Re: [R] Question on approximations of full logistic regression model

From: <khosoda_at_med.kobe-u.ac.jp>
Date: Mon, 16 May 2011 13:49:54 +0900

Thank you for your reply, Prof. Harrell.

I agree with you. Dropping only one variable does not actually help a lot.

I have one more question.
During analysis of this model I found that the confidence intervals (CIs) of some coefficients provided by bootstrapping (bootcov function in rms package) was narrower than CIs provided by usual variance-covariance matrix and CIs of other coefficients wider. My data has no cluster structure. I am wondering which CIs are better. I guess bootstrapping one, but is it right?

I would appreciate your help in advance.

--
KH



(11/05/16 12:25), Frank Harrell wrote:

> 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
>
>
> KH 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
>>
>> 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".
>>
>>> full.ols.approx<- ols(plogit ~ x1+x2+ClinicalScore+procedure)
>>> full.ols.approx$stats
>> n Model L.R. d.f. R2 g Sigma
>> 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
>> "stenosis" as predictor.
>>
>>> 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
>>
>> Validatin revealed this approximation was not bad.
>> Then, I made a nomogram.
>>
>>> 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.
>>
>> 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
> --
> 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.
E-mail address Office: khosoda_at_med.kobe-u.ac.jp Home : khosoda_at_venus.dti.ne.jp ______________________________________________ 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 - 04:52:18 GMT

This quarter's messages: by month, or sorted: [ by date ] [ by thread ] [ by subject ] [ by author ]

All messages

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Tue 17 May 2011 - 05:30:07 GMT.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.

list of date sections of archive