similar to: Log likelihood from glm

List: I'm helping a colleague with some Poisson regression modeling. He uses SAS proc GENMOD and I'm using glm() in R. Note on the SAS and R output below that our estimates, standard errors, and deviances are identical but what we get for likelihoods differs considerably. I'm assuming that these must differ just by some constant but it would be nice to have some confirmation

Hello all, I found a weird result of the GLM function that seems to be a bug. The code: a=c(rep(1,8),rep(2,8)) b=c(rep(0,8),rep(3,8)) cbind(a,b) model=glm(b~a, family=poisson) summary(model) generates a dataset with two groups. One group consists entirely of zeros, the other of 3's (as happened in a dataset I’m analyzing right now). Since they are count data, one should apply a

I've computed a loglinear model on a categorical dataset. I would like to test whether an interaction can be dropped by comparing the log-likelihoods from two models(the model with the interaction vs. the model without). Since R does not immediately print the log-likelihood when I use the "glm" function, I used SAS initially. After searching for an extracting function, I found one

Could someone tell me how to access the log likelihood of a poisson model? I've done the following.... <BEGIN R STUFF> freq.mod <- glm(formula = nfix ~ gls.gls + pol.gls + pol.rel + rac.gls + rac.pol + rac.rac + rac.rel + white + gls.gls.w + pol.gls.w + pol.rel.w + rac.gls.w + rac.pol.w + rac.rac.w + rac.rac.w + rac.rel.w, family = poisson, data = Complex2.freq, offset = lnoffset)

Full_Name: Peter Perkins Version: 1.2.1 OS: LinuxPPC Submission from: (NULL) (24.4.89.36) the lines if (missing(data)) data <- environment(formula) in glm() seem to contradict the documentaton: data: an optional data frame containing the variables in the model. By default the variables are taken from the environment from which `glm' is called. actually,

Hi I am trying to carry out some maximum likelihood estimation and I'm not making much headway, and I'm hoping that someone will be able to point me in the right direction. I am modelling mortality statistics. One way to do this is to model the mortality rate (or, more accurately, log of the mortality rate, log_m) as (say) a constant plus a proportion of age, plus time, so: r_1 <-

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

Dear R helpers, running the following code of a glm model of the family poisson, gives predicted values < 0. Why? library(MASS) library(stats) library(mvtnorm) library(pscl) data(bioChemists) poisson_glm <- glm(art ~ fem + mar + kid5 + phd + ment, data = bioChemists, family = poisson) predicted.values = predict(poisson_glm) range(predicted.values) Thank you in advance for any hints.

Full_Name: saraux Version: 2.6.1 OS: Windows vista Submission from: (NULL) (193.157.180.37) I would like to compute a glm with a distribution of poisson, using a maximum of likelihood method. But it seems not to work with a distribution of poisson. The same code with another distrubution (binomial for example) works. Here is the command I typed:

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts

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

Full_Name: Charles J. Geyer Version: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)),

Greetings, everybody. Can I ask some glm questions? 1. How do you find out -2*lnL(saturated model)? In the output from glm, I find: Null deviance: which I think is -2[lnL(null) - lnL(saturated)] Residual deviance: -2[lnL(fitted) - lnL(saturated)] The Null model is the one that includes the constant only (plus offset if specified). Right? I can use the Null and Residual deviance to

Hi I'm a postgrad in ecology, and have recently started to use R. I'm planning to model various sets of animal abundance (i.e. count) data in relation to habitat data using glm's and/or glmmPQL's. However, some of my potential response variables have many zeros. From what I gather the "family = ..." option in the command line does not allow for the direct

Dear all, I am interested in correctly testing effects of continuous environmental variables and ordered factors on bacterial abundance. Bacterial abundance is derived from counts and expressed as percentage. My problem is that the abundance data contain many zero values: Bacteria <-

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

Dear all, I am using the bigglm package to fit a few GLM's to a large dataset (3 million rows, 6 columns). While trying to fit a Poisson GLM I noticed that the coefficient estimates were very different from what I obtained when estimating the model on a smaller dataset using glm(), I wrote a very basic toy example to compare the results of bigglm() against a glm() call. Consider the

OK, I really dread to ask that .... much more that I know some discussion about p-values and if they are relevant for regressions were already on the list. I know to get p-val of regression coefficients - this is not a problem. But unfortunately one editor of a journal where i would like to publish some results insists in giving p-values for the squared deviance i get out from different glm and