similar to: MuMIn Problem getting adjusted Confidence intervals

Dear User R It's been a pleasure talking with you. I am newcomer use R. Would you please help me how to translate the script below to "R" script? * Area under receiver operating characteristic (AU-ROC) predict r1m1p, p roctab malaria r1m1p, graph summary * Area under receiver operating characteristic (AU-ROC) curve predict r1m2p, mu roctab malaria r1m2p, graph summary

*Dear Bert, * Thank you very much for your feedback and the useful link https://rseek.org/ and https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/. Actually, I want to know different performance between Stata and R, in multilevel logistic regression. For this purposes, I replicate ".do" file use Stata in

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

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 models (based on

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<-

I just downloaded version 1.0.0 and several binary libraries (VR, rpart, norm, stataread) - WinNT version. I then converted a file from Stata 6.0 to R format by using the stataread library. The file converts perfectly and I was able to use the VR function lda on the dataframe without difficulty. I then tried to use the same dataframe with RPART. The model statement:

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

I have a list of scientific names in a data set. I would like to split the names into genus, species and subspecies. Not all names include a subspecies. Could someone show me how to do this? My example code is: a <- matrix(c('genusA speciesA', 10, 'genusB speciesAA', 20, 'genusC speciesAAA subspeciesA', 15,

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,

To account for likely differences between families in naturalization rates, we fitted a generalized linear mixed model, using PROC NLMIXED in SAS10, with the naturalization rate per genus (that is, the number of naturalized species in a genus as a proportion of the total number of introduced species in a genus) as the response variable, a variable coding genera as containing at least one native

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,

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

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

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

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.

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'

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,

[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 +

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,