search for: aiccmodavg

...h phylogenetic uncertainty taken into account and thereby including 100 potential phylogenetic tree scenarios. I've managed to run models on all of the different trees and performed model averaging to get parameter estimates for the intercept and most of the predictor variable slopes using the *AICcmodavg* package and *APE*, but I seem to get stuck with some of the variables that are also included in a two-way interaction in the model. I can obtain values for the two-way interaction but not for the variables separately. The error I receive is: ?Error in modavg.AICgls(parm = "agefirstbreed&quo...

What is teh reason some functions in the AICcmodavg package do not work with 'mer' class models? One such example would be the 'importance' function. Thanks Ronny -- View this message in context: http://r.789695.n4.nabble.com/AICcmodavg-functions-and-mer-class-models-tp3714534p3714534.html Sent from the R help mailing list archive...

...e (?mod? , 1:length(buco.models), sep=") aictab(cand.set = buco.models, modnames = Modnames, sort=TRUE)) print(aictab(cand.set = buco.models, modnames = Modnames, sort=TRUE) digits = 4) Thank you in advance. Kerry Griffis-Kyle -- View this message in context: http://r.789695.n4.nabble.com/AICcmodavg-tp4643016.html Sent from the R help mailing list archive at Nabble.com.

...m ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias correction factor 2*K*(K+1)/(n-K-1) to give the AICc value of 69.312 (addition of 5, where n=13 and K=4). This same value, 69.31, can be obtained using R package AICcmodavg library ( AICcmodavg ) data (cement) cement Cand.models <- list( ) Cand.models[[1]] <- lm ( y ~ x1 + x2, data = cement ) Cand.models[[2]] <- lm ( y ~ x3 + x4, data = cement ) Cand.models[[3]] <- lm ( y ~ x1 + x2...

Dear List, I am trying to do model averaging for a negative binomial model using the package AICcmodavg. I need to use glm() since the package does not accept glm.nb() models. I can get glm() to work if I first run glm.nb and take theta from that model, but is there a simpler way to estimate theta for the glm model? The two models are: mod.nb<-glm.nb(mantas~site,data=mydata) mod.glm<-g...

...ject has disappeared. It's late on a Friday so I may be overlooking something obvious, but I'd appreciate your help if you can see what I'm doing wrong. Many thanks, Mark Na rm(list=ls()) #LOAD package, data, and keep clean copies of data library(reshape2); library(MuMIn);library(AICcmodavg) birdmatrix<-read.csv("birdmatrix.csv",stringsAsFactors=FALSE); birdmatrixclean<-birdmatrix habitatmatrix<-read.csv("habitatmatrix.csv",stringsAsFactors=FALSE); habitatmatrixclean<-habitatmatrix traits<-read.csv("traits.csv",stringsAsFactors=FALSE); trai...

CRAN (and crantastic) updates this week New packages ------------ Updated packages ---------------- New reviews ----------- This email provided as a service for the R community by http://crantastic.org. Like it? Hate it? Please let us know: cranatic at gmail.com.

I am using lm simulation in R and try to find the AICc and Akaike weight of the model. I searched out that using package "AICcmodavg" AICc is easily to get. I wonder how can I get the "Akaike weight", any function I can use to generate it? Thanks in advance. Sunny [[alternative HTML version deleted]]

Dear all, I am making model selection of generalised linear models based on delta AIC, using the command step(AIC). Also, I would like to learn the explanatory powers of each independent variable. To phrase differently, it is in need to show the delta AIC of all models rather than the final model with the least delta AIC. Please kindly advise if it is possible to exhibit all delta AIC of

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

Dear R users, I would like to fit a glm with Poisson distribution and log link with a known dispersion parameter. I do not want to estimate the dispersion parameter. I know what it is, so I simply want to fix it at a constant for this and other models to follow. My simple, no covariate model is: Tall.glm<-glm(Seedling~1, family=poisson, offset(log(area)), data=tallPSME.df) I want to

...nclear what this means and how to resolve the issue. If anyone has any idea how to address this error, I would very much appreciate your response. Thank you in advance. Jeremy My script is as follows : ######################## ## LOAD PACKAGES library(MASS) library(MuMIn) library(nnet) library(AICcmodavg) Jdata<- read.delim("/Analysis/20120709 JLittle data file.txt", header=T) attach(Jdata) names(Jdata) ##NNET ##MULTINOMIAL LOGISTIC REGRESSION ##multinom(formula, data, weights, subset, na.action, contrasts = NULL, ##Hess = FALSE, summ = 0, censored = FALSE, ##model = FALSE, ...) #...

...he package 'plotrix' by Jim Lemon et al. * TunePareto (1.0) Hans Kestler http://crantastic.org/packages/TunePareto Generic methods for parameter tuning of classification algorithms using multiple scoring functions Updated packages ---------------- ade4 (1.4-16), adlift (1.2-3), AICcmodavg (1.08), aqp (0.94-1), aspace (2.5), aspace (2.4), BioStatR (1.0.2), bnlearn (2.2), caret (4.54), caret (4.53), clustTool (1.6.5), coarseDataTools (0.3), constrainedKriging (0.1.2), DEMEtics (0.8.1), emdbook (1.2.2.1), FitAR (1.80), fpc (2.0-2), futile.paradigm (1.0.1), futile.paradigm (1.0.2), gaml...

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

CRAN (and crantastic) updates this week New packages ------------ * disclapmix (0.1) Maintainer: Mikkel Meyer Andersen Author(s): Mikkel Meyer Andersen and Poul Svante Eriksen License: GPL-2 http://crantastic.org/packages/disclapmix disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. * EstSimPDMP (1.1) Maintainer: Unknown Author(s):

Is there a function that will allow me to run all model iterations if I specify a full model? I am using information criteria to choose between possible candidate models. I have been writing out all possible model combinations by hand, and I am always worried that I am missing models or have made a mistake somewhere. It is also difficult to alter models if I want to change a term. For example,

...;Tim Clark" <mudiver1200 at yahoo.com> > Cc: r-help at r-project.org > Date: Monday, May 3, 2010, 7:50 PM > On Mon, 3 May 2010, Tim Clark wrote: > > > Dear List, > > > > I am trying to do model averaging for a negative > binomial model using the package AICcmodavg.? I need to > use glm() since the package does not accept glm.nb() > models.? I can get glm() to work if I first run glm.nb > and take theta from that model, but is there a simpler way > to estimate theta for the glm model?? The two models > are: > > > > mod.nb<-glm.n...