similar to: Help documentation in extractAIC

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, It is not critical but in case of coxph.null model (~1) extractAIC function generates Warning message: In is.na(fit$coefficients) : is.na() applied to non-(list or vector) of type 'NULL' As I understand it happens because of absent coefficients attribute. Function stats:::extractAIC.coxph Line edf <- sum(!is.na(fit$coefficients)) I think extra null-checking

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 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 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

Full_Name: Dave Ramsey Version: 1.8.0 OS: win2000 Submission from: (NULL) (202.27.240.6) there is a bug in extractAIC.survreg in library MASS. A survreg model object has no component called "residuals". Hence n <- length(fit$residuals) returns 0 resulting in errors workaround: replace n <- length(fit$residuals) with n <- length(residuals(fit)) ### sorry: error

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:

Dear list, I am confused about two functions in R: AIC(fm) and extractAIC(fm). What is the difference between two and when do I have to use one over the other? I have found the similar question previously and still not clear for me to understand. I also looked at '?AIC' and '?extractAIC' in R, which is also unclear. I pasted faked data set, fitting summary, and AICs. Thank

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,

By changing three lines in drop1 from access based on $ to access based on standard accessor methods (terms() and residuals()), it becomes *much* easier to extend drop1 to work with other model types. The use of $ rather than accessors in this context seems to be an oversight rather than a design decision, but maybe someone knows better ... In particular, if one makes these changes (which I am

Hello. I am trying to optimize a set of parameters using /optim/ in which the actual function to be minimized contains matrix multiplication and is of the form: SUM ((A%*%X - B)^2) where A is a matrix and X and B are vectors, with X as parameter vector. This has worked well so far. Recently, I was given a data set A of size 360440 x 1173, which could not be handled as a normal matrix. I

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 *

On Wed, Mar 11, 2015 at 1:23 PM, Hsiu-Khuern Tang <tangoh at gmail.com> wrote: > On Tue, Mar 10, 2015 at 10:47 PM, Avraham Adler <avraham.adler at gmail.com> wrote: >> On Wed, Mar 11, 2015 at 1:40 AM, Hsiu-Khuern Tang <tangoh at gmail.com> wrote: >>> On Tue, Mar 10, 2015 at 8:54 PM, Avraham Adler <avraham.adler at gmail.com> wrote: >>>>

On Tue, Mar 10, 2015 at 10:47 PM, Avraham Adler <avraham.adler at gmail.com> wrote: > On Wed, Mar 11, 2015 at 1:40 AM, Hsiu-Khuern Tang <tangoh at gmail.com> wrote: >> On Tue, Mar 10, 2015 at 8:54 PM, Avraham Adler <avraham.adler at gmail.com> wrote: >>> >>> I successfully rebuilt R-devel_2015-03-09 with the most recent version >>> of Rtools

>>>>> Avraham Adler <avraham.adler at gmail.com> >>>>> on Tue, 31 Jan 2017 16:07:20 -0500 writes: > On Tue, Jan 31, 2017 at 3:30 PM, peter dalgaard <pdalgd at gmail.com> wrote: >> >>> On 31 Jan 2017, at 18:56 , Avraham Adler <avraham.adler at gmail.com> wrote: >>> >>> Hello.

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get

I had a look at some influence measures, and it seems to me that currently several methods handle multiple lm (mlm) objects wrongly in R. In some cases there are separate "mlm" methods, but usually "mlm" objects are handled by the same methods as univariate "lm" methods, and in some cases this fails. There are two general patterns of problems in influence measures:

You can see how the appveyor build works here: https://github.com/rwinlib/base I suggest that you work off the build process in the rwinlib repository so you are starting from something that you know works and already incorporates the set of dependencies you need. On Fri, Feb 9, 2018, 5:33 AM Avraham Adler <avraham.adler at gmail.com> wrote: > On Fri, Feb 9, 2018 at 2:16 AM, Indrajit

R uses the C 'int' type for its integer data and that is pretty much universally 32 bit these days. In fact R wont' compile if it is not. That means the range for integer data is the integers in [-2^31, +2^31). It would be good to allow for a larger integer range for R integer objects, and several of us are thinking about how me might get there. But it isn't easy to get right, so