search for: aicc

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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 weight...

Dear all, I have tried to modify the code of MASS:::stepAIC.default(), dropterm() and addterm() to use AICc instead of AIC for model selection. The code is appended below. Somehow the calculations are still not correct and I would be grateful if anyone could have a look at what might be wrong with this code... Here is a working example: ## require(nlme) model1=lme(distance ~ age + Sex, data = Orthodon...

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...

...ce 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 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[[...

...everal 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 instead of AIC. Or at least I wonder if it would be possible to save the AIC value and number of parameters of models fitted in each step and to calculate AICc afterward. Help on this will be very much appreciated German Lopez

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use optimize(), or do a simple line search on span=, but I'm not sure how to use loess.aic to write a function that would act as a wrapper for loess() and return the mimim...

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 you for your he...

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 thanks, Katherine Boughey -- School of Environmental Sciences Univers...

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.Pr.nb) ---- 12 in model selecti...

...od.glm} \alias{selMod.list} \title{ Model selection according to information theoretic methods } \description{ Handles lm, glm and list of models lm, glm, lme and nlme objects and provides parameters to compare models according to Anderson et al. (1998) } \usage{ selMod(aModel, Order = "AICc", ...) group method selMod.lm(aModel, Order = "AICc", dropNull = FALSE, selconv=TRUE, ...) selMod.list(aModel, Order = "AICc", ...) } \arguments{ \item{aModel}{ a lm or glm model or a list of lm or glm models } \item{dropNull}{ if TRUE, drops the simplest mode...

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 have noticed my rankings from BIC and AICc are exactly inverse. I fear this behaviour is a result of my coding as follow...

Hi I get the following error when I try and get the AICc for a gls regression using qpcR: > AICc(gls1) Loading required package: nlme Error in n/(n - p - 1) : 'n' is missing My gls is like this: > gls1 Generalized least squares fit by REML Model: thercarnmax ~ therherbmax Data: NULL Log-restricted-likelihood: 2.328125 Coefficient...

...m 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 best.arima function to change the selection creteria? Any pointers would be of great help. Thanx in advance. Sachin --------------------------------- [[alternative HTML version deleted]]

...think they're away. The equations as I've used them are: log(mass)~log(K)-log(K/M)*exp(-a*t) and log(mass)~C*t^G The data file I've been using is attached. It starts at the K-Pg boundary with a body size of 3.3kg, following their description in the supplement. Their parameters and AICc values are given in their paper. I get something quite close for some of them (~0.245 G, their gamma, K estimates reasonable etc.), but in the logistic model C is more like 3 than 1.5, and the AICc values differ by ~10 rather than ~1. Cheers. Roland

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

...uot;numeric") E<-vector(mode="numeric") for (i in 2:30){ fitted[i]<-a*(X[i-1]-mu)+mu E[i]<-X[i]-fitted[i] } fitted E # Innovations residuals(model10) # Compare with the residuals from the arima() model ###### ##2. ## ###### I want to calculate the AICc Value for model selection. Is there a way to calculate the AICc from the model output without doing it manually. I guess I could write a function myself somehow but it's always nice to have an inbuilt function with which to compare what one does. I know that there has been some discussion o...

...l. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df LL AIC AICc BIC 1 1000 20 1 210 170 40 -5.192975386 90.38595 93.80618 286.6962 2 1000 20 2 210 151 59 -3.474172303 124.94834 132.48026 414.5059 3 1000 20 3 210 133 77 -1.745821627 157.49164 170.51984 535.3888 4 1000 20...

...l. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df LL AIC AICc BIC 1 1000 20 1 210 170 40 -5.192975386 90.38595 93.80618 286.6962 2 1000 20 2 210 151 59 -3.474172303 124.94834 132.48026 414.5059 3 1000 20 3 210 133 77 -1.745821627 157.49164 170.51984 535.3888 4 1000 20...

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 i...