similar to: stepAIC error

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

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 =

Hi i am running a glm model family Gamma(link=log) trying to predict a vector of 1554 (real) values Using predict() i got a vector of 950 predicted values instead of 1554. The predictions are good though The model doesnt take account of negative values and NAs which are only 121 values. Any clue? Thank -- View this message in context:

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

Greetings to r-help land. I've run into some program crashes and I've traced them back to methods() behavior after the package gdata is loaded. I provide now a minimal re-producible example. This seems bugish to me. How about you? dat <- data.frame(x = rnorm(100), y = rnorm(100)) lm1 <- lm(y ~ x, data = dat) methods(class = "lm") ## OK so far library(gdata)

So I recognize that: 1. many people hate forward stepwise regression (i've read the archives)--but I need it 2. step() or stepAIC are two ways to get a stepwise regression in R But here's the thing: I can't seem to figure out how to specify that I want the criteria to be Mallow's Cp (and then to subsequently tell me what the Cp stat is). I know it has something to do with

I have a question about stepAIC and extractAIC and why they can produce different answers. Here's a stepAIC result (slightly edited - I removed the warning about noninteger #successes): stepAIC(glm(formula = (Morbid_70_79/Present_70_79) ~ 1 + Cohort + Cohort2, family = binomial, data = ghs_70_79, subset = ghs_70_full),direction = c("backward")) Start: AIC=3151.41

Full_Name: John E. Kolassa Version: Version 1.8.1 OS: Solaris Submission from: (NULL) (128.6.76.36) Stepwise model selection for coxph appears to fail with method="exact". The code step(coxph(Surv(1:100,rep(1,100))~factor(rep(1:4,25)),method="exact")) produces the error message Start: AIC= 733.07 Surv(1:100, rep(1, 100)) ~ factor(rep(1:4, 25)) Error in

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 <- function(fit, scale, k = 2,

Although the 'spatial' documentation doesn't mention that extractAIC works, it does seem to give an output. I may have misunderstood, but shouldn't the following give at least the same d.f.? > library(spatial) > data(topo, package="MASS") > extractAIC(surf.ls(2, topo)) [1] 46.0000 437.5059 > extractAIC(lm(z ~ x+I(x^2)+y+I(y^2)+x:y, topo)) [1]

Dear R users, Would it be difficult to change the code of stepAIC (from the MASS library) to use AICc instead of AIC? It would be great to know of someone has tried this already. Best wishes Christoph.

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

I am trying to perform a backwards stepwise variable selection with an mlogit model. The usual functions, step(), drop1(), and dropterm() do not work for mlogit models. Update() works but I am only able to use it manually, i.e. I have to type in each variable I wish to remove by hand on a separate line. My goal is to write some code that will systematically remove a certain set of variables

Hi Folks, I've got a question concerning the calculation of the Schwarz-Criterion (BIC) done by summary.regsubsets() of the leaps-package: Using regsubsets() to perform subset-selection I receive an regsubsets object that can be summarized by summary.regsubsets(). After this operation the resulting summary contains a vector of BIC-values representing models of size i=1,...,K. My problem

Hello, I am using a simple linear model and I would like to get an AIC value. I came across both AIC() and extractAIC() and I am not sure which is best to use. I assumed that I should use AIC for a glm and extractAIC() for lm, but if I run my model in glm the AIC value is the same if I use AIC() on an lm object. What might be going on? Did I interpret these functions incorrectly? Thanks,

Hi, I have sent this message to this list the July, 7th. It was about a problem in MASS package. Until now there is no change in the devel version. As the problem occurs in a package and not in the R-core, I don't know if the message should have been sent here. Anyway, I have added a copy to Pr Ripley. I hope it could have been fixed. Sincerely Marc Le 09/07/2017 ? 16:05, Marc Girondot via

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 *

Dear all, I am studying a bit the various support functions that exist for extracting information from fitted model objects. From the help files it is not completely clear to me whether the number returned by nobs() should be the same as the "nobs" attribute of the object returned by logLik(). If so, then there is a slight inconsistency in the methods for 'nls' objects with

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights