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.
Thanks in advance,
Paul
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
