On Aug 5, 2011, at 9:47 AM, Paul Smith wrote:
> Dear All,
>
> I have just estimated this model:
> -----------------------------------------------------------
> Logistic Regression Model
>
> lrm(formula = Y ~ X16, x = T, y = T)
>
> Model Likelihood Discrimination Rank
> Discrim.
> Ratio Test Indexes Indexes
>
> Obs 82 LR chi2 5.58 R2 0.088 C
> 0.607
> 0 46 d.f. 1 g 0.488 Dxy
> 0.215
> 1 36 Pr(> chi2) 0.0182 gr 1.629 gamma
> 0.589
> max |deriv| 9e-11 gp 0.107 tau-a
> 0.107
> Brier 0.231
>
> Coef S.E. Wald Z Pr(>|Z|)
> Intercept -1.3218 0.5627 -2.35 0.0188
> X16=1 1.3535 0.6166 2.20 0.0282
> -----------------------------------------------------------
>
> Analyzing the goodness of fit:
>
> -----------------------------------------------------------
>> resid(model.lrm,'gof')
> Sum of squared errors Expected value|H0 SD
> 1.890393e+01 1.890393e+01 6.073415e-16
> Z P
> -8.638125e+04 0.000000e+00
> -----------------------------------------------------------
>
>> From the above calculated p-value (0.000000e+00), one should discard
> this model. However, there is something that is puzzling me: If the
> 'Expected value|H0' is so coincidental with the 'Sum of squared
> errors', why should one discard the model? I am certainly missing
> something.
It's hard to tell what you are missing, since you have not described
your reasoning at all. So I guess what is at error is your expectation
that we would have drawn all of the unstated inferences that you draw
when offered the output from lrm. (I certainly did not draw the
inference that "one should discard the model".)
resid is a function designed for use with glm and lm models. Why
aren't you using residuals.lrm?
--
David Winsemius, MD
West Hartford, CT