Date: Mon, 7 Jul 2003 20:35:42 +0100
Reply-To: Sid <not@HOME.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Sid <not@HOME.COM>
Subject: General (Newbie) Question About Multinomial Logistic Regression
I'm trying to get my head round MLR and I have a couple of simple questions
Correct me if I'm wrong but my understanding is we have a set of 'variables
of influence' and a set of discrete 'Choices', which is fed into the
software 'back box' which 'optimises' using the logistic function. Say we
are choosing a brand of car, we can treat each customer individually and
just have one long input file - with one row for each choice (and
non-choice) made. We can then estimate the impact of each variable on a
choice and predict which care a customer will chose given we know the values
of the influence variables.
But what happens if we wish to predict the outcome within sets of distinct,
Say, for example, we are looking at a horse race and wish to select the
winner. When we are training the MLR with the results of previous races, do
we just treat each horse as we treat our car customer and ignore the other
horses in the race (just a linear list of all runners in all races), or is
there some way we need to adjust the input data and/or processing to take
account of the other horses in each individual race? If so, how do we handle
the fact that the group sizes (number of runners in each race) can vary?
Persisting with this analogy, say I then want to predict the odds of each
horse winning a yet to be run race (summing to 1), will the above training
procedure (whatever that may be) allow me to do this?
There seem to be so many flavours of MLR (ordered, nested etc) the more I
read the more I'm confused.
Thanks for any help