similar to: R-beta: repeated measures GLM binomial data?

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

Does anyone know what algorithm R uses in glm, family=binomial (i.e. a logit model)? I assume that it's in the source somewhere, but I wasn't able to find it. I'd like to know what file it's in (in a unix distribution of R). Thanks for your help. --------------------------- Barnet Wagman wagman at enteract.com 1361 N. Hoyne, 2nd floor Chicago, IL 60622 773-645-8369

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +

Howdy, I looked at the references in the R and S-Plus literature to find a good introduction/reference to GLM. When I then went to Amazon it seemed to me that all the recommended books are out of print. Can anyone give me a recommendation which _available_ book to get? --Ragnar -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hello: I am trying to understand why glm() does not replicate the results in Dobson, "Introduction to Generalized Linear Models," pp. 17-20. I set up the following model. The variable CONDT is assumed as Poisson and the objective is to estimate the expected value. The data (chronic medical conditions among women in Australia) is as follows: CONDT <- c(0, 1, 1, 0, 2, 3, 0, 1,

I want to access a file on a ftp-server with R. But it doesn't work. I suppose the reason is the username and password. Here the non working file path ftp://woudc:woudc*@ftp.tor.ec.gc.ca/Archive-NewFormat/totalozone_1.0_1/stn035/dobson/1929/19290301.dobson.beck.002.smi.csv I tested other files on this server, same result: a crash of R. I tested it with other 'normal' sites (on other

In glm, is there a way to specify the family to be exponential, or Gamma but with alpha=1? This is required by Dobson Exercise 4.3d, page 48. But without specifying alpha=1, the asnwer is the same as in the book. If reply to the list, please cc me a copy. Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Dear list, I'm trying to mimic the analysis of Wedderburn (1974) as cited by McCullagh and Nelder (1989) on p.328-332. This is the leaf-blotch on barley example, and the data is available in the `faraway' package. Wedderburn suggested using the variance function mu^2(1-mu)^2. This variance function isn't readily available in R's `quasi' family object, but it seems to me

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson",

Dear all, I've replicated the cheese tasting example on p175 of GLM's by McCullagh and Nelder. This is a 4 treatment (rows) by 9 ordinal response (cols) table. Here's my simple code: #### cheese library(MASS) options(contrasts = c("contr.treatment", "contr.poly")) y = c(0,0, 1, 7, 8,8,19, 8,1, 6,9,12,11, 7,6, 1, 0,0, 1,1, 6, 8,23,7,

I don't have a copy of Dobson (1990) from which the glm.D93 example is taken in example("glm"), but I'm strongly suspecting that these are made-up data rather than real data; the means of the responses within each treatment are _identical_ (equal to 16 2/3), so two of the parameters are estimated as being zero (within machine tolerance). (At this moment I don't understand

Hello All, I am generating some plots where the title is generated with substitute and paste. An example: nval <- 20 plot(0,0) title(substitute(paste("n = ", n), list(n = nval))) But when compared to: plot(0,0) title("n = 20") the title in the first plot looks slightly different (it is not in bold). How can I get the two titles to look exactly the same? Also, how can I

Hi all, I am running a set of logistic regressions, where we want to use some weights, and I am not sure whether what I am doing is reasonable or not. The dependent variable is turnout in an election - i.e. survey respondents were asked whether or not they voted. The percentage of those who say they voted is much higher than the actual turnout, probably due both to non-response bias and social

I am new to generalized linear models and studying McCullagh & Nelder (1989). Especially, I have a problem resembling the \"cheese taste\" example (5.3.1. p. 109) of the book. I tried to analyse the cheese example with R but failed to do so because R allowed me to use logit link function only with binary family that supposes 0 <= y <= 1. Do I need to scale the y\'s or

Goal: use GEE or GLMM to analyze repeated measures data in R GEE problem: can?t find a way to do GEE with negative binomial family in R GLMM problem: not sure if I?m specifying random effect correctly Study question: Does the interaction of director and recipient group affect rates of a behavior? Data: Animals (n = 38) in one of 3 groups (life stages): B or C. Some individuals (~5)

Dear R users, I'm trying to automatically generate a *.tex file with the output of an OLS estimation. Some people suggested to use the latex function on the Hmisc package. I'm having a bit of trouble to properly specify this function (I'm not a very experienced R user). Below you will find an example, of what I'm doing. ## Annette Dobson (1990) "An Introduction to Generalized

Dear Everyone in list: I am writing some codes to automate the process of fitting linear models where the names of variables of models are produced and stored in character vectors. But I have problems to pass the vectors to the lm( ) because I don't know how to strip the quotation marks automatically. Here are the codes of the example of lm( ): ## Annette Dobson (1990) "An Introduction

A general problem i run into, i know there must be a simple solution. I like to create a variable by appending a 1 for example, (i need to loop later on from 1 to X, thus the reason for this). So i assign the variable vplot with this value, however it has quotes and when i use it in a barplot, it throws an error. but the tcenter$X1 does exist, its an element of a data frame. So if i type

Dear all, I have a question regarding the use of stepAIC and polynomial (quadratic to be specific) terms in a binary logistic regression model. I read in McCullagh and Nelder, (1989, p 89) and as far as I remember from my statistics cources, higher-degree polynomial effects should not be included without the main effects. If I understand this correctly, following a stepwise model selection based

Hello, silly question I suppose, but somehow I can't manage to extract the probabilities from a glm.predict() result: > str(res) Named num [1:9] 0.00814 0.01877 0.025 0.02941 0.03563 ... - attr(*, "names")= chr [1:9] "1" "2" "3" "4" ... I got from: # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2) clotting <-