On Aug 5, 2011, at 12:21 PM, Paul Smith wrote:
On Fri, Aug 5, 2011 at 4:54 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
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?
----------------------------------------------------------
residuals.lrm(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
Great. Now please answer the more fundamental question. Why do you
think this mean "discard the model"?
David Winsemius, MD
West Hartford, CT
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