similar to: stepAIC() that can use new extractAIC() function implementing AICc

Displaying 20 results from an estimated 400 matches similar to: "stepAIC() that can use new extractAIC() function implementing AICc"

2002 Apr 01
something confusing about stepAIC
Folks, I'm using stepAIC(MASS) to do some automated, exploratory, model selection for binomial and Poisson glm models in R 1.3. Because I wanted to experiment with the small-sample correction AICc, I dug around in the code for the functions stepAIC dropterm.glm addterm.glm extractAIC.glm and came across something I just don't understand. stepAIC() passes dropterm.glm() a
2011 May 10
Help documentation in extractAIC
Hello. The sentence in extractAIC's help <> which discusses AIC's estimate of -2logL from RSS reads: "AIC only handles unknown scale and uses the formula n log (RSS/n) - n + n log 2pi - sum(log w) where w are the weights. Further AIC counts the scale estimation as a parameter
2005 Jan 26
Source code for "extractAIC"?
Dear R users: I am looking for the source code for the R function extractAIC. Type the function name doesn't help: > extractAIC function (fit, scale, k = 2, ...) UseMethod("extractAIC") <environment: namespace:stats> And when I search it in the R source code, the best I can find is in (R source root)/library/stats/R/add.R: extractAIC <-
2011 Aug 04
Use extractAIC in frailty cox model (estimated with coxph function, gaussian random effect) i obtaided > extractAIC(fit.cox.f) [1] 11.84563 8649.11736 but I don't know why I can't use the classic formulation of the AIC where the degree of freedom are the number of the parameter (in my case 3). -- View this message in context:
2014 Jun 26
AICc in MuMIn package
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
2006 Dec 12
Calculating AICc using conditional logistic regression
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
2011 Sep 04
AICc function with gls
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 Coefficients: (Intercept) therherbmax 1.6441405
2004 Dec 04
AIC, AICc, and K
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
2009 Jan 23
R stepping through multiplie interactions
I have a lm in R in the form model <- lm( Z ~ A*B*C*D,data=mydata) I want to run the model and include all interactions expect the 4 way (A:B:C:D) is there an easy way of doing this? I then want to step down the model eliminating the non-significant terms I understand step() does this but how would I do it by hand? -- View this message in context:
2013 Apr 16
Model ranking (AICc, BIC, QIC) with coxme regression
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
2006 Jul 12
AICc vs AIC for model selection
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
2005 Aug 15
stepAIC invalid scope argument
I am trying to replicate the first example from stepAIC from the MASS package with my own dataset but am running into error. If someone can point where I have gone wrong, I would appreciate it very much. Here is an example : set.seed(1) df <- data.frame( x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000) ) df$y <- 0.5*df$x1 + rnorm(1000, mean=8, sd=0.5) # pairs(df); head(df) lo <-
2008 Aug 01
drop1() seems to give unexpected results compare to anova()
Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <-, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are
2009 Jan 29
Inconsistency in F values from dropterm and anova
Hi, I'm working on fitting a glm model to my data using Gamma error structure and reciprocal link. I've been using dropterm (MASS) in the model simplification process, but the F values from analysis of deviance tables reported by dropterm and anova functions are different - sometimes significantly so. However, the reported residual deviances, degrees of freedom, etc. are not different.
2008 Nov 28
AIC function and Step function
I would like to figure out the equations for calculating "AIC" in both "step() function" and "AIC () function". They are different. Then I just type "step" in the R console, and found the "AIC" used in "step() function" is "extractAIC". I went to the R help, and found: "The criterion used is AIC = - 2*log L + k *
2012 Oct 30
error in lm
Hi everybody I am trying to run the next code but I have the next problem Y1<-cbind(score.sol,, score.pur) > vol.lm<-lm(Y1~1, data=vol14.df) > library(MASS) > stepAIC(vol.lm,~fsex+fjob+fage+fstudies,data=vol14.df) Start: AIC=504.83 Y1 ~ 1 Error in addterm.mlm(fit, scope$add, scale = scale, trace = max(0, trace - : no addterm method implemented for
2012 Feb 13
R's AIC values differ from published values
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
2002 Apr 28
dropterm() in MASS
To compare two different models, I've compared the result of using dropterm() on both. Single term deletions Model: growth ~ days + I(days^0.5) Df Sum of Sq RSS AIC <none> 2.8750 -0.2290 days 1 4.8594 7.7344 4.6984 I(days^0.5) 1 0.0234 2.8984 -2.1722 AND Single term deletions Model: growth ~ days + I(days^2) Df Sum
2009 Feb 25
indexing model names for AICc table
hi folks, I'm trying to build a table that contains information about a series of General Linear Models in order to calculate Akaike weights and other measures to compare all models in the series. i have an issue with indexing models and extracting the information (loglikehood, AIC's, etc.) that I need to compile them into the table. Below is some sample code that illustrates my
2003 Apr 28
stepAIC/lme problem (1.7.0 only)
I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250),