[R] alternative to logistic regression

[Prof Brian Ripley]

On Fri, 16 Nov 2007, markleeds at verizon.net wrote:

>> From: Prof Brian Ripley <ripley at stats.ox.ac.uk>
>> Date: 2007/11/16 Fri AM 09:28:27 CST
>> To: Terry Therneau <therneau at mayo.edu>
>> Cc: markleeds at verizon.net, r-help at 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,                  ripley at stats.ox.ac.uk
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