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

From: Bin Yue <leffgh_at_163.com>
Date: Mon, 10 Dec 2007 19:42:36 -0800 (PST)

 

Dear list:

     After reading the following two links: http://luna.cas.usf.edu/~mbrannic/files/regression/Logistic.html http://www.tufts.edu/~gdallal/logistic.htm

     I've known the mathematical basis for logistic regression.However I am still not so sure about the "logit "

     For a categorical independent variable, It is easy to understand the procedures how "log odds" are calculated. As I know, First the observations are grouped according to the IV and DV, generating a contingency table.The columns are the levels of IV, and the rows are the levels of DV(0, or 1).For each column,we get the proprotions for DV=0 and DV=1 at given IV. Using the proportions the log odds can be computed.Is that right?

   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?

   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.     Regards,
    Bin Yue



Best regards,
Bin Yue

student for a Master program in South Botanical Garden , CAS
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