmulti(mod2, level=2) 3) glmulti("y", c("a",

I'm running r 2. on a mac running 10.6.4 and a dual-core macbook pro. I'm having a funny time with multicore. When I run it with 2 cores, mclapply, R borks with the following error. The process has forked and you cannot use this CoreFoundation functionality safely. You MUST exec(). Break on __THE_PROCESS_HAS_FORKED_AND_YOU_CANNOT_USE_THIS_COREFOUNDATION_FUNCTIONALITY___YOU_MUST_EXEC__()

dear list, I stumbled over the follwing strange behaviour/error when using `nls' which I'm tempted (despite the implied "dangers") to call a bug: I've written a driver for `nls' which allows specifying the model and the data vectors using arbitrary symbols. these are internally mapped to consistent names, which poses a slight complication when using `deriv' to

binequality looks like just the package I need, specifically for obtaining Gini income coefficients for binned data. I have upgraded to the latest version of R, the latest version of RStudio, and updated all packages. Still I have a persistent problem using fitFunc(). To keep things simple, I am testing things using the example code in the binequality manual, page 6 (or, "?fitFunc").

Hi, I am trying to fi a glm-poisson model to 400.000 records. I have tried biglm and glmulti but i have problems... can it really be the case that 400.000 are too many records??? I am thinking of using random samples of my dataset..... Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/glm-poisson-fitting-400-000-records-tp3925100p3925100.html Sent from the R help

Hello, I am working on my thesis and can't really figure out how to produce a reasonable graph from the output from my glm., I could just give the R-output in my results and then discuss them, but it would be more interesting if I could visualise what is going on. My research is how bees react to different fieldmargins, for this I have 4 different types of field margin (A,B,C & D) and

Hello MasteRs- Because I want to parallelize several calls to the glmulti package, what I'm essentially doing is trying to parallelize different calls to rJava. I'm using plyr functions which use foreach and then doMC which means multicore is my backend for parallelizing. I've tried several approaches to this but have not succeeded, i also find virtually no record of folks trying

Dear List, I am looking for a function that will find the best subset of negative binomial models. I have a large data set with 15 variables that I am interested in. I want an easy way to run all possible models and find a subset of the "best" models that I can then look at in more detail. I have found two functions that seem to provide what I am looking for, but am not sure which

Hi All, Its a bit of a beginners question I'm afraid. I have a looped stepwise regression (using MASS and StepAIC) to take random predictors out of the total number. For this example a random sample of 5 out of a total of 20. The loop will continue until all combinations of variables have been run through the loop. The output from each loop can be derived from taking the significant (p)