search for: 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...

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 m...

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 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' [dredge]. The data file is large (585000 rows) and has no NA, -9999 or blan...

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...

Hi, I have a GAM quasipoisson that I'd like to run through MuMIn package - dredge - gettop.models - model.avg However, I'm having no luck with script from an example in MuMIn help file. In MuMIn help they advise "include only models with smooth OR linear term (but not both) for each variable". Their example is: # Example with gam mode...

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<- 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...

[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$k.score + central$seen.in.wild + central$captivity + central$had.damage + central$importance.of.being.informed + central$RC1+ central$RC2 + centra...

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. Firstly it is a mixed model that I am running with lme4. The .csv file that it comes from has more factors than I will probably use but I only listed the ones that I wanted to test. I had also coded the on...

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 g...

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 un...

...1) - sum(NTIES.CI^3 - NTIES.CI)/((n.x + n.y) * (n.x + n.y - 1)))) dz <- (dz - CORRECTION.CI)/SIGMA.CI abs(dz - zq) } To examine the behivour I plotted the course of the wdiff for a lot of d and three zq (0.05, 0.5, 0.95) and I let optimize to compute the minimums. Then I plotted it. mumin<-min(x)-max(y) mumax<-max(x)-min(y) lll<-seq(mumin,mumax,by=0.01) wdl<-apply(cbind(lll),1,wdiff,zq=qnorm(0.05)) wdm<-apply(cbind(lll),1,wdiff,zq=qnorm(0.50)) wdu<-apply(cbind(lll),1,wdiff,zq=qnorm(0.95)) plot(lll,wdl,type="l") lines(lll,wdm,lty=4) lines(lll,wdu,lty=7)...

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 onl...

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 numb...

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...

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 of the variables. But isn't that the same? I might have a mental block on this. Which model (formula) would dredge() "test" tha...

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, family="gaussi...

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 crosses to the right): Model selection table (Int) all.rLD bng INF all.rLD:bng all.rLD:INF bng:INF all.rL...

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 (please see bellow). Also the logLik changes (as expected). > logLik (glmmadmb.Tot.P...

...;m not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request, if anyone has example code for using gam in a multimodel inference framework, especially with bivariate smooths, I'd be most grateful. Cheers and Thanks in Advance Mike require(MuMIn) data(Cement) # option 1, create model.selection object using dredge fm0 <- lm(y ~ ., data = Cement) print(dd <- dredge(fm0)) fm1 <- lm(formula = y ~ X1 + X2, data = Cement) fm2 <- lm(formula = y ~ X1 + X2 + X4, data = Cement) fm3 <- lm(formula = y ~ X1 + X2 + X3, data = Cement) fm4...