Thanks.  I am trying the new test statistic, Hosmer-Le Cessie.  Seems to be
pretty straight forward using this formula:

*f <- lrm( . . ., x=T, y=T)
resid(f, 'gof')

*So far so good.  The function I was using that someone else wrote
seems to work ok until I have less predictors, and then
it doesn't want to give me the HL statistic.

Amber

On Wed, Jun 11, 2008 at 9:40 AM, Ben Bolker <bolker@zoology.ufl.edu> wrote:

amber wrote:
>
>> Hello.  I haven't been very active in this list-serv, and I apologize for
>> that.  However, I am running some logistic regression and most of my
>> predictors are continuous, so I want to use the hosmer-lemeshow stat
>> rather
>> than the regular chi-squared to see if my model fits ok.  I haven't been
>> able to find any functions for this in R except for at this site:
>> http://www.math.mcmaster.ca/peter/s4f03/s4f03_0607/rochl.html, but I
>> can't
>> seem to get it to work.  I also keep finding information on a similar but
>> more powerful test called Hosmer-Le Cessie, but I haven't tried it.  I
>> can't
>> quite remember why I even need to use the HL test instead of the
>> chi-squared
>> test except that my predictors are continuous so it will give me a better
>> approximation of the fit (from what I remember in my discrete data
>> analysis
>> course).  In any event, any advice any of you may have would be greatly
>> appreciated.
>>
>> Thanks a lot,
>> Amber
>>
>> P.S.  I'm using the glm function to run the regression.
>>
>>
>   Why not use the Hosmer-Le Cessie test, as you mention above?
> What doesn't work with the code you cite (you have to give it
> y and yhat -- use predict() to get yhat).
>
> http://finzi.psych.upenn.edu/R/Rhelp02a/archive/17403.html
>
>


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