similar to: aic() component in GLM-family objects

FWIW p. 206 of the White Book gives the following for names(binomial()): family, names, link, inverse, deriv, initialize, variance, deviance, weight. So $aic wasn't there In The Beginning. I haven't done any more archaeology to try to figure out when/by whom it was first introduced ... Section 6.3.3, on extending families, doesn't give any other relevant info. A patch for

Is it generally known/has it been previously discussed here that the $aic() component in GLM-family objects (e.g. results of binomial(), poisson(), etc.) does not as implemented actually return the AIC, but rather -2*log-likelihood + 2*(model_has_scale_parameter) ? Can anyone in this forum gauge how a documentation patch would be received? This behaviour does not seem to be documented in ?family

>>>>> Ben Bolker >>>>> on Sun, 3 Jun 2018 17:33:18 -0400 writes: > Is it generally known/has it been previously discussed here that the > $aic() component in GLM-family objects (e.g. results of binomial(), > poisson(), etc.) does not as implemented actually return the AIC, but > rather -2*log-likelihood + 2*(model_has_scale_parameter)

Making data, especially data that have been the subject of published papers, widely available, can be a useful spinoff from the R project, another gift to the scientific community beyond the provision of computing and analytic tools. Nowadays, in a complete publication of a scientific result, there is every reason for the data to be part of that publication. The Gentleman and Lang 2004

Hi, I am a student in archaeology with some interest in statistics and R. Recently I've obtained the permission to distribute in the public domain a small dataset (named "spearheads" with 40 obs. of 14 variables) that was used in an introductory statistics book for archaeologists (published in 1994). I've rewritten most of the exercises of that book in R and made them

under "Details" (version 2015-11-29 r69717; not quite cutting-edge, but nothing has changed in src/library/stats/man/family.Rd in 5 months [sorry for using the Github mirror, but I prefer the interface ... <https://github.com/wch/r-source/blob/trunk/src/library/stats/man/family.Rd>]) it says: valid.mu: logical function. Returns ?TRUE? if a mean vector ?mu? is within the

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --27464147-1557463723-1147085467=:8118 Content-Type: TEXT/PLAIN; charset=iso-8859-1; format=flowed Content-Transfer-Encoding: 8BIT On Mon, 8 May 2006, x.sole at iconcologia.net wrote: > Full_Name: Xavier Sol? > Version: 2.3.0 > OS: Windows

Can anyone explain to me why the AIC values are so different when using glm.nb and glm with a negative.binomial family, from the MASS library? I'm using R 1.8.1 with Mac 0S 10.3.4. >library(MASS) > dfr <- data.frame(c=rnbinom(100,size=2,mu=rep(c(10,20,100,1000),rep(25,4))), + f=factor(rep(seq(1,4),rep(25,4)))) > AIC(nb1 <- glm.nb(c~f, data=dfr)) [1] 1047 >

Dear list I am relatively new to networking problems of this kind so apologies for the potentially simple question. I am trying to upgrade an existing network to one using Samba 3 to configure roaming XP profiles on a limited number of clients. I have re-written the smb.conf file to reflect what I think are the appropriate settings, and this passed testparm successfully, but I am unclear

Dear all, I am trying to model number of samples from a given series. The series are modelled according Gamma function. In order to estimate the # samples, I use BIC/AIC with MLE (computed from dgamma function). Here is the code I have. __BEGIN__ mlogl <- function( x_func, theta_func, samp) { # computing log_likelihood return( - sum(dgamma(samp, shape = x_func, scale=theta_func, log

Hello List, I am modeling a binomial response (nest survival) and I want to incorporate a random effect, in this case site. I had previously been using glm with a custom link function, but my understanding is that lmer does not currently allow a custom link. Therefore, I was investigating if other procedures for mixed models will allow a custom link function. here is the custom link function:

Hi all, I've just installed 1.9.18p5 (although I've just got the mail about 1.9.18p6, so I'll be putting that on now) and am having a problem with browsing... [samba@suau1] ~> smbclient -L suau1 Added interface ip=134.225.16.5 bcast=134.225.19.255 nmask=255.255.252.0 Server time is Mon May 11 15:24:39 1998 Timezone is UTC+1.0 Password: Domain=[UNSEEN] OS=[Unix]

John, You have specified a model with E(y) = 1/eta where eta = X beta is the linear predictor and E(y) must be >0, since the family is Gamma and you have a lot of covariates in the model. glm now has to try to find a best linear predictor, but under the constraint that eta>0 for every single one of the observations (the log-likelihood involves a log(eta) term). The internal

I'm not sure who is responsible for quantile(), but I assume they read this list. Ivan Frohne and I have produced a revision of the quantile.default() function which enables the computation of alternative sample quantile definitions. The code and .Rd file are attached. This enables the user to produce quantiles that are equivalent to those in various statistics package. There is a type

I'm seeing various things fail when I try to next braces inside \eqn. This source \eqn{{\bf\beta}_j}{b(j)} is the vector produces this error ---------------------------------------------- [4] ! Missing $ inserted. <inserted text> $ l.258 \eqn{{\bf\beta}_j}{\bf\beta}_ j{{b(j)} is the vector of coefficients fo... I've inserted a

Dear R-Users, I wish to create a new family object based on the Binomial family. The only difference will be with the link function. Thus instead if using the 'logit(u)' link function, i plan to use '-log(i-u)'. So far, i have tried to write the function following that of the Binomial and Negative Binomial families. The major problem i have here is with the definition of the

Hi, I am doing a small experiment to test if I can use MathJax ( official site: http://www.mathjax.org ) in R's html help pages (i.e. options(help_type='html')) and it seems working with some minor modifications. The screenshot (rendered by Firefox 4.0 beta and with STIX fonts) of the help page in html format with MathJax enabled is at the following url:

In prepping our next release, we noticed that the LICENSE.TXT files were still all copyrighted 2015. I've done a bit of archaeology and it looks like the files to update are: cfe/trunk/LICENSE.TXT clang-tools-extra/trunk/LICENSE.TXT compiler-rt/trunk/LICENSE.TXT libclc/trunk/LICENSE.TXT libcxx/trunk/LICENSE.TXT libcxxabi/trunk/LICENSE.TXT lld/trunk/LICENSE.TXT llvm/trunk/LICENSE.TXT

Browsing the web recently, I came across what I think is a new utility for generating an interactive, web-based R graph of data from a database. The generated graph can be manipulated by a user to choose different data, limits, etc. I would like to investigate using this with data from our research project but unfortunately, I didn't bookmark it and it's no longer in my browser history,

I've been trying to figure out how to build a list of terms from a nonlinear model (terms() returns a error). I need to compute and evaluate the partial derivatives (Jacobian) for each equaiton in a set of equations. For example: > eqn <- q ~ s0 + s1 * p + s2 * f + s3 * a > sv2 <- c(d0=3,d1=4.234,d2=4,s0=-2.123,s1=0.234,s2=2.123,s3=4.234) > names( sv2 ) [1] "d0"