similar to: Negative Binomial Regression

Any thoughts on the following I'd be most grateful - I'm sure there is an easy and quick way to do this but I'm having a mental block this evening. Essentially, I'm trying to replace missing data in my dataset with reference values based on age and sex. So an example dataset is set.seed(1) X = data.frame(age=rnorm(10, 50, 10), sex=rbinom(10, 1, 0.5), A=rnorm(10), B=rnorm(10))

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

Dear R users, Is it possible to have an overlap histogram plot? For example: stuff <- data.frame(Mode = c("Land", "Air"), AgeGroup = c("Young", "Adult", "Old"), Value = sample(1:300)) histogram( ~Value | AgeGroup * Mode, data = stuff, auto.key = TRUE) Instead of having 2 * 3 panel, I want to have just 3 panel, overlapping the "Mode"

I have been experimenting with interactive packages such iplots and playwith. Consider the following sample dataset: A B C D 1 5 5 9 3 2 8 4 1 7 3 0 7 2 2 6 Let's say I make a plot of variable A. I would like to be able to click on a data point (e.g. 3) and have a pop-up window tell me the corresponding value for variable D (e.g. 4). I am also trying to produce multiple

With R 1.8.1 running in Fedora Core 1 Linux, I am having some trouble recoding and ploting some factor variables. First, can I give you some example data? Here is a column of names for age groups: agegroups <- c( "15-19", "20-24", "25-29","30-34", "35-39",

Hello, I have different results from these two softwares for a simple binomial GLM problem. >From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 >From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36 Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data

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, I have to run a general linear mixed model which looks at 2 dependent variables at the same time (var1 divided by var2). I have tryed to search for such a kind of model structure but since I just started using R my search was not successful. Especielly since I only have an old SAS GENMOD code structure from my project supervisor as an indication. My question is no, does there exist a code

Hello Everybody: I am searching for an R package for fitting Generalized Estimating Equations (GEE) with weights (i.e. Weighted Estimating Equations). From the R documentation I found "geese(geepack)" for fitting Generalized Estimating Equations. In this documentation, under the paragraph “weights” it has been written, “an optional vector of weights to be used in the fitting process.

Hello! I am trying to run a GLMM using LME4, and keep getting the warning message: "In mer_finalize(ans) : false convergence (8)" I am quite new to R, and in looking into this thus far, it appears that there are a variety of reasons why this might occur, such as needing to standardize some parameters or if all subjects in one combination of parameters all have the same outcome. I also

Hello everyone, I use Bob Muenchen's approach for reading in "in-stream" (to use SAS parlance) delimited data within a script. This works great: mystring <- "id,workshop,gender,q1,q2,q3,q4 1,1,f,1,1,5,1 2,2,f,2,1,4,1 3,1,f,2,2,4,3 4,2, ,3,1, ,3 5,1,m,4,5,2,4 6,2,m,5,4,5,5 7,1,m,5,3,4,4 8,2,m,4,5,5,5" mydata <- read.table( textConnection(mystring),

Hi, I have what I believe is a repeated-measures dataset that I'm trying to analyze using lme(). This is *not* homework, but an exercise in my trying to self-teach myself repeated-measure ANOVA for other *real* datasets that I have and that are extremely similar to the following design. I'm fairly sure the dataset described below would work with lme() -- but it'd be great if anybody

Dear all, I'm working with count data following over-dispersed poisson distribution and have to work with mixed-models on them (like proc GENMOD on SAS sys.). I'm still not to sure about what function to use. It seems to me that a glmmPQL will do the job I want, but I'll be glad if people who worked on this type of data can share what they learned. Thanks for your time. simon

Hi All, I'm using glm() in R to perform Poisson regression, I'm wondering if its possible to get equivalent Type 1 / Type 3 Analysis (similar to one in PROC GENMOD). Thanks, Kim [[alternative HTML version deleted]]

Dear Members, Greetings! I have come across a discrepancy shown by R and SAS results on same data for logistics regression.. When I processed the above csv file(1000.csv) for predicting the Action (i/c) by Age Group(1-7,Na) and Gender(M,F,Na) with GLM of R I get: R result Call: glm(formula = Action ~ Gender + AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance

Hello! I would like to add names to a table. The table presents median values, numbers of values (n) and percentage(%) for a given agegroup. Thus I would like to add a label above these three variables for each category. But if I try to use colnames I get a message telling me that the number of labels must be equal to the number of columns in the table. I hope someone could understand what I mean!

We are using glm() to models to counts of deaths due to rare causes using a log link and Poisson error distribution, with population as the offset. Approximate confidence intervals for the parameter estimates are easy to calculate using a standard normal deviate, but obviously when the counts of deaths are small (which is why we are using Poisson regression), these intervals are very approximate

I have a dataset in "long" format that looks something like this: ID TEST RESULT 1 A 17 1 A 12 1 B 15 1 C 12 2 B 8 2 B 9 Now what I would like to do is transpose it like so: ID TEST A TEST B TEST C 1 17 15 12 1 12 . . 2

Hello R-users: I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!)

Hello everyone. I am new to R, so please bear with me. I am trying to find an easy way to export descriptive statistics and other information about my data frame to a LaTeX format. I have found the describe function in PrettyR to be very helpful in producing results in the exact format I'm looking for. However, the value of the describe function is a LIST (rather than a data frame) which I