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

[Bin Yue]

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


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Best regards,
Bin Yue

*************
student for a Master program in South Botanical Garden , CAS

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