On Fri, 16 Nov 2007, [EMAIL PROTECTED] wrote: >> From: Prof Brian Ripley <[EMAIL PROTECTED]> >> Date: 2007/11/16 Fri AM 09:28:27 CST >> To: Terry Therneau <[EMAIL PROTECTED]> >> Cc: [EMAIL PROTECTED], r-help@r-project.org >> Subject: Re: [R] alternative to logistic regression > > Thanks to both of you, Terry and Brian for your comments. I'm not sure what I > am going to do yet because I don't have enough data yet to explore/ > confirm my linear hypothesis but your comments > will help if I go that route. > > I just had one other question since I have you both thinking about GLM's at > the moment : Suppose one > is doing logistic or more generally multinomial regression with one > predictor. The predictor is quantitative > in the range of [-1,1] but, if I scale it, then > the range becomes whatever it becomes. > > But, there's also the possibility of making the predictor a factor say > by deciling it and then say letting the deciles be the factors. > > My question is whether would one expect roughly the same probability > forecasts from two models, one using the numerical predictor and one > using the factors ? I imagine that it shouldn't matter so much but I > have ZERO experience in logistic regression and I'm not confident with > my current intuition. Thanks so much for talking about my problem and I > really appreciate your insights.
It's just as in linear regression. If there really is a linear relationship the predictions will be the same. But it is quadratic, they will be very different. Discreting a numeric explanatory variable is a common way to look for non-linearity (as in the 'cpus' example studied in MASS). -- 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, UK Fax: +44 1865 272595 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
