Re: [R] Ordinal Independent Variables
On Mon, 22 May 2006, Frank E Harrell Jr wrote: Rick Bilonick wrote: 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 family=binomial. It produces linear and quadratic contrasts. If it's improper to do this for an ordinal variable, why does glm not balk? Rick B. Standard regression methods don't make good use of ordinal predictors and just have to treat them as categorical. Design is a bit picky about this. If the predictor has numeric scores for the categories, you can get a test of adequacy of the scores (with k-2 d.f. for k categories) by using scored(predictor) in the formula. Or just create a factor( ) variable to hand to Design. Contrasts in S/R are used to set the coding of factors, and model.matrix() does IMO 'make good use of ordinal predictors'. I don't know what is meant by 'Standard regression methods': the charitable interpretation is that these are the overly restrictive methods used by certain statistical packages. (I first learnt of the use of polynomial codings for ordinal factors in the late 1970s, when I first learnt anything about ANOVA, so to me they are 'standard'.) So are you saying this is a design deficiency in package Design, or that the authors of S ca 1991 were wrong to allow arbitrary contrasts? -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Ordinal Independent Variables
Prof Brian Ripley wrote: On Mon, 22 May 2006, Frank E Harrell Jr wrote: Rick Bilonick wrote: 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 family=binomial. It produces linear and quadratic contrasts. If it's improper to do this for an ordinal variable, why does glm not balk? Rick B. Standard regression methods don't make good use of ordinal predictors and just have to treat them as categorical. Design is a bit picky about this. If the predictor has numeric scores for the categories, you can get a test of adequacy of the scores (with k-2 d.f. for k categories) by using scored(predictor) in the formula. Or just create a factor( ) variable to hand to Design. Contrasts in S/R are used to set the coding of factors, and model.matrix() does IMO 'make good use of ordinal predictors'. I don't know what is meant by 'Standard regression methods': the charitable interpretation is that these are the overly restrictive methods used by certain statistical packages. (I first learnt of the use of polynomial codings for ordinal factors in the late 1970s, when I first learnt anything about ANOVA, so to me they are 'standard'.) So are you saying this is a design deficiency in package Design, or that the authors of S ca 1991 were wrong to allow arbitrary contrasts? Brian, What I meant was that unlike the case of ordinal response varables where multiple intercepts in logistical models do not cost degrees of freedom because the ordering constraint is fully utilized, ordinal predictors require k-1 degrees of freedom for k levels using any standard contrast. Special methods (e.g. pool adjacent violators to impose a monotonicity constraint) would have to be used to get a lot out of the ordinal nature of the predictor. There's nothing wrong with allowing arbitrary contrasts; more progress has been made in statistics for ordinal responses than ordinal predictors. Frank -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Ordinal Independent Variables
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 family=binomial. It produces linear and quadratic contrasts. If it's improper to do this for an ordinal variable, why does glm not balk? Rick B. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Ordinal Independent Variables
Rick Bilonick wrote: 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 family=binomial. It produces linear and quadratic contrasts. If it's improper to do this for an ordinal variable, why does glm not balk? Rick B. Standard regression methods don't make good use of ordinal predictors and just have to treat them as categorical. Design is a bit picky about this. If the predictor has numeric scores for the categories, you can get a test of adequacy of the scores (with k-2 d.f. for k categories) by using scored(predictor) in the formula. Or just create a factor( ) variable to hand to Design. -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
