similar to: Ordinal data - Regression Trees & Proportional Odds

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/

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Dear list members, I am estimating an ordinal generalized estimating equations (GEE) regression model on repeated measurements data. Is there any way (preferably in R) to test the proportional odds assumption of this model? Thanks in advance. Andreas [[alternative HTML version deleted]]

I am trying to incorporate random effects (random intercept is good enough) to a proportional odds logistic regression model for ordinal outcomes. Could lme4 do this? I'd appreciate any input. Hakan Demirtas

Hello dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/

I want to test whether the proportional odds assumption for an ordered regression is met. The UCLA website points out that there is no mathematical way to test the proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm), and use graphical inspection ("We were unable to locate a facility in R to perform any of the tests commonly used to test the parallel slopes

Hi, I want to do a global likelihood ratio test for the proportional odds logistic regression model and am unsure how to go about it. I am using the polr() function in library(MASS). 1. Is the p-value from the likelihood ratio test obtained by anova(fit1,fit2), where fit1 is the polr model with only the intercept and fit2 is the full polr model (refer to example below)? So in the case of the

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

Hey, everyone, I am using proportional odds model for ordinal responses in dose-response experiments. For some samll data, SAS can successfully provide estimators of the parameters, but the built-in function polr() in R fails. Would you like to tell me how to make some change so I can use polr() to obtain the estimators? Or anyone can give me a hint about the conditions for the existance of MLE

Dear community, I am currently attempting to perform a (L1) penalized ordinal logistic regression with proportional odds. For the moment I only found R packages allowing to perform forward or backward continuation ratio model with several penalizations. Does anyone have a clue of what R package I could use ? I am not even quite sure that penalized logistic regression with proportional odds has

Good evening, I'm trying to mount the proportional odds model (ordinal polytomous regression model) and most of my independent variables are qualitative (and are transformed into dummy). I have an inconvenience that I would like to help me resolve, below I present part of the code I'm developing for fitting the model (The variables included here are only for trial, because I have many

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 function and package I use to conduct ordinal regression?? My data is composed 2colums and 180rows. The first colum indicate level of mass and second colum is intensity. So, I want to calculate how much intensity are related mass. [[alternative HTML version deleted]]

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

R-experts: I would like to explore the partial proportional odds models of Peterson and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that I am analyzing. I have not been able to locate a R package that implements these models. Is anyone aware of existing R functions, packages, etc... that might be used to implement the partial proportional odds models? Brant Inman

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package

Hi Since the 'equal slope' assumption doesn't hold in my data I cannot use a proportional odds model ('Design' library, together with 'Hmisc'). I would like to try therefore a partial proportional odds model Please, could anybody tell me, where to find the code and how to specify such a model ..or any potential alternatives many thanks for your kind help christoph

Hi all, I'm new to R, have been reading books and trying to get started coding too. The first thing of substance I've been trying to do is to create a function to return a list of all binary trees of a list of ordinals. So, for example, an input of list(3,1,2,4) would return: list(list(1, list(2, list(3, 4))), list(1, list(list(2, 3), 4)), list(list(1, 2), list(3, 4)),

Hi - I am fitting various Cox PH models with spline predictors. After fitting the model, I would like to use termplot() to examine the functional form of the fitted model (e.g., to obtain a plot of the relative risk (or log r.r.) versus the predictors). When there is only 1 predictor in the model, termplot returns a "?". In this case, I have not been able to figure out how to create