Hi,
I"m trying to confirm goodness of fit for a logistic
regression model I'm working on, but I keep ending up
with very small p-values implying poor fit.
My outcome is a dichotomous variable - screened vs.
not screened. My predictor variables are age category
(in 5 year intervals), income tertiles, race, and
gender. My final model constructed through stepwise
backward elimination includes all the variables and
some interaction terms. However, when I try to run the
goodness of fit test (Pearson or Hosmer Lemeshow), I
keep getting extremely small p-values.
Can someone explain to me what this means? Does this
mean that the model is not valid, and the odds ratios
are incorrect?
Can you get poor fit simply as a marker of large
sample size (my sample size is 500 000)
I'm not able to understand why the model doesn't fit
when it has been constructed from the data stepwise
backward elimination, and all the variables are
univariately significant?
Thanks.
Ashwin
__________________________________________________
Do You Yahoo!?
Tired of spam? Yahoo! Mail has the best spam protection around
http://mail.yahoo.com
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
