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
0
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 glm.fit stepAIC dropterm.glm addterm.glm extractAIC.glm and came across something I just don't understand. stepAIC() passes dropterm.glm() a
2011 May 10
0
Help documentation in extractAIC
Hello. The sentence in extractAIC's help <http://www.stat.psu.edu/~dhunter/R/html/stats/html/extractAIC.html> 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
2
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
0
extractAIC
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
0
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
1
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
2
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
1
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
2
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
0
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
2
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
2
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
5
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 <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are
2009 Jan 29
1
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
2
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
2
error in lm
Hi everybody I am trying to run the next code but I have the next problem Y1<-cbind(score.sol, score.com.ext, 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
2
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
2
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
3
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
2
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),