>From: Prof Brian Ripley <[EMAIL PROTECTED]> >Date: 2007/11/16 Fri AM 09:44:59 CST >To: [EMAIL PROTECTED] >Cc: Terry Therneau <[EMAIL PROTECTED]>, r-help@r-project.org >Subject: Re: Re: [R] alternative to logistic regression
Thanks Brian: I'll look at the MASS book example for sure but I don't think I was so clear in my last question so let me explain again. What I meant to say was : Suppose Person A and Person B both have the same raw data which is categorical response ( say 3 responses ) and 1 numeric predictor. Now, suppose person A fits a logit regression with the logit link and family = binomal so that it's an S curve in the probability space and the the predictor was numeric so the x axis was numeric. suppose person B fits a logit regression with the logit link and family = binomal so that it's an S curve in the probability space and the the predictor was a factor so the x axis was say deciles. They both then predict off of their respective models given a new value of the predictor ( Person A's predictor is in the form of a number and Person B's predictor is say a decile where the number fell in. Would their forecast of the probability given that predictor be roughly the same ? I'm sorry to be a pest but I'm not clear on that. Thanks and I'm sorry to bother you so much. >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.
