[R] Goodness of fit of binary logistic model

[Paul Smith]

Dear All,

I have just estimated this model:

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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
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Analyzing the goodness of fit:

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> 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
>
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>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.

Thanks in advance,

Paul