similar to: Ordinal GEE model

  I am trying to used "ordgee" from "geepack" for an ordinal dataset. When I write the code it returns  "Warning message:In binomial(link) : use of binomial(link=link) is deprecated" , but the program runs. Even when I run your example for "ohio" and "respdis", it returns the same error. Please guide me [[alternative HTML version deleted]]

Hi there, I just installed the package under windows, and it works very well. However, as I tried to install it on a cluster which uses linux OS, "Linux lionxc.aset.psu.edu 2.6.18-194.el5 #1 SMP Tue Mar 16 21:52:39 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux" I always failed. The message during the installation process is as follows: > install.packages("geepack") Warning

Good evening, In my research I am studying the marginal models, where the main goal is on the structure of the association. My practical example has cluster with up to 600 observations and with this database, the function geese() return me the following message: This application has requested the runtime to terminate it is on unusual way. Please contact the application’s support team

Hi, In my research I am studying the marginal models, where the main goal is on the structure of the association. My practical example has cluster with up to 600 observations and with this database, the function geese() return me the following message: This application has requested the runtime to terminate it is on unusual way. Please contact the application’s support team for more

Hello, I'm trying to fit a special kind of proportional odds model from: Whitehead et al. (2001). Meta-analysis of ordinal outcome using individual patient data. Statistics in medicine 20: 2243-2260. (model 2) The data are as follows: library(nlme) library(geepack) library(Design) library(MASS) options(contrasts=c("contr.SAS","contr.poly")) counts <-

what is exactly difference between "gee", "geese", and "ordgee" [[alternative HTML version deleted]]

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

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-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 (Sorry if you get more than one copy of this message, but I felt that it was urgent to get this important info out.) The values of freedom and openness are crucial to understanding itself, so that civilization and public welfare now depend on them, as I argue below. These values may find their best expression in the free and open source software

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 (Sorry if you get more than one copy of this message, but I felt that it was urgent to get this important info out.) The values of freedom and openness are crucial to understanding itself, so that civilization and public welfare now depend on them, as I argue below. These values may find their best expression in the free and open source software

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I am having some trouble extracting results from the function by, used to average variables in a data.frame first by one factor (depth) and then by a second factor (station). The real data.frame is quite large > dim(data.2001) [1] 32049 11 Here is a