similar to: interpreting results of regression using ordinal predictors in R

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

Hi, I have a dataset with a continuous response variable and, among other predictors, an ordinal variable. Here is what it could look like treatment <- factor(rep(c("AA", "AC", "AD","AE", "AB"), each = 10)) length <- c(75, 67, 70, 75, 65, 71, 67, 67, 76, 68, 57, 58, 60, 59, 62, 60, 60, 57, 59, 61, 58,

I would consider this is a question for a statistics forum such as stats.stackexchange.com, not R-help, which is about R programming. They do sometimes intersect, as here, but I think you need to *understand what you're doing* before you write the R code to do it. Obviously, IMO. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and

I'm trying to develop a linear model for crop productivity based on variables published as part of the SSURGO database released by the USDA. My default is to just run lm() with continuous predictor variables as numeric, and discrete predictor variables as factors, but some of the discrete variables are ordinal (e.g. drainage class, which ranges from excessively drained to excessively poorly

Dear colleagues: I've been storing up this question for a long time and apologize for the length and verbosity of it. I am having trouble in consulting with graduate students on their research projects. They are using surveys to investigate the sources of voter behavior or attitudes. They have predictors that are factors, some ordered, but I am never confident in telling them what

I have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact

Dear Peter. I guess you know that Jim Lindseys code include nordr and ordglm in library gnlm - I attach the htmls which do various linear and nonlinear ordinal regressions - exemplified with just the data mentioned, McCullagh (1980) JRSS B42, 109-142. I had it work very fine. -----Oprindelig meddelelse----- Fra: Peter Malewski <p.malewski at tu-bs.de> Til: Troels Ring <tring at

Hi there, during my work I have to use kernel smoothing methods for multivariate ordinal data. The R-package "KernSmooth" unfortunately includes only a version for continous scaled variables. Does anybody know whether there exists also a version for ordinal data? Thanks for help! --

Dear colleagues, i am carrying out an ordinal regression model. I try it on SPSS but I "flirt" with R as well. I have a few questions. 1. What is the most reliable/tested/trusted package for ordinal regression in the R world? 2. Also, I have a statistical question. What is the danger of having to many 'empty cells' in ordinal regression? How many empty cells are too many? Do

I'm finally cleaning up old things / todo's; We had about half a dozen e-mails on R-devel back in mid March...... Here is my proposal, a sometimes useful utility for constructing strings in cat() or text(), legend(), etc.: ordinal <- function(i, language =3D "english", gender =3D c("female","male"), sep=3D""= ) { ii <- i

When I run "lrm" from the Design package, I get a warning about contrasts when I include an ordinal variable: Warning message: Variable ordfac is an ordered factor. You should set options(contrasts=c("contr.treatment","contr.treatment")) or Design will not work properly. in: Design(eval(m, sys.parent())) I don't get this message if I use glm with

Hi! I'm looking for a solution to analyse data, which consists of dichotomous responses (yes/no) for 2 multinomial ordinal variables. I was trying glm() and got hierarhical models treating all variables as nominal, but I can't figure out how to tell glm() to use a model for ordinal data like this: log(Mij) = intercept + X + Y + Z + beta*(x-x')*(y-y') where beta is a

Hello,dear all: I want to install the package "ordinal",but I don't see the package listed under package sources. I try to search it by "google",then I found this: http://euridice.tue.nl/~plindsey/rlibs.html but the connect does not work. Where can I found the package "ordinal" ? Is it still available? Thanks in advance. ^^

Hello All: I know the function loglin() and loglm() from librarary(MASS) performs analysis on nominal categorical data. Are there any libraries, functions or examples available for analysis of ordinal categorical data? I have in mind procedures that can replicate results in Alan Agresti (1984) "Analysis of Ordinal Categorical Data." Thanks, ANDREW

I have a spatial data set with ordinal response variable containing four levels. I would like to know if and how spatial autocorrelation can be taken into account when ordinal logistic regression is used (e.g. the function lrm from the Design package). Thanks for your help! Christof

Dear Dr. Fieberg, you used a regression tree approach to explore ordinal data set in addition to the proportinal odds model. I find this very interesting. I would like to know, how good the results of the regression tree approach turned out in comparison to the proportional odds model. Since people very often treat ordinal data as continuous, I would like to know how successfull this strategy

This is to announce the new R-package ?ordinal? that implements cumulative link (mixed) models for ordinal (ordered categorical) data (http://www.cran.r-project.org/package=ordinal/). The main features are: - scale (multiplicative) as well as location (additive) effects - nominal effects for a subset of the predictors (denoted partial proportional odds when the link is the logistic) - structured

This is to announce the new R-package ?ordinal? that implements cumulative link (mixed) models for ordinal (ordered categorical) data (http://www.cran.r-project.org/package=ordinal/). The main features are: - scale (multiplicative) as well as location (additive) effects - nominal effects for a subset of the predictors (denoted partial proportional odds when the link is the logistic) - structured

Hi: Is there any function in R to fit ordinal regression models (linear and non-linear) described by Peter McCullagh. Regression Models for Ordinal Data, JRSS-B, 1980, 42:109-142 Thanks, Venkat ----------------------------------------------------------------------- E. S. Venkatraman, Ph.D. Phone: (212) 639-8520 Fax: (212) 717-3137 Assistant Attending Member Memorial

Patrick Lindsey has made available a library devoted to ordinal models available at: http://www.luc.ac.be/~plindsey/publications.html Best wishes Troels Ring -----Oprindelig meddelelse----- Fra: Peter Malewski <p.malewski at tu-bs.de> Til: E. S. Venkatraman <venkat at biost.mskcc.org> Cc: r-help at stat.math.ethz.ch <r-help at stat.math.ethz.ch> Dato: 24. februar 2000 22:48