From: Frank Harrell <f.harrell_at_vanderbilt.edu>

Date: Sun, 15 May 2011 20:25:50 -0700 (PDT)

*>
*

*> Then, fitted an approximation to the full model using most imprtant
*

*> variable (R^2 for predictions from the reduced model against the
*

*> original Y drops below 0.95), that is, dropping "stenosis".
*

*>
*

*> 104.0000000 487.9006640 4.0000000 0.9908257 1.3341718 0.1192622
*

*>
*

*> This approximate model had R^2 against the full model of 0.99.
*

*> Therefore, I updated the original full logistic model dropping
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*> "stenosis" as predictor.
*

*>
*

*>
*

*>
*

*>
*

*> Validatin revealed this approximation was not bad.
*

*> Then, I made a nomogram.
*

*>
*

> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis)

*>
*

> Another nomogram using ols model,

*>
*

> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis)

*>
*

> These two nomograms are very similar but a little bit different.

*>
*

*> My questions are;
*

*>
*

*> 1. Am I doing right?
*

*>
*

*> 2. Which nomogram is correct
*

*>
*

*> I would appreciate your help in advance.
*

*>
*

*> --
*

*> KH
*

*>
*

*> ______________________________________________
*

*> R-help_at_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.
*

*>
*

Frank Harrell

Department of Biostatistics, Vanderbilt University

Date: Sun, 15 May 2011 20:25:50 -0700 (PDT)

I think you are doing this correctly except for one thing. The validation
and other inferential calculations should be done on the full model. Use
the approximate model to get a simpler nomogram but not to get standard
errors. With only dropping one variable you might consider just running the
nomogram on the entire model.

Frank

細田弘吉 wrote:

*>
**> Hi,
*

> I am trying to construct a logistic regression model from my data (104

*> patients and 25 events). I build a full model consisting of five
**> predictors with the use of penalization by rms package (lrm, pentrace
**> etc) because of events per variable issue. Then, I tried to approximate
**> the full model by step-down technique predicting L from all of the
**> componet variables using ordinary least squares (ols in rms package) as
**> the followings. I would like to know whether I am doing right or not.
**>
*

>> library(rms) >> plogit <- predict(full.model) >> full.ols <- ols(plogit ~ stenosis+x1+x2+ClinicalScore+procedure, sigma=1) >> fastbw(full.ols, aics=1e10)

>

> Deleted Chi-Sq d.f. P Residual d.f. P AIC R2

> stenosis 1.41 1 0.2354 1.41 1 0.2354 -0.59 0.991> x2 16.78 1 0.0000 18.19 2 0.0001 14.19 0.882> procedure 26.12 1 0.0000 44.31 3 0.0000 38.31 0.711> ClinicalScore 25.75 1 0.0000 70.06 4 0.0000 62.06 0.544> x1 83.42 1 0.0000 153.49 5 0.0000 143.49 0.000

>> full.ols.approx <- ols(plogit ~ x1+x2+ClinicalScore+procedure) >> full.ols.approx$stats

> n Model L.R. d.f. R2 g Sigma

>> full.approx.lrm <- update(full.model, ~ . -stenosis)

>> validate(full.model, bw=F, B=1000)

> index.orig training test optimism index.corrected n

> Dxy 0.6425 0.7017 0.6131 0.0887 0.5539 1000

> R2 0.3270 0.3716 0.3335 0.0382 0.2888 1000> Intercept 0.0000 0.0000 0.0821 -0.0821 0.0821 1000> Slope 1.0000 1.0000 1.0548 -0.0548 1.0548 1000> Emax 0.0000 0.0000 0.0263 0.0263 0.0263 1000

>> validate(full.approx.lrm, bw=F, B=1000)

> index.orig training test optimism index.corrected n

> Dxy 0.6446 0.6891 0.6265 0.0626 0.5820 1000> R2 0.3245 0.3592 0.3428 0.0164 0.3081 1000> Intercept 0.0000 0.0000 0.1281 -0.1281 0.1281 1000> Slope 1.0000 1.0000 1.1104 -0.1104 1.1104 1000> Emax 0.0000 0.0000 0.0444 0.0444 0.0444 1000

>> full.approx.lrm.nom <- nomogram(full.approx.lrm,

> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis)

>> plot(full.approx.lrm.nom)

> Another nomogram using ols model,

>> full.ols.approx.nom <- nomogram(full.ols.approx,

> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis)

>> plot(full.ols.approx.nom)

> These two nomograms are very similar but a little bit different.

Frank Harrell

Department of Biostatistics, Vanderbilt University

-- View this message in context: http://r.789695.n4.nabble.com/Question-on-approximations-of-full-logistic-regression-model-tp3524294p3525372.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help_at_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.Received on Mon 16 May 2011 - 03:30:44 GMT

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