similar to: glm with binomial errors in R and GLIM

After some futile searches, I decided to ask the list to see if any of the sages out there would have an answer: I have a function I wrote a few years ago in S, which calls glim numerous times. I'd like to port it to R, but glm works differently from glim, which takes as part of its input an X design matrix. I probably could write a function to convert glim to glm, but hope this

Dear Experts, In glim() of S Plus, one of the returned values is "var", the estimated variance matrix of coefficients. However, in glm() of R (there is no glim() in R), "var" is not one of the returned values. Anyone know what could I get the varience matrix of coefficients in glm() in R? As a novice in R and S+, I'd appreciate your help Sincerely, Charlie Liu

Hello, I would like to know if there is any package that allow us to fit Generalized Linear Models via Maximum Likelihood and Linear Models using Generalized Least Squarse in R as the functions glim and gls, respectively, from S-Plus. Also, anybody know if there is any package that fit Log-Linear Models using Generalized Least Squares? Any help will be very useful. Thanks, -- Frederico

Dear All, I wonder if anyone can advise me as to whether there is a consensus as to how the effect size should be calculated from GLIM models in R for any specified significant main effect or interaction. In investigating the causes of variation in infection in wild animals, we have fitted 4-way GLIM models in R with negative binomial errors. These are then simplified using the STEP procedure,

Hi all I have another GLIM question. I have been using R as well as Genstat (version 6) in order to fit GLIM models to the data (displayed below). The same models are fitted but the answers supplied by the two packages are not the same. Why? Can anyone help? A discription of the data and the type of model/s fitted can be found below. Regards Allan The

Hi all I try to make a weibull survival analysis on R. I know make this on GLIM, and now I try to make the GLIM exercice GLEX8 on R to learning and compare the test. The variables are: time censor group bodymass In GLIM I make: $calc %s=1 $ to fit weibull rather than exponential $input %pcl weibull $ $macro model group*bodymass $endmac$ $use weibull t w %s $ Then, GLIM estimate an alpha for the

I've downloaded R for windows (9.0.1) and it is great! I've converted all my lecture notes for my GLM course to run on R (they are available on my web page below). I must admit I particularly like the default contrast options, which are identical to GLIM. Also I like the gl function - very useful! I have a couple of questions/bugs: 1. predict.glm doesn't work, but predict.lm does -

Un bon mot s'il vous plait. I have coded an R routine to decompose messy data following Murray Aitkin and Brian Francis's NPMLE GLIM macros for the normal distribution only but extended to incorporate censoring and variance heterogeneity. Essentially it is a wrapper for nlm() in the M-part while the E-part re-estimates the weights in the same way as the GLIM macros. The big problem is

I came to R from GLIM and its glm. My data sets (ecological community data) are severely over-dispersed, and so I was delighted to find out that the MASS library has glm.nb which is an advancement from the GLIM macros I had used (N.E.Breslow, Applied Statistics 33, 38--44; 1984). However, I need to use offset, but that failed. I am not (yet --- hopefully) fluent enough in R to be able to

Hello, Has anyone written R functions for applying self-controlled case series methods (http://statistics.open.ac.uk/sccs/). In fact only thing needed is to modify glm function to allow absorption of effect. Eg. in Poisson model individual effect is used as factor, but it is considered as nuisance term where parameter estimates are not needed. Could anyone point how absorbing individual

Dear Friends, My problem is related to how to measure probabilities from a probit model by changing one independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <-

Hello All: I hope I can get someone interested in this problem: Agresti in "Analysis of Categorical Data," p. 289, applies a "row and column effects model" to analyze a two-dimensional cross-classification of ordinal data. He got his results in either SAS or GLIM. Is there a way to replicate his results with R? He claims the RC model fits well with G^2(RC) = 3.57 with df =

I want to do a logistic regression without an intercept term. This option is absent from glm, though present in some of the inner functions glm uses. I gather glm is the standard way to do logistic regression in R. Hoping it would be passed in, I said > r <- glm(brain.cancer~epilepsy+other.cancer, c3, > family=binomial(link="logit"), intercept=FALSE) which produced

Dear list, I am looking for a way to fix the scale parameter when fitting a generalized linear model with gamma errors and log link. Is there something like "SCALE" such as in GLIM? As always thanks a lot. Peter

I am new to R so may well have missed the point somewhere. I would like to use an exponential error in my generalized linear model. It seems natural to restrict the Gamma family to do this ( and as one might in GLIM) by specifying the scale. This does not seem possible in R . Have I missed something? Sorry to raise such a trivial point but I am keen to specify the scale G.Janacek

Hi, I am a first user of R. I was hoping I could get some help on some data I need to analyze. The experimental design is a complete randomized design with 2 factors (Source material and Depth). The experimental design was suppose to consist of 4 treatments replicated 3 time, Source 1 and applied at 10 cm and source 2 applied at 20 cm. During the construction of the treatmetns the depths vary

Hello R-helpers, I was wondering if anyone is porting S lib packages for design of experiments: conf.design, glim, graff(all from statlib)? I was able to change the format of S packages into R (including docs). In conf.design, by W. Venables, sort.list function is missing from R. I just put sort.list <- sort, but I got more error messages. Before doing more work, would like to know if this

Hello R-helpers, I was wondering if anyone is porting S lib packages for design of experiments: conf.design, glim, graff(all from statlib)? I was able to change the format of S packages into R (including docs). In conf.design, by W. Venables, sort.list function is missing from R. I just put sort.list <- sort, but I got more error messages. Before doing more work, would like to know if this

I need capabilities, for my data analysis, like the Pinheiro & Bates S-Plus/R package nlme() but with binomial family and logit link. I need multiple crossed, possibly interacting fixed effects (age cohort of twin when entered study, sex of twin, sampling method used to acquire twin pair, and twin zygosity), a couple of random effects other than the cluster variable, and the ability to

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