similar to: GAM quasipoisson in MuMIn

Hi, Got my issues sorted. Error message solved: I heard from the guy who developed MuMIn and his suggestion worked. "As for the error you get, it seems you are running an old version of MuMIn. Please update the package first." I did (I was only 1 version behind in both R and in MuMIn) and error disappeared! Running quasipoisson GAM in MuMIn: As for my questions on GAM and " to

Hi All, I'm having trouble with the automatized model generation (dredge) function in the MuMIn package. I'm trying to use it to automatically generate subsets of models from a global cox proportional hazards model, and rank them based on AICc. These seems like it's possible, and the Mumin documentation says that coxph is supported. However, when I run the code (see below), it gives

Dear list, I am trying to use MuMIn to compare all possible mixed models using the dredge function on binomial data but I am getting an error message that I cannot decode. This error only occurs when I use glmer. When I use an lmer analysis on a different response variable every works great. Example using a simplified glmer model global model: mod<- glmer(cbind(st$X2.REP.LIVE,

Hello R community, I am attempting to run multiple logistic regressions (multinomial, via package 'nnet'), with Automated Model Selection (dredge, package 'MuMIn'). The aim is to reduce the number of predictor variables by assessing relative performance of each variable, which can be done in a coarse fashion using the Automated Model Selection option in package 'MuMIn'

Hi all, I'm having trouble with the model generating 'dredge' function in the MuMIn 'Multi-model Inference' package. Here's the script: globalmodel<- glm(TB~lat+protocol+tested+ streams+goats+hay+cattle+deer, family="binomial") chat<- deviance(globalmodel)/59 #There we 59 residual degrees of freedom in this global model. models<- dredge(globalmodel,

Hello, I try to understand the different approaches how to select the best fit regression model. This is not about AIC, BIC, etc. It is about the difference between the steps() function (in stats package) and the dredge() function (in MuMIn) package. I see several examples on the internet. step() explore the model space with a step wise approach. And dredge() try out all possible combinations

Dear List, I want to ask a AIC question based on package library(MuMIn) The relative importance of 16 explanatory variables are assessed using delta AIC in a generalized linear model. Please kindly advise if it is possible to show models with any two only certain variables. Thank you. Elaine I asked a similar question and got a great help for models with only one variable as below.

The subject says it all really. Question 1. Here is some code created to illustrate my problem, can anyone spot where I'm going wrong? Question 2. The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,

Hello, I hope that you can bare with me. I am new to models, but I think I have a pretty godd understanding of how to run them now, including how to use AICc and Anova. The issue is that I have many factors that I wish to compare so doing each one at a time would take forever. I came across the MuMIn package and I was so excited, however I am getting an error message and i don't know why.

Hello, I am using package MuMIn to calculate AIC for a full model with 10 explanatory variables. Thanks in advance in sharing your experience. Q1 In the AIC list of all models, each model is differentiated by model number. Please kindly advise if it is possible to find the corresponding explanatory variable(s) for the model number. Q2 error message I tried to display sub-model with only

Hello I am not able to use the MuMIn package (version 1.7.7) for multimodel inference with a GLM Negative Binomial model (It does work when I use GLM Poisson). The GLM Negative Binomial gives the following error statement: Error in get.models(NBModel, subset = delta < 4) : object has no 'calls' attribute Here is the unsuccessful Negative Binomial code. > > BirdNegBin

Hello R users I'm using MuMIn but for some reason I'm not getting the adjusted confidence interval and uncoditional SE whe I use model.avg(). I took into consideration the steps provided by Grueber et al (2011) Multimodel inference in ecology and evolution: challenges and solutions in JEB. I created a global model to see if malaria prevalence (binomial distribution) is related to any

Hi all, My replies within the forum aren't getting approved, though my emails always go through, so here is my reply to a question I previously posted (all questions and answers shown). Thanks, Cat I'm having trouble with the model generating 'dredge' function in the MuMIn 'Multi-model Inference' package. Here's the script: globalmodel<-

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

hello everyone, I'm modelling in lmer an average chick weight defined as "Total.brood.mass ~ offset(chick.number), with three fixed and two random effect. Next, I want to use function dredge from MuMIn package for model averaging. Not sure why, but in consequence the offset variable is treated as a predictor, so I get a table that mixes models with and without that offset term (the first

[cc'ing back to r-help: this is good etiquette so that the responses will be seen by others/ archived for future reference.] On 10-08-25 04:35 PM, Marino Taussig De Bodonia, Agnese wrote: > Yes, I meant "MuMIn" > > the global formula I introduced was: > > rc4.mod<-lm(central$hunting~ central$year + central$gender + central$hunter + central$k.score +

Hi All: the package "MuMIn" can be used to select the model based on AIC or AICc. The code is as follows: data(Cement) lm1 <- lm(y ~ ., data = Cement) dd <- dredge(lm1,rank="AIC") print(dd) If I want to select the model by BIC, what code do I need to use? And when to select the best model based on AIC, what the differences between the function "dredge" in

Hello, I am modelling in glmmADMB count data (I´m using a negative binomial distribution to avoid possitive overdispersion) with four fixed and one random effect. I´m also using MuMIn package to calculate the AICc and also to model averaging using the function dredge. What I do not understand is why dredge calculates a different value of the AICc and degrees of freedom than the function AICc

Hola a todos, He realizado un dredge (para obtener todos los modelos GAM posibles a parir de un full model), luego he seleccionado un confidence set (los modelos que no se diferencian en 2 en AIC) y he hecho un model averaging con ese confidence set. Ahora me gustaría aplicar ese modelo "average" ajustado sobre otro set de datos pero no se como especificar en R que use el mismo modelo

I am struggling to get GAM model predictions from the top models calculated using model.avg in the package "MuMIn". My model looks something like the following: gamp <- gam(log10(y)~s(x1,bs="tp",k=3)+s(x2,bs="tp",k=3)+ s(x3,bs="tp",k=3)+s(x4,bs="tp",k=3)+s(x5,bs="tp",k=3)+ s(x6,bs="tp",k=3)+x7,data=dat,