Why not try it out for yourself to see how much the predictions change: x <- runif(100, -1, 1) p <- exp(3*x)/(1+exp(3*x)) y <- rbinom(100, 1, p)
plot(x,p, xlim=c(-1,1), ylim=c(0,1), col='blue') points(x,y) xx <- seq(-1,1, length=250) lines(xx, exp(3*xx)/(1+exp(3*xx)), col='blue') fit1 <- glm( y ~ x, family=binomial ) fit2 <- glm( y ~ cut( x, seq(-1,1,0.2) ), family=binomial ) points( x, predict(fit1, type='response'), col='red') points( x, predict(fit2, type='response'), col='green') Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] (801) 408-8111 > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of > [EMAIL PROTECTED] > Sent: Friday, November 16, 2007 10:28 AM > To: Prof Brian Ripley; [EMAIL PROTECTED] > Cc: r-help@r-project.org; Terry Therneau > Subject: Re: [R] alternative to logistic regression > > >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. > ______________________________________________ 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.
