similar to: AICcmodavg

Using the Cement hardening data in Anderson (2008) Model Based Inference in the Life Sciences. A Primer on Evidence, and working with the best model which is lm ( y ~ x1 + x2, data = cement ) the AIC value from R is model <- lm ( formula = y ~ x1 + x2 , data = cement ) AIC ( model ) 64.312 which can be converted to AICc by adding the bias

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 at Nabble.com.

Dear moderator, If possible I would like to send in the following question for R-help: I am analyzing a small data set using PGLS with 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

With new kernel, appeared some problems with build proccess here. After many minutes waiting for rpmbuild were over, a gpg problem appears!!: ... scripts/modsign/mod-extract.c:311: warning: format '%lx' expects type 'long unsigned int', but argument 3 has type 'Elf64_Xword' + KEYFLAGS='--no-default-keyring --homedir ..' + KEYFLAGS='--no-default-keyring

I downloaded the src rpm for CentOS 5 and tried to recompile it. It failed with the following message: + sh ./scripts/modsign/modsign.sh /var/tmp/kernel-2.6.18-8.el5-root /lib/modules/2.6.18-8.el5PAE/kernel/drivers/char/watchdog/pcwd_pci.ko CentOS gpg: WARNING: unsafe permissions on homedir `..' + mv -f

Hi folks, I'm not sure if this is a bug or not so I thought I'd check here first. I came across it while working on an update to my package where I try to get AICtab slot from the summary of an lmer object ( summary(my_lmer)@AICtab ). The attached contains a minimal example, where the code below will work if you load the f() function by sourcing the package's R code but will throw an

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

Hello everybody, I have a problem with accessing class attributes. I think it might be due to namespace access restrictions or something similar, but I'm unable to figure it out, maybe someone knows the trick how to do it ... I'm trying to extract some information from the summary object for a model fitted using the lmer function from the lme4 package: smry <- summary(lmer(foo

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)

Dear R users I have a question related to the interpretation of results based on GAMMs using Simon Woods package gamm4. I have repeated measurements (hours24) of subjects (vpnr) and one factor with three levels (pred). The outcome (dv) is binary. In the first model I'd like to test for differences among factor levels (main effects only): gamm.11<-gamm4(dv ~ pred +s(hours24), random = ~

Hi, I have been having a tedious issue with lmer models with lots of factors and lots of levels. In order to get the basic information at the beginning of the print out I also have to generate these enormous tables as well. Is there a method command to leave off all of the effects and correlations? Or, do I have to go to string commands?

Dear R-helpers, I have run the code below which I expected to make an object called dd1, but that object does not exist. So, in summary, my problem is that my function is meant to make an object (dd1), and it does indeed make that object (I know that the last line of the function prints it out) but then, after the function has run, the object has disappeared. It's late on a Friday so I may

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.

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

I can't find a way to retrieve z values and p-values from the output from lmer in the lme4 package. How is this done? Rick B.

Odette Gaston <odette.gaston <at> gmail.com> writes: > > Dear all, > > I have a problem with accessing class attributes. > I was unable to solve this > yet, but someone may know how to solve it. My best guess at your immediate problem (doing things by hand) is that you're not using the whole vector. From your example: Delta <- c(m1 = 0, m2 = 1.8, m3 =

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