search for: glmulti

Dear List, I'm using glmulti for model averaging in R. There are ~10 variables in my model, making exhaustive screening impractical - I therefore need to use the genetic algorithm (GA) (call: method = "g"). I need to include random effects so I'm using glmulti as a wrapper for lme4. Methods for doing this...

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(&...

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 th...

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, alway...

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 line will compute the missing data: tempData <...

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

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 have the following questions: 1) does parallel processing only work for...

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...

...type + N.type +total, method="ML")->mod where Effect_size is the mean response for each experiment, variance is the SE around the mean, mods are the variables, and the method used to fit the AIC score is maximum likelihood estimation. I thought I might be able to use the function glmulti(mod, level=1) to rank all alternative models using AICc scores. If I fit a glm in in this way (e.g. glm(Effect_size~Myco_type+N.type+total)->mod), the glmulti function works beautifully. Is it possible to extract AIC scores from the rma models as well? Is there another package that might...

...itting 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 'glmulti' R package. This produces these errors: /> model.2.10 <- glmulti(as.factor(dependent) ~ predictor_1*predictor_2*predictor_3*predictor_4*predictor_5*predictor_6*predictor_7*predictor_8*predictor_9, data = database, fitfunc = clm, link = "probit", method = "g", crit =...

...You MUST exec(). Break on __THE_PROCESS_HAS_FORKED_AND_YOU_CANNOT_USE_THIS_COREFOUNDATION_FUNCTIONALITY___YOU_MUST_EXEC__() to debug. If, however, I crank the # of cores back to 1, it runs just fine. The code looks as follows: mmi_fits<-mclapply(responses, function(a_response){ gmult<-glmulti(glm(make_formula(a_response, sp), data=df, family=binomial)) return(gmult) }, mc.cores=2) -Jarrett

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...

...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 one (if either) are appropriate. glmulti() in package glmulti does an exhaustive search of all models and gives a number of candidate models to choose from based on your choice of Information Criterion. This seems to be exactly what I am after, but I found nothing about it on this list which makes me think there is some reason no one is...

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 he...

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)

hello all, I have a simple linear model with 4/5 variables that I am trying to fit. I would like to find the lowest AIC value with any combination of all the variables. I would like to implement this with a while/for loop. Possibly I would like to generalize this so then I can use it when I have many more variables. I do not want to use step AIC. At the moment I am doing it manually but I

...ndrea ospina valencia <zphya@yahoo.es> > Cc: "r-help-es@r-project.org" <r-help-es@r-project.org> > Date: Sat, 7 Apr 2012 00:27:55 +0200 > Subject: Re: [R-es] grafica modelo logit y probit > Hola, > > ¿ Has probado con los paquetes: "effects" y "glmulti" ? > > Saludos, > Carlos Ortega > www.qualityexcellence.es > > > El 6 de abril de 2012 21:00, sofia andrea ospina valencia > <zphya@yahoo.es>escribió: > > > Buenas noches > > > > Por favor me pueden indicar cual es la sentencia para realizar l...

...meters, that predicted the two species well (p-values <0.05, and >95% sucessfull bootstrapping). 3) I wrote down all the multiple models with decent p-values and calculated AICc ( AICc is for small data sets, and should be used anyway as for very large N AIC almost equals AICc) (the package glmulti does all the combination models and you can set limits on number of parameters or interactions etc) 4) I manually calculated the weigth based on the AICc of each model with proper performance. This gives you a good idea of which one the best model is and how good that model is compared to all the...

CRAN (and crantastic) updates this week New packages ------------ * DCGL (1.0) Bao-Hong Liu http://crantastic.org/packages/DCGL Functions for basic differential coexpression analyses: gene filtering, link filtering, DCG (Differentially-Coexpressed Gene) identification and DCL (Differentially-Coexpressed Links) identification.Two algorithms,named DCP and DCe, are provided for