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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 Sloan-Kettering Cancer Center -------------------------------------------------...

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 mail1.stofanet.dk> Dato: 25. februar 2000 07:35 Emne: Re: Sv: [R] Ordinal Regression >On Thu, 24 Feb 2000, Troels Ring wrote: > >&g...

...d to calculate confidence intervals for the parameters I get the following error message Waiting for profiling to be done... Re-fitting to get Hessian Error in X[, -i, drop = FALSE] : incorrect number of dimensions Can someone explain the error-message? (The data are from McCullagh (1980), JRSS,B) tonsiles<-data.frame(carrier=factor(rep(c('yes','no'),each=3)), size=ordered(rep(c(1,2,3),2)), count=c(19,29,24,497,560,269)) library(MASS) m<-polr(size~carrier,data=tonsiles,weights=count) confint(m) Ulrich platform...

Thanks to those who responded to my inquiry about generalized linear mixed models on R and S-plus. Before I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar

Dear R-users, I have an outcome variable and I'm unsure about how to treat it. Any advice? I have spending data for each county in the state of California (N=58). Each county has been allocated money to spend on any one of the following four categories: A, B, C, and D. Each county may spend the money in any way they see fit. This also means that the county need not spend all the money that

...Re: [R] Ordinal Regression >On Thu, 24 Feb 2000, E. S. Venkatraman wrote: > >> 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 > >I dont't know this article, but... > >polr {MASS} R Documentation > Proportional Odds Logistic Regression > >described in detail, of course, in VR3 > >Peter > > >**I'd never join any club that woul...

...tion penalty methods described in Koenker, Ng and Portnoy (Biometrika, 1994). When z2 is bivariate fitting is based on the total variation penalty (triogram) methods described in Koenker and Mizera (2003), available at http://www.econ.uiuc.edu/~roger/research/goniolatry/gon.html and forthcoming in JRSS(B). There are options to constrain the qss components to be monotone and/or convex/concave for univariate components, and to be convex/concave for bivariate components. Fitting is done by new sparse implementations of the dense interior point (Frisch-Newton) algorithms already available in the pa...

Dear there, I am working on the NHANES survey data, and want to apply quantile regression on these complex survey data. Does anyone know how to do this? Thank you in advance, Yiling Cheng Yiling J. Cheng MD, PhD Epidemiologist CoCHP, Division of Diabetes Translation Centers for Disease Control and Prevention 4770 Buford Highway, N.E. Mailstop K-10 Atlanta, GA 30341 [[alternative HTML

Hi, I was recently plowing through the docs of the quantreg package by Roger Koenker and came across the total variation penalty approach to 1-dimensional spline fitting. I googled around a bit and have found some papers originated in the image processing community, but (apart from Roger's papers) no paper that would discuss its statistical aspects. I have a couple of questions in this

You may want to take a look at a paper by Julia Kelsall and Scott Zeger in JRSS(C) - 1999, pp. 331-344. This paper describes a frequency domain approach to log-linear regression modeling of poisson-distributed count data, accounting for correlation and over-dispersion. There are also some S functions available to implement the methodology. Ravi. -----Original Message----- Fr...

Hello, I'm trying to use AIC to choose between 2 models with positive, continuous response variables and different error distributions (specifically a Gamma GLM with log link and a normal linear model for log(y)). I understand that in some cases it may not be possible (or necessary) to discriminate between these two distributions. However, for the normal linear model I noticed a discrepancy

Dear all, I've now spent a couple of days trying to learn R and, in particular, the gam() function, and I now have a few questions and reflections regarding the latter. Maybe these things are implemented in some way that I'm not yet aware of or have perhaps been decided by the R community to not be what's wanted. Of course, my lack of complete theoretical understanding of what