[R] "Longitudinal" with binary covariates and outcome

From: Ted Harding <Ted.Harding_at_manchester.ac.uk>
Date: Tue, 11 Mar 2008 00:17:18 +0000 (GMT)


Hi Folks,
I'd be grateful for suggestions about approaching the following kind of data. I'm not sure what general class of models it is best situated in (that's just my ignorance), and in particular if anyone could point me to case studies associated with an R approach that would be most useful.

Suppose I have data of the following kind. Each "subject" is observed at say 4 time-points T2, T2, T3, T4, yielding values of binary (0/1) variables X1, X2, X3, X4. At time T4 is also observed a binary variable Y. The objective is to study the predictive power of (X1, X2, X3, X4) for the outcome "Y=1".

A useful model should take account of the possibility that more "recent" X's are likely to be better predictors than less "recent" so that, say, P(Y=1|X4=1) is likely to be larger than P(Y=1|X1=1), and also that the more X's are 1, the more likely it is that Y=1.

Any suggestions or comments and, as I say, pointers to an R treatment of similar problems would be most welcome.

With thanks,
Ted.



E-Mail: (Ted Harding) <Ted.Harding_at_manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861
Date: 11-Mar-08                                       Time: 00:17:14
------------------------------ XFMail ------------------------------

______________________________________________
R-help_at_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. Received on Tue 11 Mar 2008 - 00:20:09 GMT

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Wed 12 Mar 2008 - 15:30:21 GMT.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.

list of date sections of archive