similar to: S3 and S4 clash

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

>>>>> "MM" == Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Tue, 18 May 2010 12:37:21 +0200 writes: >>>>> "GaGr" == Gabor Grothendieck <ggrothendieck at gmail.com> >>>>> on Mon, 17 May 2010 09:45:00 -0400 writes: GaGr> BIC seems like something that would logically go into stats

The symptom triggering this email is that an S4 summary method sometimes refuses to be invoked, even when a package is explicitly loaded, if the first load of the package is implicit. It may or may not be specific to 'summary' methods and/or the 'VGAM' package. I've sent to R-devel because (i) it looks like some kind of bug to me, but I'm not sure; (ii) it's not

Can anyone help me figure out the right way to import a method that is defined as S3 in one package and S4 in another? Specifically: profile() is defined as an S3 method in the stats package: function (fitted, ...) UseMethod("profile") <bytecode: 0xa4cd6e8> <environment: namespace:stats> In stats4 it is defined as an S4 method: stats4:::profile standardGeneric for

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 *

R 2.3.0, all packages up to date Linux, SuSE 10.0 Hi I want to calculate AIC or BIC from several results from mle calculation. I found the AIC function, but it does not seem to work with objects of class mle - If I execute the following: ml1 <- mle(...) AIC(ml1) I get the following error messale: Error in logLik(object) : no applicable method for "logLik" Therefore I am using the

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

pkgA's NAMESPACE has importFrom(graphics, plot) exportClasses("A") exportMethods("plot") R/foo.R has setClass("A") setMethod("plot", "A", function(x, y, ...) {}) During R CMD INSTALL pkgA_1.0.tar.gz we are told ** preparing package for lazy loading Creating a generic function for 'plot' from package

Hello- I understand that it's convention, when comparing two models using the anova function anova(model1, model2), to put the more "complicated" (for want of a better word) model as the second model. However, I'm using lme in the nlme package and I've found that the order of the models actually gives opposite results. I'm not sure if this is supposed to be the case

Dear developers, the implementation of S4 generics for existing S3 ones in the base package is concerned to be a threat to quick startup times [1]. But since S4 is promoted, and S3/S4 interoperability a pain when package developing [2], are there efforts to improve the situation? E.g. an S3 free system, etc. A good thing [2] is the package 'stats4', including some setGeneric calls (e.g.

Hi, on Windows XP Pro with R version 2.7.1 Patched (2008-06-27 r46012) the 'Cairo' and the 'EBImage' packages does not play well together. Loading EBImage before Cairo cause the following to happen: # Rterm --vanilla > library(EBImage); > library(Cairo) Error in inDL(x, as.logical(local), as.logical(now), ...) : unable to load shared library

Dear R-help gurus (and T.Yee, the VGAM maintainer) - I've been banging my head against the keyboard for too long now, hopefully someone can pick up on the errors of my ways... I am trying to use optim to fit a zero-inflated negative binomial distribution. No matter what I try I can't get optim to recognize my initial parameters. I think the problem is that dnbinom allows either

I'm using R 2.0.1. I looked in the email archives but didn't see anything on this topic. I've noticed a surprising (to me) difference between AIC and BIC: > methods("AIC") [1] AIC.default* AIC.logLik* > methods("BIC") [1] BIC.gls* BIC.lm* BIC.lme* BIC.lmList* BIC.logLik* BIC.nls* The BIC.gls BIC.lm BIC.lme BIC.lmList and BIC.nls functions appear

Hi, How can we set the environment for the minuslog function in mle()? The call in this code fails because the "ll" function cannot find the object 'y'. Modifying from the example in ?mle: library(stats4) ll <- function(ymax=15, xhalf=6) { -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) } fit.mle <- function(FUN, x, y) { loglik.fun <- match.fun(FUN)

Dear all, I've noticed by trying to download gz files from here : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM907811 At the bottom one can download GSM907811.CEL.gz . If I download this manually and try oligo::read.celfiles("GSM907811.CEL.gz") everything works fine. (oligo is a bioConductor package) However, if I download using download.file("

[posted to R-devel, no discussion: resubmitting it as a bug, just so it gets logged appropriately] Sorry to report further difficulties with nls and profiling and constraints ... the problem this time (which I didn't check for in my last round of testing) is that the nls profiler doesn't seem to respect constraints that have been set when using the port algorithm. See test code

I have a slight conundrum. I'm attempting to write a scrip that will take a number of objects (lm, glm, and lmer) and return AIC scores and weights. I've run into 3 problems, and was wondering if anyone had any pointers. 1) is there any convenient way to extract the name of the objects? Simply, if I have a vector of objects c(my.lm, my.lmer) and I want to get a character

Dear all, I asked this question on r-package-devel but Martin Maechler pointed out this was more suited on R-devel. So here it goes: per the manual, one should create and register both the S3 and a S4 method if one needs a method for an S4 class for a function using S3 dispatching. This is cumbersome, and not very optimal. I was wondering if there's a better way to do this. Currently I

R 2.3.0 Linux, SuSE 10.0 Hi I have two problems with mle - probably I am using it the wrong way so please let me know. I want to fit different distributions to an observed count of seeds and in the next step use AIC or BIC to identify the best distribution. But when I run the script below (which is part of my original script), I get one error message for the first call of mle: Error in

With the exception of "L-BFGS-B", all of the other optim() methods return the value of the function when they are given a trivial function (i.e., one with no variable arguments) to optimize. I don't think this is a "bug" in L-BFGS-B (more like a response to an undefined condition), but it leads to a bug in stats4::mle -- a spurious error saying that a better fit has been