[R] the observed "log odds" in logistic regression

[Bernardo Rangel Tura]

On Mon, 2007-12-10 at 19:42 -0800, Bin Yue wrote:
(...)
>    My problem  is this : in my data set , the IVs are continuous variables,
> do I still have to generate such a table and compute the log odds for each
> level of IV according to which the log odds are calculated?  

If IV is a continuous variable isn't possible you create a contingency
table because don't exist levels.

Similar is not possible calculate de log odds of P(IV=x) but is possible
calculate log odds of P(IV<x) or log odds of P(IV=x+delta) with delta
tend to zero. 

In this case is common create a cut-off for IV and fit log odds of
P(IV>x)

>    In R , fitted(fit) gives the fitted probability for DV to be 1.  Dose the
> observed probability exist ? If it does exist , how can I extract it ? If
> the IV is cartegorical , the DV can readily changed to be a tow-culumned
> matrix, thus log(the observed probabily/(1-the observed probability) might
> be the "log odds". I wonder what if the IV is continuous ?
>      And about the residuals. It seems that  the residual is not the actual
> DV minus the fitted probability. For in my model extreme residuals lie well
> beyond (0,1).  I wonder how   the residual is computed.
>       Would you please help me ?  Thank all very much again.

So to help you send a small part of your data and a reproductive example
to us because is more easy understand your question this way
-- 
Bernardo Rangel Tura, M.D,MPH,Ph.D
National Institute of Cardiology
Brazil