similar to: AICc function with gls

Hi I'm trying to replicate Smith et al.'s (http://www.sciencemag.org/content/330/6008/1216.abstract) findings by fitting their Gompertz and logistic models to their data (given in their supplement). I'm doing this as I want to then apply the equations to my own data. Try as a might, I can't quite replicate them. Any thoughts why are much appreciated. I've tried contacting the

Hi, sry if i hijack this, but maybe it's helpful for other gluster users... > pure NVME-based volume will be waste of money. Gluster excells when you have more servers and clients to consume that data. > I would choose LVM cache (NVMEs) + HW RAID10 of SAS 15K disks to cope with the load. At least if you decide to go with more disks for the raids, use several (not the built-in ones)

Dear all I have undertaken some phylogenetic and non-phylogenetic regressions with gls() in nlme with single preictor variables. A p value is associated with the intercept (upper p value) and another with the predictor variable (lower). Which p value is important? What does it mean if the intercept p value is insignificant but the predictor is still significant? Thanks a lot, and sorry for my

Hi Is it possible to use grofit to get the AIC of several (e.g. two) growth models and compare both these and model parameters? All I can get it to do so far is return parameters for a single model. Cheers Roland [[alternative HTML version deleted]]

Hi I'm trying to use ouch's hansen and brown functions but I get the error: > brown(logflatnodes,archotreeouch) Error in backsolve(l, x, k = k, upper.tri = upper.tri, transpose = transpose) : NA/NaN/Inf in foreign function call (arg 1) and with hansen also: Error in optim(par = c(sqrt.alpha, sigma), fn = function(par) { : function cannot be evaluated at initial parameters

Hello there, as Strahil suggested a separate thread might be better. current state: - servers with 10TB hdds - 2 hdds build up a sw raid1 - each raid1 is a brick - so 5 bricks per server - Volume info (complete below): Volume Name: workdata Type: Distributed-Replicate Number of Bricks: 5 x 3 = 15 Bricks: Brick1: gls1:/gluster/md3/workdata Brick2: gls2:/gluster/md3/workdata Brick3:

Actually, pure NVME-based volume will be waste of money. Gluster excells when you have more servers and clients to consume that data. I would choose? LVM cache (NVMEs) + HW RAID10 of SAS 15K disks to cope with the load. At least if you decide to go with more disks for the raids, use several? (not the built-in ones) controllers. @Martin, in order to get a more reliable setup, you will have to

Dear List: First, many thanks to those who offered assistance while I constructed code for the simulation. I think I now have code that resolves most of the issues I encountered with memory. While the code works perfectly for smallish datasets with small sample sizes, it arouses a windows-based error with samples of 5,000 and 250 datasets. The error is a dialogue box with the following: "R

Hi, I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below). #Model1 >spring.cox <- coxme (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2+(R3|Individual), spring) I've already found some explications in

I have a case-control study that I'm analysing using the conditional logistic regression function clogit from the survival package. I would like to calculate the AICc of the models I fit using clogit. I have a variety of scripts that can calculate AICc for models with a logLik method, but clogit does not appear to use this method. Is there a way I can calculate AICc from clogit in R? Many

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

How can I extract K (number of parameters) from an AIC calculation, both to report K itself and to calculate AICc? I'm aware of the conversion from AIC -> AICc, where AICc = AIC + 2K(K+1)/(n-K-1), but not sure of how K is calculated or how to extract that value from either an AIC or logLik calculation. This is probably more of a basic statistics question than an R question, but I thank

Dear R-helpers I have generated a suite of GLMs. To select the best model for each set, I am using the meta-analysis approach of de Luna and Skouras (Scand J Statist 30:113-128). Simply put, I am calculating AIC, AICc, BIC, etc., and then using whichever criterion minimizes APE (Accumulated Prediction Error from cross-validations on all model sets) to select models. My problem arises where I

Dear members of the list, I'm fitting poisson regression models using stepAIC that appear to be overparametrized. I would like to know if there is the possibility of fitting models by steps but using the AICc instead of AIC. Best wishes German Lopez

Hello List, I was wondering if a package or piece of code exists that will allow all possible subsets regression model selection within program R. I have already looked at step(AIC) which does not test differing combinations of variables within a model as far as I can tell. In addition I tried to use the leaps command, but that does not use the criterion I am looking for. Any help or advice

I am fitting logistic regression models, by defining my own link function, and would like to get AICc values. Using the glm command gives a value for AIC, but I haven't been able to get R to convert that to AICc. Is there a code that has already been written for this? Right now I am just putting the AIC values into an excel spreadsheet and calculating AICc, likelihood, and AIC

Hi I'm trying to fit arima models with the arima() function and I have two questions. ###### ##1. ## ###### I have n observations for my time series. Now, no matter what arima(p,d,q)- model I fit, I always get n residuals. How is that possible? For example: If I try this out myself on an AR(1) and calculate the fitted values from the estimated coefficients I can calculate n-1 residuals.

Hi, I'm fitting poisson regression models to counts of birds in 1x1 km squares using several environmental variables as predictors. I do this in a stepwise way, using the stepAIC function. However the resulting models appear to be overparametrized, since too much variables were included. I would like to know if there is the possibility of fitting models by steps but using the AICc

I would like test AICc as a criteria for model selection for a glm using stepAIC() from MASS package. Based on various information available in WEB, stepAIC() use extractAIC() to get the criteria used for model selection. I have created a new extractAIC() function (and extractAIC.glm() and extractAIC.lm() ones) that use a new parameter criteria that can be AIC, BIC or AICc. It works as

Hi, I am using 'best.arima' function from forecast package to obtain point forecast for a time series data set. The documentation says it utilizes AIC value to select best ARIMA model. But in my case the sample size very small - 26 observations (demand data). Is it the right to use AIC value for model selection in this case. Should I use AICc instead of AIC. If so how can I modify