similar to: Listing all binary trees of an ordinal set

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

After working on some more plugins I realized that the naming for flac's ordinal types were not going to cut it. There is too much chance for conflicts with other libraries/programs with names like 'bool' and 'uint32' so I went back and prefixed all flac ordinal types with 'FLAC__'. It makes the code a little harder to read until you get used to it but should make the

Hi, I am working on a regression tree in Rpart that uses a continuous response variable that is ordered. I read a previous response by Pfr. Ripley to a inquiry regarding the ability of rpart to handle ordinal responses in 2003. At that time rpart was unable to implement an algorithm to handle ordinal responses. Has there been any effort to rectify this in recent years? Thanks! Stacey On

I have a data set w/ an ordinal response taking on one of 10 categories. I am considering using polr to fit a cumulative logits model. I previously fit the model in SAS (using proc logistic) which provides a test for the proportional odds assumption (p < 0.001 for the test). Are there simple diagnostic plots that can be used to look at the validity of this assumption and possibly help w/

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

On Mon, Jun 25, 2001 at 12:49:04PM -0700, Josh Coalson wrote: > After working on some more plugins I realized that the naming for flac's > ordinal types were not going to cut it. There is too much chance for > conflicts with other libraries/programs with names like 'bool' and 'uint32' > so I went back and prefixed all flac ordinal types with 'FLAC__'. It

Package: logcheck-database Version: 1.2.68 Severity: normal I'm using ldap to authenticate users. And thus pam_unix is sufficient, but allowed to fail. It has now started to spam the logs with lots of Jan 2 09:22:57 sisko sshd[28511]: pam_unix(sshd:auth): authentication failure; logname= uid=0 euid=0 tty=ssh ruser= rhost=host92-22-static.38-79-b.business.telecomitalia.it user=root And on

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

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

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

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 I've been asked to help evaluate a vegetation data set, specifically to examine it for community similarity. The initial problem I see is that the data is ordinal. At best this only captures a relative ranking of abundance and ordinal ranks are assigned after data collection. I've been trying to find a procedure in R that can handle ordinal based classification and so far have

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

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