R help - Dec 2007 - the observed "log odds" in logistic regression

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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Bin Yue <leffgh <at> 163.com> writes:
>      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.
..
>    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?  
Let's assume you are going to use glm in package stats. glm can be fed with 
data in three ways; in your case, you should use the "one-row/one 0-1
event"
format, that is the "long" style. You do not have to compute any
logit,
glm will do that for your.

The example coming closest to your's is the birthwt example in 
MASS/scripts/ch07.R  and chapter 7 in Venables/Ripley MASS. Try to generate 
a small, self-running example with a data set similar to your's, and you
have
a good chance to get a more detailed answer.

Dieter

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