similar to: Proportional odds ordinal logistic regression models with random effects

Hi, Is there any R library/package that calculates tetrachoric correlations from given marginals and Pearson correlations among ordinal variables? Inputs to polychor function in polycor package are either contingency tables or ordinal data themselves. I am looking for something that takes marginal distributions and Pearson correlation as inputs. For example, Y1=(1,2,3) with P(Y1=1)=0.3,

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

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 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 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

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

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

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

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

I'd like to estimate an ordinal logistic regression with a random effect for a grouping variable. I do not find a pre-packaged algorithm for this. I've found methods glmmML (package: glmmML) and lmer (package: lme4) both work fine with dichotomous dependent variables. I'd like a model similar to polr (package: MASS) or lrm (package: Design) that allows random effects. I was

Hi, What R libraries should I use to implement mixed effects models with continuous time and discrete-time survival data? What if I have two crossed random effects? I'd appreciate any help. Regards, Hakan Demirtas [[alternative HTML version deleted]]

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/

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 <-

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 all, I have been using the lrm function in R to run an ordinal logistic regression and I am a bit confused about the methods for checking the model assumptions. I have produced residual plots in R of the score.binary type which I think look ok. However, the partial type plots show bell shaped patterns and have crossing lines, indicating violation of parallelism. However, I noticed

I'm dealing with a classification problem using ordinal logistic regression. In the case of binary logistic regression with unequal proportions of 0's and 1's, a threshold in the interval [0,1] has to be adapted to transform back the predicted probabilities into 0 and 1. This can be done quite straightforward using e.g. the Kappa statistics as accuracy criterion. With

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

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, Is there an R library that has the same functionalities of Splus7.0+ library correlatedData? I'd appreciate any input. Hakan Demirtas [[alternative HTML version deleted]]

Is there a way to fit mixed proportional odds models in R? -- Using Opera's revolutionary e-mail client: http://www.opera.com/m2/