[R] error in lrm( )
David Winsemius
dwinsemius at comcast.net
Thu Dec 9 15:50:33 CET 2010
On Dec 9, 2010, at 8:06 AM, 笑啸 wrote:
> Dear Sir or Madam：
>
>
>
> I am a doctor of urology,and I am engaged in developing a nomogram
> of bladder cancer. May I ask for your help on below issue?
>
> I set up a dataset which include 317 cases. I got the Binary
> Logistic Regression model by SPSS.And then I try to reconstruct the
> model
>
> （lrm(RECU~Complication+T.Num+T.Grade+Year+TS)） by R-Project,and try
> to internal validate the model through using the function
> “validate( )”,and get the ROC through the function
> “plot.roc( )”.The outcomes like this: At last I want to get the
> Logistic model ,and get the prediction accuracy .Now the “Area under
> the curve”(0.6931) is not too bad,
As a doctor with some experience using logistic regression and looking
at the output of ROC analyses, I would ask why you think an AUC is
"not too bad"? Just flipping a fair coin would give you an expected
AUC of 0.5.
> but the “Dxy”(I think it as the prediction accuracy probability) is
> too low.
If I'm not mistaken there is a 1-1 relationship between AUC and Dxy,
so I suspect you have an incorrect scale for judging the merits of one
or the other of those figure of merit numbers. (You may want to
purchase Harrell's text "Regression Modeling Strategies" in which I
just checked my memory on this point and found that I was correct.)
--
David.
> And I don’t know which reason lead to the outcomes.Maybe I have a
> mistake understanding on the function “lrm( )”,and apply it wrong.
>
> Could you please give me some idea on how to resulve this problem?
> Thanks in advance for your kind support.
>
> warmly regards,
> Ding
> ---------------------------------------
> outcomes
> ----------------------------------------------------------------------------
> Logistic Regression Model
> lrm(formula = RECU ~ Complications + T.Num + T.Grade + Year + TS, x
> = TRUE, y = TRUE)
>
> Model Likelihood
> Discrimination Rank Discrim.
> Ratio Test
> Indexes Indexes
> Obs 317 LR chi2 37.78 R2
> 0.154 C 0.693
> 0 201 d.f. 5 g
> 0.876 Dxy 0.386
> 1 116 Pr(> chi2) <0.0001 gr
> 2.400 gamma 0.408
> max |deriv| 2e-09 gp
> 0.183 tau-a 0.180
> Brier
> 0.207
>
> Coef
> S.E. Wald Z Pr(>|Z|)
> Intercept -2.3566 0.3819
> -6.17 <0.0001
> Complications 1.6807 0.6005
> 2.80 0.0051
> T.Num 0.6481 0.2503
> 2.59 0.0096
> T.Grade 0.4276 0.1820
> 2.35 0.0188
> Year 0.5759
> 0.2849 2.02 0.0432
> TS 0.6313
> 0.2750 2.30 0.0217
>
>> validate(f,B=200)
>
> index.orig training test optimism index.corrected n
>
> Dxy 0.3861 0.4081 0.3699 0.0382 0.3479 200
> R2 0.1537 0.1716 0.1378 0.0339 0.1198 200
> Intercept 0.0000 0.0000 -0.0585 0.0585 -0.0585 200
> Slope 1.0000 1.0000 0.8835 0.1165 0.8835 200
> Emax 0.0000 0.0000 0.0375 0.0375 0.0375 200
> D 0.1160 0.1315 0.1030 0.0285 0.0875 200
> U -0.0063 -0.0063 0.0021 -0.0084 0.0021 200
> Q 0.1223 0.1378 0.1010 0.0369 0.0855 200
> B 0.2073 0.2035 0.2114 -0.0079 0.2153 200
> g 0.8755 0.9415 0.8170 0.1244 0.7511 200
> gp 0.1833 0.1920 0.1728 0.0192 0.1641 200
>
>> plot.roc(RECU,l)
>
> Call:
>
> plot.roc.default(x = RECU, predictor = l)
>
> Data: l in 201 controls (response 0) < 116 cases (response 1).
>
> Area under the curve:
> 0.6931______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
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